Brain ecosystem mcp

brain-ecosystem-mcp

USE FOR:

• "self-learning AI brain for Claude Code"
• "persistent memory MCP server"
• "autonomous research agent with hypothesis testing"
• "trading brain with adaptive strategies"
• "MCP server with 280+ tools"
• "AI that learns from errors across sessions"
• "dream mode memory consolidation" tags: [MCP, self-learning, autonomous, Claude-Code, Trading-Brain, error-memory, knowledge-graph, CCXT, persistent-memory, Hebbian] kind: tool category: mcp-integration

---

What Is Brain Ecosystem?

3 autonomous self-learning AI brains running as MCP servers, designed for Claude Code.

• Repo: https://github.com/timmeck/brain-ecosystem
• Install: npm install -g @timmeck/brain && brain setup
• Architecture: Each brain = separate process, separate SQLite DB, separate port
• Communication: IPC named pipes between brains (Hebbian synapse network)
• Dashboard: Command Center at localhost:7790 (13 pages of metrics)

"117+ autonomous engines run in feedback loops — observing, detecting anomalies, forming hypotheses, testing and falsifying them statistically"

---

Three Brains

Brain	Port	Tools	Specialty
Brain	7777-7778	280	Error memory, code intelligence, autonomous research
Trading Brain	7779-7780	181	Adaptive trading, paper trading, signal learning, backtesting
Marketing Brain	7781-7782	177	Content strategy, cross-platform optimization
Command Center	7790	—	Unified dashboard, 13 monitoring pages

---

Installation

# Main brain
npm install -g @timmeck/brain
brain setup        # configures Claude Code MCP automatically

# Trading brain
npm install -g @timmeck/trading-brain
trading setup

# Marketing brain
npm install -g @timmeck/marketing-brain
marketing setup

For Cursor/Windsurf/Cline (HTTP/SSE):

{
  "mcpServers": {
    "brain": {
      "url": "http://localhost:7778/sse"
    }
  }
}

---

Brain (280 MCP Tools)

Core Capabilities

• Error Memory: Tracks every error across sessions, learns solutions, never repeats
• Code Intelligence: Understands codebase structure, dependencies, patterns
• Autonomous Research: Multi-step roadmaps, hypothesis generation + falsification
• Knowledge Graph: Persistent cross-session knowledge with entity relationships
• Dream Mode: Offline memory consolidation (runs when idle)
• Self-modification: Can edit own code with human approval gates

Research Engine

Goal → decompose → sub-goals → hypotheses
    → test statistically (17 falsification methods)
    → confirm/reject → update knowledge graph
    → synthesize report

Data Sources

• Brave Search + Playwright (web research)
• Firecrawl (deep page extraction)
• GitHub (code assimilation from repos)
• Vision: Anthropic + Ollama (image analysis)

---

Trading Brain (181 MCP Tools)

Capabilities

• Adaptive strategies: Learn which signals work, weight by performance
• Paper trading: Simulate with real market data before live
• Signal learning: Identify patterns that predicted past moves
• Backtesting: Automated strategy evaluation with feedback loops
• Live data: CCXT WebSocket (100+ exchanges) + CoinGecko

How It Adapts

Trade executed → outcome recorded
→ signal that preceded it gets weighted up/down
→ strategy parameters auto-adjusted
→ anomaly detection flags regime changes
→ hypothesis: "this pattern no longer works" → test → confirm → disable

---

Self-Learning Architecture

Input (error/trade/content event)
    ↓
117 autonomous engines in parallel feedback loops:
    - AnomalyDetector
    - HypothesisGenerator
    - StatisticalFalsifier
    - PatternRecognizer
    - KnowledgeGraphUpdater
    - DreamConsolidator (offline)
    ↓
Hebbian synapse: brains share relevant discoveries
    ↓
Knowledge persists in SQLite → available next session

---

Dream Mode (Memory Consolidation)

When idle (no active requests):

Brain enters "dream mode":
1. Replays recent experiences
2. Identifies patterns not obvious during active processing
3. Prunes weak connections (low-weight knowledge)
4. Strengthens high-value patterns
5. Prepares summaries for fast retrieval next session

---

Claude Code Integration

After brain setup, Claude Code gets access to all 280 Brain tools:

# In Claude Code session — brain remembers across sessions:
> "You made an error with X last week"
→ Brain recalls error memory: exact context + solution applied

> "Research async patterns in Python"  
→ Brain creates 5-step research roadmap, executes autonomously,
  synthesizes findings into knowledge graph entry

> "What patterns have worked for BTCUSDT this month?"
→ Trading Brain queries signal performance history → ranked list

---

Key Advantages Over Standard Memory Tools

Feature	Brain Ecosystem	Standard MCP Memory
Error learning	✓ (auto, cross-session)	Manual
Hypothesis testing	✓ (statistical)	✗
Dream consolidation	✓	✗
Self-modification	✓ (human-gated)	✗
Trading brain	✓ (181 tools)	✗
Inter-brain comms	✓ (Hebbian)	✗
Vision	✓ (Anthropic+Ollama)	✗

---

KNOWLEDGE INJECTION: AntV MCP Server Chart

Source: https://github.com/antvis/mcp-server-chart

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: mcp-server-chart

name: mcp-server-chart description: > AntV MCP Server Chart - MCP server generating 26+ chart types via AntV. Tools: generate_bar_chart, generate_line_chart, generate_pie_chart, generate_network_graph, generate_sankey, generate_treemap, generate_spreadsheet, etc. npx @antv/mcp-server-chart. Works with Claude, VSCode, Dify. USE FOR:

• generate charts via MCP
• bar/line/pie/scatter chart from data
• network graph visualization
• sankey treemap funnel chart
• Claude generates charts automatically tags: [MCP, charts, AntV, visualization, bar, line, pie, network-graph, sankey] kind: tool category: mcp-integration

---

What Is mcp-server-chart?

MCP server generating 26+ visualization types using AntV.

• Repo: https://github.com/antvis/mcp-server-chart
• Install: npm install -g @antv/mcp-server-chart

MCP Config (Claude Code / Desktop)

{
  "mcpServers": {
    "mcp-server-chart": {
      "command": "npx",
      "args": ["-y", "@antv/mcp-server-chart"]
    }
  }
}

Available Tools (generate_* pattern)

Standard:    area, bar, column, line, pie, scatter, dual_axes
Statistical: boxplot, histogram, violin
Flow:        funnel, sankey, treemap
Hierarchy:   mind_map, fishbone, org_chart
Network:     network_graph, venn
Geographic:  district_map, path_map, pin_map
Other:       radar, word_cloud, liquid, spreadsheet

Usage in Claude

User: "Plot this data as a bar chart: [data]"
Claude: calls generate_bar_chart({ data: [...], xField: "x", yField: "y" })
→ returns chart image/URL

---

KNOWLEDGE INJECTION: Dify

Source: https://github.com/langgenius/dify

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: dify-llm-platform

name: dify-llm-platform description: > Dify - open-source LLM app development platform. Visual canvas for AI workflows, RAG pipelines (PDF/PPT ingestion), agent builder (50+ tools: Google, DALL-E, Wolfram), LLMOps observability, BaaS APIs. Supports GPT, Claude, Llama3, Mistral, 100+ models. Self-host (Docker) or cloud (200 free GPT-4 calls). USE FOR:

• build LLM app with visual workflow
• RAG pipeline from documents
• AI agent with tools
• self-hosted ChatGPT alternative
• LLMOps monitoring tags: [Dify, LLM, RAG, agent, workflow, visual, self-hosted, open-source, GPT, Claude] kind: platform category: ai-agent-builder

---

What Is Dify?

Open-source LLM application development platform.

• Repo: https://github.com/langgenius/dify
• Stars: 100k+
• Deploy: Docker Compose (2 CPU, 4GB RAM) or dify.ai cloud

Core Capabilities

• Visual Workflow Canvas: drag-and-drop LLM pipeline builder
• RAG: ingest PDFs, PPTs, web pages → vector search → grounded answers
• Agent Builder: Function Calling or ReAct agents + 50+ built-in tools
• Model Hub: GPT-4o, Claude, Llama3, Mistral, Gemini, + OpenAI-compatible
• LLMOps: trace every call, monitor cost, replay prompts
• BaaS API: REST API for any app to call your workflow

Docker Install

git clone https://github.com/langgenius/dify
cd dify/docker
cp .env.example .env
docker compose up -d
# Access: http://localhost/install

Agent Tools (50+)

Google Search, Bing, DuckDuckGo, Wikipedia, DALL-E, Stable Diffusion, WolframAlpha, Weather API, News API, Code execution, Web scraping, + custom tools

---

KNOWLEDGE INJECTION: Open WebUI

Source: https://github.com/open-webui/open-webui

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: open-webui

name: open-webui description: > Open WebUI - self-hosted, offline-capable AI platform. Ollama + OpenAI-compatible backends. RAG with 9 vector DBs, web search (15+ providers), image gen (DALL-E/ComfyUI), voice/video chat, Python function calling, enterprise auth (LDAP/OAuth/SCIM). Docker install. Privacy-first local AI deployment. USE FOR:

• self-hosted ChatGPT alternative
• local Ollama web interface
• offline AI with RAG
• multi-model comparison
• enterprise private AI deployment tags: [Open-WebUI, Ollama, self-hosted, RAG, local-AI, privacy, ChatGPT-alternative] kind: platform category: ai-agent-builder

---

What Is Open WebUI?

Extensible self-hosted AI platform — runs fully offline.

• Repo: https://github.com/open-webui/open-webui
• Supports: Ollama (local LLMs) + any OpenAI-compatible API

Quick Install

# With Ollama bundled
docker run -d -p 3000:8080 --gpus=all   -v ollama:/root/.ollama -v open-webui:/app/backend/data   --name open-webui ghcr.io/open-webui/open-webui:ollama

# Existing Ollama
docker run -d -p 3000:8080   --add-host=host.docker.internal:host-gateway   -v open-webui:/app/backend/data   --name open-webui ghcr.io/open-webui/open-webui:main
# Access: http://localhost:3000

Key Features vs ChatGPT

Feature	Open WebUI	ChatGPT
Self-hosted	Yes	No
Offline	Yes	No
Local models	Ollama	No
RAG	9 vector DBs	Limited
Web search	15+ providers	Yes
Image gen	DALL-E/ComfyUI/A1111	DALL-E only
Python tools	Native	Sandboxed
Cost	Free	$20/mo

---

KNOWLEDGE INJECTION: Awesome MCP Servers (Reference)

Source: https://github.com/punkpeye/awesome-mcp-servers

Routed to: claude-ai-tools.md

Date: 2026-03-18

Awesome MCP Servers — Ecosystem Reference

500+ MCP servers across 40+ categories. Key ones by domain:

Development & Code

Server	What It Does
GitHub MCP	Repos, PRs, issues, code search
GitLab MCP	GitLab API integration
Filesystem MCP	Local file read/write/search
Git MCP	git log, diff, branch operations
Docker MCP	Container management
Kubernetes MCP	Cluster insights, kubectl

AI & Agents

Server	What It Does
Memory MCP	Persistent knowledge graph
Sequential Thinking	Chain-of-thought reasoning
Fetch/Browser	Web content retrieval
Playwright MCP	Browser automation
AgentShield	Security vulnerability scanning

Data & Databases

Server	What It Does
PostgreSQL MCP	Schema inspection + queries
MongoDB MCP	Document DB queries
Elasticsearch MCP	Search and analytics
Snowflake MCP	Data warehouse queries
SQLite MCP	Local database

Communication

Server	What It Does
Gmail MCP	Email read/send/search
Slack MCP	Channel messages, search
Telegram MCP	Bot messages
Discord MCP	Server interaction

Productivity

Server	What It Does
Notion MCP	Pages, databases, blocks
Jira MCP	Issues, sprints, projects
Google Calendar MCP	Events, scheduling
Airtable MCP	Base/table operations

Finance & Trading

Server	What It Does
Crypto APIs	Price feeds, portfolio
Payment MCP	Stripe, payment processing
Multi-cloud cost	Cloud cost analysis

Search & Data

Server	What It Does
Brave Search	Web search
Firecrawl	Deep web scraping
EXA MCP	AI-powered search
75+ data extractors	Specialized data sources

Full list: https://github.com/punkpeye/awesome-mcp-servers

---

KNOWLEDGE INJECTION: Everything Claude Code

Source: https://github.com/affaan-m/everything-claude-code

Routed to: claude-ai-tools.md

Date: 2026-03-18

Everything Claude Code — Production Optimization Reference

Complete system for maximizing Claude Code performance (10+ months production-tested).

Components Overview

Component	Count	Purpose
Agents	21-25	Specialized subagents (planner, architect, reviewer, security, language-specific)
Skills	102+	Domain workflows (TDD, security review, frontend/backend patterns)
Commands	52-57	Slash commands: /tdd, /plan, /code-review, /build-fix, /e2e
Rules	29-34	Universal + language-specific (TS, Python, Go, Swift, PHP, Java)
Hooks	8-20+	PreToolUse, PostToolUse, Stop, SessionStart automations
MCP	14+	Pre-configured: GitHub, Supabase, Vercel, Railway

Key Slash Commands

/tdd           - Test-driven development workflow
/plan          - Architecture planning
/code-review   - Security + quality review
/build-fix     - Build error diagnosis
/e2e           - End-to-end test generation
/multi-plan    - Multi-agent orchestration
/instinct-status - Check learned patterns
/evolve        - Extract patterns from session into skills

Token Optimization Strategies

• Use Haiku/Sonnet for simple tasks, Opus for complex reasoning
• Compress context with /compact before long sessions
• Use subagents with limited scope (avoid full context bleed)
• Skills reduce re-explanation overhead
• Auto-extract patterns → reusable skills over time

Hooks Patterns

{
  "hooks": {
    "PreToolUse": ["secret-detector", "format-checker"],
    "PostToolUse": ["session-persist", "pattern-extractor"],
    "Stop": ["summary-generator"],
    "SessionStart": ["context-loader", "instinct-injector"]
  }
}

Specialized Agents

• planner: breaks task into subtasks, creates task graph
• architect: system design, file structure decisions
• code-reviewer: security + style + logic review
• security-reviewer: OWASP, injection, auth vulnerabilities
• go/python/ts-reviewer: language-specific best practices

---

KNOWLEDGE INJECTION: Cherry Studio

Source: https://github.com/CherryHQ/cherry-studio

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: cherry-studio

name: cherry-studio description: > Cherry Studio - desktop AI client for Windows/Mac/Linux. Unifies 20+ AI providers: OpenAI, Anthropic Claude, Gemini, Ollama (local), LM Studio, Qwen, Kimi. 300+ pre-configured assistants, multi-model simultaneous chat, MCP server support, document processing (PDF/Office/images), no environment setup needed. USE FOR:

• unified desktop AI client
• multi-model comparison chat
• local + cloud AI in one app
• 300+ pre-built AI assistants
• MCP integration desktop tags: [Cherry-Studio, desktop, multi-model, Ollama, Claude, GPT, Gemini, MCP, assistants] kind: tool category: ai-agent-builder

---

What Is Cherry Studio?

Unified desktop AI client — no setup required.

• Repo: https://github.com/CherryHQ/cherry-studio
• Platforms: Windows, macOS, Linux
• Models: 20+ providers, local (Ollama/LM Studio) + cloud

Supported Providers

OpenAI (GPT-4o) · Anthropic (Claude) · Google (Gemini) · Mistral Ollama (local) · LM Studio (local) · Qwen · Kimi · Baidu · iFlytek OpenRouter · Perplexity · Poe · + more

Key Features

• 300+ assistants: pre-configured prompts for coding, writing, analysis
• Multi-model chat: send same prompt to multiple models simultaneously
• Document processing: PDF, Word, Excel, PowerPoint, images
• MCP support: connect MCP servers (with Marketplace planned)
• WebDAV sync: sync conversations across devices
• No setup: download → login/API key → use immediately

---

KNOWLEDGE INJECTION: AionUi

Source: https://github.com/iOfficeAI/AionUi

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: aionui-desktop-agent

name: aionui-desktop-agent description: > AionUi - free open-source multi-agent desktop platform (macOS/Windows/Linux). Auto-detects Claude Code, Codex, Qwen Code CLI tools. 20+ AI providers. Cron scheduling for 24/7 unattended automation. File management, Excel AI, PowerPoint generation, image recognition. Remote access via Telegram/Lark/DingTalk. USE FOR:

• multi-agent desktop automation
• unify Claude Code + Codex in one UI
• cron scheduled AI tasks
• file batch rename organize
• Excel AI processing
• remote AI control via Telegram tags: [AionUi, desktop-agent, multi-agent, Claude-Code, automation, scheduling, Telegram, remote] kind: tool category: ai-agent-builder

---

What Is AionUi?

Multi-agent desktop platform — AI agents operate autonomously on your computer.

• Repo: https://github.com/iOfficeAI/AionUi
• Platforms: macOS · Windows · Linux
• Install: download desktop app, sign in with Google or API key

Key Differentiators

• Auto-detects existing Claude Code, Codex, Qwen Code CLIs → unifies them
• Built-in agent: no CLI setup needed (full file/web/code capabilities)
• Multi-agent: run Claude Code + Codex simultaneously, independent contexts

Supported Models (20+ providers)

Gemini · Anthropic Claude · OpenAI · Qwen · Kimi · Baidu · Ollama · LM Studio

Automation Capabilities

Feature	Description
Cron scheduling	Natural language task descriptions, runs 24/7
File management	Batch rename, intelligent classification
Excel processing	AI analysis, formatting, report generation
Document generation	PowerPoint, Word, Markdown automated creation
Image operations	Text-to-image, editing, recognition

Remote Access

• WebUI (browser access)
• Telegram bot integration
• Lark/Feishu (enterprise)
• DingTalk

---

KNOWLEDGE INJECTION: Awesome Agent Skills (VoltAgent)

Source: https://github.com/VoltAgent/awesome-agent-skills

Routed to: claude-ai-tools.md

Date: 2026-03-18

Awesome Agent Skills — Curated Registry (549+ skills)

Real-world agent skills from 40+ organizations. Works with Claude Code, Codex, Gemini CLI, Cursor, GitHub Copilot.

Source: https://github.com/VoltAgent/awesome-agent-skills

Notable Skills by Category

Official / Anthropic

• anthropics/docx — Create, edit, analyze Word documents
• PDF, presentations, design, web artifacts

Infrastructure & DevOps

• Vercel, Cloudflare, Netlify, AWS, Google Cloud skills
• HashiCorp Terraform code generation
• Kubernetes, Docker, CI/CD workflows

Databases & Data

• Supabase, Neon, ClickHouse, Tinybird

Dev Frameworks

• React, Next.js, React Native, Expo, WordPress

AI/ML

• Hugging Face, Replicate, fal.ai, OpenAI integration

Security (Trail of Bits — 23 skills)

• Insecure defaults detection
• Property-based testing + smart contracts
• Semgrep rule creation for vulnerability detection

Product & SaaS

• Content strategy planning
• Pricing/packaging/monetization strategy
• CAC, LTV, payback period calculations (saas-economics-efficiency-metrics)
• Investor materials, fundraising

Web3/Crypto

• Binance trading tools
• Blockchain interaction skills

Security warning: Skills are curated, not audited. Review before install. Scanners: Snyk Skill Security Scanner, Agent Trust Hub.

---

KNOWLEDGE INJECTION: wshobson/ai-trading-crew (112 agents, 146 skills)

Source: https://github.com/wshobson/agents

Routed to: claude-ai-tools.md

Date: 2026-03-18

wshobson/ai-trading-crew — 112 Agents + 146 Skills System

Production Claude Code plugin system: 112 agents, 146 skills, 79 tools, 72 plugins.

Architecture

• 72 focused single-purpose plugins (1-6 per category)
• 23 categories, mix-and-match install
• 3-tier model: Opus (complex) / Sonnet (mid) / Haiku (simple)
• Progressive disclosure: skills load only when activated

Install

/plugin marketplace add wshobson/agents
/plugin install python-development
/plugin install security-audit

Key Plugin Categories

• Language specialists: Python, JS/TS, Go, Rust, systems
• Infrastructure: Kubernetes, cloud, CI/CD
• Security + compliance
• Data engineering + MLOps
• Full-stack + framework-specific
• Business ops + documentation
• Quantitative trading + risk management (limited)
• Payments: Stripe, PayPal, billing

---

KNOWLEDGE INJECTION: Ruflo (Enterprise Agent Orchestration)

Source: https://github.com/ruvnet/ruflo

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: ruflo-agent-orchestration

name: ruflo-agent-orchestration description: > Ruflo (formerly Claude Flow) - enterprise multi-agent orchestration for Claude Code. 60+ specialized agents in swarms (hierarchical/mesh/ring/star topologies). Self-learning SONA architecture, HNSW vector search (150x-12500x faster retrieval), EWC++ anti-forgetting, Mixture of Experts routing. MCP server. npx ruflo@latest init USE FOR:

• enterprise multi-agent orchestration
• Claude Code swarm of agents
• self-learning agent routing
• 60+ specialized agents
• MCP server for Claude Code tags: [ruflo, multi-agent, swarm, orchestration, Claude-Code, MCP, self-learning, HNSW] kind: framework category: ai-agent-builder

---

What Is Ruflo?

Enterprise AI agent orchestration platform built on Claude Code.

• Repo: https://github.com/ruvnet/ruflo
• Install: npx ruflo@latest init --wizard
• Claude Code MCP: claude mcp add ruflo -- npx ruflo@latest mcp start

Key Differentiators vs CrewAI/LangGraph

Feature	Ruflo	CrewAI	LangGraph
Self-learning	SONA (0.05ms adapt)	No	No
Anti-forgetting	EWC++	No	No
Vector retrieval	HNSW (12500x faster)	Basic	Basic
Agent topologies	4 (hier/mesh/ring/star)	Hierarchical	Graph
Swarm size	Unlimited	Limited	Limited

Installation

curl -fsSL https://cdn.jsdelivr.net/gh/ruvnet/claude-flow@main/scripts/install.sh | bash
# or
npx ruflo@latest init --wizard

Usage

npx ruflo@latest --agent coder --task "Implement user authentication"
npx ruflo@latest --list                    # list all 60+ agents
npx ruflo@latest mcp start                 # start MCP server

Swarm Topologies

Hierarchical: Queen agent → Worker agents (tree structure)
Mesh:         All agents peer-to-peer (collaborative)
Ring:         Sequential pipeline (A→B→C→A)
Star:         Central coordinator → spoke agents

---

KNOWLEDGE INJECTION: alirezarezvani/claude-skills (204 skills)

Source: https://github.com/alirezarezvani/claude-skills

Routed to: claude-ai-tools.md

Date: 2026-03-18

claude-skills — 204 Production Skills Library

Most comprehensive open-source Claude Code skills library (204 skills, 266 Python CLI tools). Compatible with 11 AI coding platforms.

Skill Domains

Domain	Skills	Notable
Engineering Core	25	Architecture, DevOps, security, AI/ML
Engineering POWERFUL	30	Advanced tier skills
Playwright Testing	9+	Test generation, migration
Product Management	13	Strategy, UX research, analytics
Marketing	43	7 specialized pods
Project Management	6	PM, scrum, Jira/Confluence
Regulatory/Quality	12	FDA, ISO, MDR, GDPR
C-Level Advisory	28	Full executive suite
Business & Growth	4	Customer success, sales
Finance	2	Financial analysis, SaaS metrics

Skill Structure

skill-name/
├── SKILL.md          # structured instructions + workflow
├── tools/            # Python CLI tools (standard library only)
│   └── tool_name.py
├── scripts/          # optional automation scripts
└── references/       # templates + domain knowledge

Install

# Claude Code plugin marketplace
/plugin install alirezarezvani/claude-skills/engineering

# Manual: copy skill folder to ~/.claude/skills/
cp -r marketing-pod ~/.claude/skills/

---

KNOWLEDGE INJECTION: ARIS (Auto Research in Sleep)

Source: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: aris-auto-research

name: aris-auto-research description: > ARIS - Autonomous ML research methodology using Claude Code + GPT adversarial reviewer. 4 workflows: idea discovery, experiment bridge, auto-review loop, paper writing. 20 composable skills. State file recovery (no daemon needed). GPU automation via SSH. Proven: borderline reject (5/10) -> submission-ready (7.5/10) in 4 overnight rounds. USE FOR:

• autonomous ML research overnight
• auto paper review and improvement loop
• GPU experiment automation SSH
• academic paper writing Claude Code
• cross-model adversarial review (Claude + GPT) tags: [research, autonomous, ML, paper-writing, GPU, Claude-Code, adversarial-review, overnight] kind: methodology category: ai-agent-builder

---

What Is ARIS?

Autonomous ML research methodology — Claude Code executes, GPT-5.4 reviews adversarially.

• Repo: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep
• Result: borderline reject → submission-ready in 4 overnight rounds (5/10 → 7.5/10)
• Philosophy: cross-model collaboration avoids local minima of self-critique

4 Workflows

Workflow 1:   Idea Discovery
              literature survey → 8-12 ideas → novelty check → GPU pilots → ranked report

Workflow 1.5: Experiment Bridge
              reads plan → implements code → validates small-scale → deploys full GPU suite

Workflow 2:   Auto Review Loop (key workflow)
              GPT-5.4 reviews paper → identifies gaps → Claude Code writes experiments
              → SSH deploys to GPU → monitors → rewrites sections → repeat (max 4 rounds)

Workflow 3:   Paper Writing
              narrative → claims-evidence matrix → figures/tables → LaTeX → PDF → 2-round auto-improve

Slash Commands

/research-pipeline "your direction"   # full pipeline
/idea-discovery "topic"               # workflow 1
/experiment-bridge                    # workflow 1.5
/auto-review-loop "paper scope"       # workflow 2 (overnight key)
/paper-writing "NARRATIVE_REPORT.md"  # workflow 3

# With overrides:
/research-pipeline "topic" -- AUTO_PROCEED: false, wandb: true

State Recovery (Persistence)

Each skill saves progress to JSON after every round:
  REVIEW_STATE.json
  AUTO_REVIEW.md

If session terminates mid-loop:
  Restart Claude Code → skill reads state → resumes from last checkpoint

GPU Automation

Configure in project CLAUDE.md:

SSH_HOST: your-gpu-server.com
SSH_USER: ubuntu
CONDA_ENV: research
SLURM: true  # if using SLURM cluster

Skills handle: ssh → rsync code → conda activate → run experiments → fetch results

---

KNOWLEDGE INJECTION: AI Maestro

Source: https://github.com/23blocks-OS/ai-maestro

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: ai-maestro-orchestration

name: ai-maestro-orchestration description: > AI Maestro - centralized dashboard for managing 35+ AI agents across machines. Peer mesh network (no central server), agent-to-agent messaging (AMP protocol), Code Graph visualization, Kanban task tracking, persistent memory, tmux auto-discovery. Gateway: Slack/Discord/Email/WhatsApp. curl install, Node.js 18+, tmux required. USE FOR:

• manage many AI agents from one dashboard
• agent-to-agent communication
• multi-machine AI coordination
• Code Graph codebase visualization
• tmux session management for agents tags: [AI-Maestro, multi-agent, dashboard, tmux, mesh-network, AMP, Code-Graph, Kanban] kind: tool category: ai-agent-builder

---

What Is AI Maestro?

Centralized dashboard for managing distributed AI agent workforces.

• Repo: https://github.com/23blocks-OS/ai-maestro
• Use case: built for devs managing 35+ agents across terminals

Install

curl -fsSL https://raw.githubusercontent.com/23blocks-OS/ai-maestro/main/scripts/remote-install.sh | sh
# Requirements: Node.js 18+, tmux

Core Features

Feature	Description
Auto-discovery	Finds existing tmux sessions automatically
Peer mesh	No central server — equal-status machines
AMP protocol	Agent-to-Agent Messaging with priority + crypto signatures
Code Graph	Interactive codebase visualization, delta indexing
Kanban	Task dependencies + status tracking
Persistent memory	Cross-session contextual continuity
War rooms	Multi-agent team assembly
Gateways	Slack, Discord, Email, WhatsApp routing

Agent Messaging Protocol (AMP)

Message types: command, query, response, broadcast
Priority: critical > high > normal > low
Signature: cryptographic for auth
Push notifications: real-time delivery

---

KNOWLEDGE INJECTION: Babysitter (Agent Workflow Control)

Source: https://github.com/a5c-ai/babysitter

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: babysitter-agent-control

name: babysitter-agent-control description: > Babysitter - deterministic agent workflow framework. Process-as-code (JS functions), mandatory stops between steps, enforced quality gates, event-sourced audit journal. 50-67% token compression. 2000+ pre-built process templates. 4 modes: interactive, autonomous (yolo), planning, continuous (forever monitoring). USE FOR:

• deterministic agent workflow (no hallucination drift)
• quality convergence loops
• human-in-the-loop agent control
• resume interrupted agent workflows
• parallel dependent task execution
• 2000+ pre-built process templates tags: [babysitter, deterministic, agent-control, quality-gates, audit-journal, workflow, Claude-Code] kind: framework category: ai-agent-builder

---

What Is Babysitter?

Deterministic agent workflow framework — agents can only execute what the process permits.

• Repo: https://github.com/a5c-ai/babysitter
• Install: via Claude Code plugin marketplace

Core Mechanisms

Process as Code  → JS functions define exactly what agents may do
Mandatory Stops  → every step halts; process logic decides next action
Enforced Gates   → quality thresholds block progression
Event Journal    → immutable audit trail, replay from any checkpoint

4 Execution Modes

/babysitter:call     # Interactive — pauses for human approval
/babysitter:yolo     # Autonomous — fully automatic
/babysitter:plan     # Planning — review before executing
/babysitter:forever  # Continuous — indefinite monitoring

Quality Convergence

// Automated refinement until threshold met
process.qualityGate({
  check: () => runTests(),
  threshold: 0.95,          // 95% pass rate required
  maxRounds: 5,             // max refinement attempts
  onFail: "refine-code"     // action if threshold not met
})

Token Compression

50-67% context reduction built-in — summarizes completed steps, retains only active context.

Run Resumption

/babysitter:observe    # live dashboard
/babysitter:resume     # continue interrupted workflow from checkpoint

Pre-Built Process Library

2,000+ templates covering:

• Code review + refactoring
• Test generation
• Documentation
• Security audits
• Data pipeline validation
• Content generation

---

KNOWLEDGE INJECTION: Skills Manager (jiweiyeah)

Source: https://github.com/jiweiyeah/Skills-Manager

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: skills-manager-desktop

name: skills-manager-desktop description: > Skills Manager - desktop app (Tauri 2.0 + React 19) for managing AI skills across multiple coding assistants. Write a skill once, sync via symlinks to Claude Code, Codex, Opencode simultaneously. Granular enable/disable per tool. Cross-platform (macOS/Windows/Linux). Monaco Editor for in-app skill editing. USE FOR:

• manage Claude Code skills across multiple AI tools
• sync skills via symlinks no duplication
• enable/disable skills per tool
• visual skill editor desktop app
• cross-tool skill management tags: [skills-manager, Claude-Code, Codex, skills-sync, symlinks, Tauri, desktop, cross-platform] kind: tool category: ai-agent-builder

---

What Is Skills Manager?

Centralized desktop app to manage AI assistant skills across Claude Code, Codex, Opencode, etc.

• Repo: https://github.com/jiweiyeah/Skills-Manager
• Stack: Tauri 2.0 (Rust) + React 19 + TypeScript + Tailwind CSS v4 + Radix UI + Monaco Editor
• Install: download from Releases (.dmg / .msi / .exe / .deb / .AppImage / .rpm)

Key Features

• Unified hub: one place to write, edit, and organize skills
• Smart sync: symlinks prevent file duplication across tools
• Granular control: enable/disable skills per AI tool independently
• In-app editor: Monaco Editor for rich code/markdown editing
• Auto-detect: finds installed AI tools and their skill directories automatically

Supported Tools

• Claude Code: ~/.claude/skills/
• Codex CLI: ~/.codex/skills/
• Opencode: ~/.opencode/skills/
• Custom tools: configurable paths

Windows Note

Requires Administrator privileges or Developer Mode enabled for symlink creation.

---

KNOWLEDGE INJECTION: Claude Code Karma

Source: https://github.com/JayantDevkar/claude-code-karma

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: claude-code-karma

name: claude-code-karma description: > Claude Code Karma - local-first analytics dashboard for Claude Code sessions. Reads ~/.claude/ JSONL files, serves via FastAPI (port 8000), displays in SvelteKit UI (port 5173). No cloud, no accounts, no telemetry. Shows token usage, costs, cache hit rates, tool/agent distribution, file operations, plugin/skill/hook inventory, live session monitoring. USE FOR:

• visualize Claude Code session analytics
• token usage and cost tracking
• cache hit rate analysis
• tool and agent usage statistics
• file operation monitoring
• plugin/skill/hook inventory dashboard tags: [claude-code-karma, analytics, dashboard, token-usage, costs, sessions, FastAPI, SvelteKit] kind: tool category: ai-agent-builder

---

What Is Claude Code Karma?

Local analytics dashboard for your Claude Code sessions.

• Repo: https://github.com/JayantDevkar/claude-code-karma
• Architecture: ~/.claude/ JSONL → FastAPI (8000) → SvelteKit (5173) → SQLite index
• No external calls — completely local

Install

git clone https://github.com/JayantDevkar/claude-code-karma.git
cd claude-code-karma

# Terminal 1 — backend
cd api && pip install -e ".[dev]" && pip install -r requirements.txt
uvicorn main:app --reload --port 8000

# Terminal 2 — frontend
cd frontend && npm install && npm run dev
# Access: http://localhost:5173

Dashboard Features

View	What It Shows
Session Browser	All sessions with search/filter, real-time status
Analytics	Token usage, costs, cache hit rates, tool distribution
Project Organization	Workspaces by git repo + activity tracking
Tool & Agent Tracking	Built-in + MCP tools + custom agents + usage stats
File & Task Management	File operations, task creation/completion
Live Monitoring	Real-time via Claude Code hooks (optional)
Plugin Ecosystem	Installed plugins, skills, commands, hooks

---

KNOWLEDGE INJECTION: Swing Skills (whynowlab)

Source: https://github.com/whynowlab/swing-skills

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: swing-trade-psychology-coach-firewall

name: swing-trade-psychology-coach-firewall description: > Swing - 6-skill AI trade-psychology-coach firewall suite. Prevents systematic AI reasoning failures: ambiguous execution, unverified claims, anchoring, sycophancy, hidden reasoning, optimism bias. Skills: swing-clarify (5W1H), swing-research (4-stage verification), swing-options (5 alternatives), swing-review (steel-man + 3-vector critique), swing-trace (assumption mapping), swing-mortem (5-category failure projection). npx skills add whynowlab/swing-skills --all. USE FOR:

• prevent AI hallucination and overconfidence
• 5W1H request decomposition before execution
• source-tiered research with cross-validation
• generate 5 probability-weighted alternatives
• steel-man critique of decisions
• assumption mapping and confidence analysis
• pre-mortem failure projection tags: [swing, trade-psychology-coach-firewall, clarify, research, options, review, trace, mortem, reasoning, Claude-Code] kind: skills-suite category: ai-agent-builder

---

What Is Swing?

6-skill trade-psychology-coach firewall that addresses systematic AI reasoning failures.

• Repo: https://github.com/whynowlab/swing-skills
• Compatible: Claude Code (primary), Cursor, GitHub Copilot, Codex CLI

Install

npx skills add whynowlab/swing-skills --all   # all 6 skills

# Individual:
npx skills add whynowlab/swing-skills/swing-clarify
npx skills add whynowlab/swing-skills/swing-research

# Manual:
cp -r swing-skills/skills/* ~/.claude/skills/

The 6 Skills

Skill	Problem Solved	Method
swing-clarify	Ambiguous requests rushed	5W1H decomposition
swing-research	Unverified claims	4-stage verification, S/A/B/C source grading
swing-options	Anchoring on obvious answer	5 probability-weighted alternatives
swing-review	Sycophancy / no critique	Steel-man then 3-vector critical analysis
swing-trace	Hidden reasoning	Assumption map, decision forks, weakest-link
swing-mortem	Optimism bias	5-category failure projection + leading indicators

Usage

/swing-clarify Build me an auth system
/swing-research Is gRPC better than REST for mobile?
/swing-review We chose Kubernetes for our 3-person startup
/swing-options Which database for real-time leaderboard?
/swing-trace Why do you recommend microservices?
/swing-mortem We are migrating to microservices in Q3

Recommended Chain

clarify -> (research decision) -> options -> research -> review
        -> (risk analysis)     -> mortem

---

KNOWLEDGE INJECTION: Spec-Flow (echoVic)

Source: https://github.com/echoVic/spec-flow

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: spec-flow

name: spec-flow description: > Spec-Flow - structured feature development workflow for AI coding agents. 5 sequential phases with approval gates: Proposal -> Requirements (EARS syntax) -> Design -> Tasks -> Implementation. Generates living markdown docs in .spec-flow/ directory. 3 modes: Step, Batch, Phase. Triggers: "spec-flow", "spec mode", "need a plan", "structured development". Install: git clone into ~/.claude/skills. USE FOR:

• structured feature development with AI
• requirements specification EARS syntax
• phase-gated AI coding workflow
• living documentation .spec-flow directory
• proposal -> requirements -> design -> tasks -> implementation tags: [spec-flow, structured-development, requirements, EARS, phases, AI-workflow, Claude-Code] kind: skill category: ai-agent-builder

---

What Is Spec-Flow?

Phase-gated structured development workflow for AI coding agents.

• Repo: https://github.com/echoVic/spec-flow
• Install: cd ~/.claude/skills && git clone https://github.com/echoVic/spec-flow.git
• Trigger: "spec-flow", "spec mode", "need a plan", or Chinese: "写个方案"

5 Phases (with human approval gates)

1. Proposal       → overview, scope, success criteria
2. Requirements   → EARS syntax specs (SHALL/WHEN/WHERE/IF clauses)
3. Design         → architecture, components, data models, interfaces
4. Tasks          → numbered implementation checklist, dependencies
5. Implementation → execute tasks one by one, verify each

Execution Modes

# Default (step-by-step, confirmation at each phase)
spec-flow: Build user authentication system

# Fast (skip confirmations)
spec-flow --fast: Add dark mode toggle

# Simple (skip design phase)
spec-flow --skip-design: Fix the login bug

Documentation Structure

.spec-flow/
  steering/     # project context, conventions
  active/       # in-progress feature docs
    feature-name/
      proposal.md
      requirements.md
      design.md
      tasks.md
  archive/      # completed features

EARS Requirements Format

WHEN user submits login form
IF credentials are valid
THE SYSTEM SHALL authenticate the user and redirect to dashboard

WHERE the user is not authenticated
THE SYSTEM SHALL redirect to login page

---

KNOWLEDGE INJECTION: HAM — Hierarchical Agent Memory

Source: https://github.com/kromahlusenii-ops/ham

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: ham-hierarchical-memory

name: ham-hierarchical-memory description: > HAM (Hierarchical Agent Memory) - reduces Claude Code token consumption 50% by replacing one massive CLAUDE.md with small scoped CLAUDE.md files at each directory level. Agent loads only 2-3 relevant files per session. Auto-detects project stack, self-maintaining (updates decision/pattern files as work progresses). Analytics dashboard at localhost:7777. Install: git clone into ~/.claude/skills/ham. USE FOR:

• reduce Claude Code token usage by 50%
• hierarchical scoped CLAUDE.md files
• context reduction hundreds vs thousands of tokens
• self-maintaining memory decision files
• ham dashboard analytics token savings
• benchmark baseline vs HAM performance tags: [HAM, hierarchical-memory, token-reduction, CLAUDE.md, context-management, Claude-Code, analytics] kind: skill category: ai-agent-builder

---

What Is HAM?

Hierarchical Agent Memory — cuts Claude Code starting context from thousands of tokens to hundreds.

• Repo: https://github.com/kromahlusenii-ops/ham
• Install: git clone https://github.com/kromahlusenii-ops/ham.git ~/.claude/skills/ham
• Token reduction: up to 50%

Core Concept

Before HAM:  One massive CLAUDE.md → loads entire project context every session
After HAM:   Small CLAUDE.md at each directory level → loads only 2-3 relevant files

Commands

# Setup
go ham              # auto-configures everything
ham update          # refresh memory files
ham status          # show current memory state
ham route           # show which files will load for current directory

# Analytics (web UI at localhost:7777)
ham dashboard       # open analytics dashboard
ham savings         # show token savings report
ham carbon          # environmental impact calculator

# Benchmarking
ham benchmark       # run benchmark vs baseline
ham baseline start  # start baseline measurement
ham baseline stop   # stop baseline, compute metrics

# Maintenance
ham audit           # check for stale/missing memory files
ham commands        # list all available commands

Memory Structure

project/
  CLAUDE.md              # global conventions, project overview
  src/
    CLAUDE.md            # frontend/backend patterns
    components/
      CLAUDE.md          # component-specific conventions
    api/
      CLAUDE.md          # API patterns, auth flows
  tests/
    CLAUDE.md            # testing patterns, fixtures

Self-Maintaining Cycle

Read:  agent reads directory CLAUDE.md before starting work
Write: agent updates CLAUDE.md after completing work
       (new patterns, decisions, gotchas discovered)

---

KNOWLEDGE INJECTION: geo-lint (IJONIS)

Source: https://github.com/IJONIS/geo-lint

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: geo-lint

name: geo-lint description: > geo-lint - first open-source linter for GEO (Generative Engine Optimization). 97 rules across 5 categories: GEO (AI citation readiness, E-E-A-T, RAG optimization), SEO (metadata/schema/keywords), content quality, technical (broken links/perf), i18n. Agent-first JSON output for auto-fix loops. Ensures content gets cited by ChatGPT, Perplexity, Google AI Overviews, Gemini. npm install -D @ijonis/geo-lint. Claude Code skill: /geo-lint audit. USE FOR:

• optimize content for AI citation (GEO)
• lint markdown for AI search visibility
• automated fix loop until zero violations
• E-E-A-T signal validation
• RAG optimization for content
• SEO + GEO combined content audit tags: [geo-lint, GEO, SEO, AI-citation, content-optimization, RAG, E-E-A-T, Claude-Code, linter] kind: tool category: ai-agent-builder

---

What Is geo-lint?

First open-source linter for Generative Engine Optimization — ensures content gets cited by AI search engines.

• Repo: https://github.com/IJONIS/geo-lint
• Install: npm install -D @ijonis/geo-lint
• Node.js 18+ required, zero peer dependencies

Install

npm install -D @ijonis/geo-lint

# Claude Code users:
curl -fsSL https://raw.githubusercontent.com/IJONIS/geo-lint/main/install.sh | bash

Commands

npx geo-lint                  # human-readable output
npx geo-lint --format=json    # machine-readable for AI agents
/geo-lint audit               # Claude Code skill: full site scan + fix
/content-creator setup        # create SEO/GEO-optimized content

97 Rules Across 5 Categories

Category	Rules	Focus
GEO	36	AI citation readiness, E-E-A-T signals, RAG optimization
SEO	34	Metadata, schema markup, keywords
Content Quality	14	Readability, word count, jargon
Technical	10	Broken links, performance
i18n	3	Translation pairs

Agent-First Fix Loop

geo-lint --format=json → violations with fix suggestions
→ AI agent modifies content
→ geo-lint re-runs
→ repeat until violations == 0

Each violation includes: file location, rule name, severity, plain-language fix instruction, machine-readable fix pattern.

Supported Formats

• Markdown / MDX (native)
• Extensible: Astro, HTML, CMS platforms

---

KNOWLEDGE INJECTION: Awesome Claude Code (hesreallyhim)

Source: https://github.com/hesreallyhim/awesome-claude-code

Routed to: claude-ai-tools.md

Date: 2026-03-18

Awesome Claude Code — 400+ Resource Reference

Curated index of 400+ Claude Code extensions, skills, tools, hooks, and workflows.

Agent Skills (Highlights)

• Trail of Bits Security Skills — 12+ skills for code auditing + vulnerability detection
• Everything Claude Code — Wide-ranging engineering domain skills
• Fullstack Dev Skills — 65 skills across full-stack frameworks + Jira
• Claude Scientific Skills — Research, engineering, finance, writing
• Superpowers — SDLC planning through debugging competencies
• TACHES Resources — Sub agents, skills, meta-skills, workflow adaptation
• AgentSys — Task-to-production, PR management, multi-agent review
• Compound Engineering Plugin — Error-to-learning discipline agents

Orchestrators

• Claude Swarm — Multi-agent session orchestration
• Claude Squad — Terminal app, multiple agents in separate workspaces
• Claude Task Master — Task management for AI-driven development
• Auto-Claude — Multi-agent kanban UI for full SDLC automation
• Claude Code Flow — Code-first orchestration for autonomous agent cycles
• TSK — Rust CLI delegating to sandboxed Docker agents
• Happy Coder — Multi-instance control from phone/desktop

Key Tools

• claudekit — CLI toolkit: auto-save, quality hooks, 20+ subagents
• SuperClaude — Configuration framework with commands and personas
• cchistory — Session command history like shell history
• cclogviewer — HTML UI for JSONL conversation files
• recall — Full-text session search with terminal interface
• Container Use — Safe multi-agent isolation environments
• Rulesync — Auto-generates configs for Claude Code, Cursor, others
• claude-code-karma — Analytics dashboard for sessions/tokens/costs (see separate entry)
• Vibe-Log — Local prompt analysis with session analytics

Hooks (Highlights)

• parry — Prompt injection scanner detecting attacks/exfiltration
• TDD Guard — Blocks TDD-violating changes in real-time
• Dippy — Auto-approve safe bash, prompt for destructive operations
• CC Notify — Desktop notifications for input needs + task completion
• cchooks — Lightweight Python SDK with clean API

Status Lines

• claude-code-statusline — 4-line statusline with themes and cost tracking
• ccstatusline — Customizable: model, branch, token usage
• claude-powerline — Vim-style powerline with real-time tracking
• CCometixLine — High-performance Rust statusline with git integration

Key Slash Commands

Command	Purpose
/commit	Conventional commit with emojis
/create-pr	PR creation workflow
/tdd	TDD enforcement
/check	Code quality + security checks
/context-prime	Comprehensive project priming
/prd-generator	Product Requirements Document generation
/optimize	Performance bottleneck identification
/mermaid	ER diagram generation from SQL

Workflow Systems (Highlights)

• RIPER Workflow — Research, Innovate, Plan, Execute, Review phases
• AB Method — Spec-driven, large problems into focused missions
• Ralph Wiggum Techniques — Autonomous loop with exit detection + safety
• Claude Code PM — Project management with specialized agents + commands
• Simone — Broader PM with documents, guidelines, processes
• Spec-Flow — Phase-gated structured development (see separate entry)

Usage Monitors

• Claude Code Usage Monitor — Real-time terminal with burn rate predictions
• CC Usage — Dashboard for cost + token analysis
• ccflare — Web UI usage dashboard with comprehensive metrics

Full index: https://github.com/hesreallyhim/awesome-claude-code

---

KNOWLEDGE INJECTION: Refly (refly-ai)

Source: https://github.com/refly-ai/refly

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: refly-agent-skill-builder

name: refly-agent-skill-builder description: > Refly - open-source platform transforming enterprise workflows into versioned agent skills. Copilot-led DSL compiler: describe workflow in natural language, compiles in <3 minutes. Intervenable runtime (pause/audit/redirect mid-execution). Exports to Claude Code, Cursor, MCP. 3000+ native integrations: Stripe, Slack, Salesforce, GitHub, MCP servers. Central skill registry, version-controlled, team-shareable. USE FOR:

• codify business workflows as agent skills
• natural language to agent skill compiler
• intervenable runtime pause audit redirect
• deploy skills as API webhook or Claude Code tool
• 3000+ tool integrations for agent skills tags: [Refly, agent-skills, enterprise, workflow, DSL, MCP, Claude-Code, versioned, registry] kind: platform category: ai-agent-builder

---

What Is Refly?

Open-source platform for building versioned enterprise agent skills.

• Repo: https://github.com/refly-ai/refly
• Install: npm install -g @powerformer/refly-cli

Architecture

Input Layer:     3000+ tools, MCP servers, private connectors
Processing Layer: Vibe-driven DSL compiler + stateful intervenable runtime
Output Layer:    Claude Code, Cursor, APIs, webhooks, agent frameworks

Usage

npm install -g @powerformer/refly-cli

# Install a skill from registry
refly skill install <skill-id>

# Publish your skill
refly skill publish <skill-id>

# Execute workflow via API
curl -X POST https://instance/api/v1/workflows/{WORKFLOW_ID}/execute \
  -H "Authorization: Bearer API_KEY"

Key Differentiators

• Under 3 minutes from natural language description to deployed skill
• Intervenable: pause, audit, redirect agent logic during execution (compliance-friendly)
• Universal export: skills become APIs, webhooks, or native Claude Code/Cursor tools
• Central registry: version-controlled skills shareable across teams

---

KNOWLEDGE INJECTION: prompts.chat (f/prompts.chat)

Source: https://github.com/f/prompts.chat

Routed to: claude-ai-tools.md

Date: 2026-03-18

prompts.chat — World Largest Open-Source Prompt Library

143,000+ GitHub stars. Works with ChatGPT, Claude, Gemini, Llama, Mistral. Originally launched as Awesome ChatGPT Prompts (Dec 2022).

Access

# CLI
npx prompts.chat

# Claude Code plugin
/plugin install prompts.chat

# MCP server
# add to MCP config

# Browse online: https://prompts.chat/prompts
# CSV/Markdown/Hugging Face dataset available

Key Facts

• 143,000+ GitHub stars
• 40+ academic citations
• Most liked dataset on Hugging Face
• CC0 1.0 license (public domain, unrestricted use)
• Featured by Forbes, Harvard, Columbia
• Self-hosting option available

Categories

• Act As prompts (persona-based): Linux terminal, SQL terminal, JavaScript console, Excel sheet, etc.
• Writing and communication
• Analysis and research
• Code generation and review
• Educational explanations

Prompt Engineering Guide

Free interactive guide with 25+ chapters on techniques:

• Zero-shot, few-shot, chain-of-thought
• Role prompting, output formatting
• Task decomposition, context injection

Full prompt browser: https://prompts.chat/prompts

---

KNOWLEDGE INJECTION: LobeHub

Source: https://github.com/lobehub/lobehub

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: lobehub-platform

name: lobehub-platform description: > LobeHub - open-source AI platform for human-agent co-evolution. Web app + desktop + PWA. Multi-model (OpenAI, Ollama, 100+ providers), 10,000+ MCP plugins marketplace, agent builder, agent groups, personal memory, real-time internet search, chain-of-thought visualization, voice TTS/STT, image generation, knowledge base. Self-host via Docker or Vercel one-click. USE FOR:

• self-hosted multi-model AI platform
• agent builder with customizable skills
• MCP marketplace 10000+ plugins
• team agent groups collaboration
• personal memory adaptive learning
• chain-of-thought visualization tags: [LobeHub, LobeChat, multi-model, self-hosted, agents, MCP, Ollama, GPT, Claude, voice] kind: platform category: ai-agent-builder

---

What Is LobeHub?

Open-source AI platform — web app, desktop, PWA with 10,000+ MCP plugins.

• Repo: https://github.com/lobehub/lobehub
• Main product: LobeChat — https://github.com/lobehub/lobe-chat

Quick Deploy

# Docker
docker run -d -p 3210:3210 lobehub/lobe-chat

# Vercel one-click (or Zeabur, Sealos, Alibaba Cloud)
# Set OPENAI_API_KEY or other provider credentials

Key Features

Feature	Details
Multi-model	OpenAI, Ollama (local), Anthropic, Gemini, 100+ providers
MCP Marketplace	10,000+ compatible plugins
Agent Builder	Custom agents with tools, memory, persona
Agent Groups	Team-based agent collaboration
Personal Memory	Adaptive learning across sessions
Internet Search	Real-time web integration
Chain of Thought	Visualize AI reasoning steps
Voice	TTS/STT conversational interface
Image Gen	Generation + visual recognition
Knowledge Base	File uploads, RAG

---

KNOWLEDGE INJECTION: claude-mem (thedotmack)

Source: https://github.com/thedotmack/claude-mem

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: claude-mem

name: claude-mem description: > claude-mem - persistent memory compression system for Claude Code. Auto-captures tool usage, generates semantic summaries, persists across sessions. Progressive disclosure (layered retrieval with token costs). mem-search skill for natural language history queries. 4 MCP tools: search, timeline, get_observations. Web viewer at localhost:37777. Private tag for sensitive content. Install via /plugin marketplace add thedotmack/claude-mem. USE FOR:

• persistent Claude Code memory across sessions
• semantic session history search
• context compression and retrieval
• web UI for memory visualization
• MCP tools for memory queries tags: [claude-mem, persistent-memory, Claude-Code, session-continuity, compression, MCP, search] kind: tool category: ai-agent-builder

---

What Is claude-mem?

Persistent memory compression system — context survives Claude Code session restarts.

• Repo: https://github.com/thedotmack/claude-mem
• Web UI: http://localhost:37777

Install

# Via Claude Code Plugin Marketplace (recommended)
/plugin marketplace add thedotmack/claude-mem
/plugin install claude-mem
# Restart Claude Code

# Via OpenClaw Gateway
curl -fsSL https://install.cmem.ai/openclaw.sh | bash

Features

• Automatic: captures tool usage, generates semantic summaries with no manual action
• Progressive Disclosure: layered retrieval showing token costs per memory level
• mem-search skill: natural language queries into project history
• Privacy: wrap content in <private> tags to exclude from memory
• Citations: reference past observations by ID

MCP Tools

Tool	Purpose
search	Query memory index by natural language
timeline	Chronological context view
get_observations	Fetch full details by observation ID

Web Viewer (localhost:37777)

Real-time memory stream visualization + settings management.

---

KNOWLEDGE INJECTION: Continue (continuedev)

Source: https://github.com/continuedev/continue

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: continue-ai-code-review

name: continue-ai-code-review description: > Continue - open-source AI-powered code review platform running agents on every PR as GitHub status checks. Review rules stored as markdown in .continue/checks/ directories (source-controlled). Returns green pass or red with suggested diffs. VS Code extension available. CLI via npm @continuedev/cli (cn command). macOS/Linux/Windows install. Custom checks for security, best practices, team conventions. USE FOR:

• AI code review on every pull request
• source-controlled review rules markdown
• GitHub status checks from AI agents
• custom security and best practice checks
• automated PR quality enforcement tags: [Continue, AI-code-review, PR, GitHub-status-checks, CI, VS-Code, open-source] kind: tool category: ai-agent-builder

---

What Is Continue?

Open-source AI code review platform — agents run on every PR as GitHub status checks.

• Repo: https://github.com/continuedev/continue
• CLI command: cn

Install

# macOS/Linux
curl -fsSL https://install.continue.dev | bash

# Windows (PowerShell)
iwr https://install.continue.dev/windows -useb | iex

# Node.js alternative (Node 20+)
npm install -g @continuedev/cli

How It Works

1. Store review rules as markdown in .continue/checks/
2. Continue runs agents on every PR
3. Returns GitHub status check: green (pass) or red (with suggested diffs)
4. Teams enforce rules as required status checks

Review Rule Structure

.continue/
  checks/
    security.md       # security vulnerability rules
    best-practices.md # team coding standards
    custom-check.md   # any custom requirement

VS Code Extension

Available through VS Code marketplace — provides IDE-level AI assistance integrated with the same agent framework.

---

KNOWLEDGE INJECTION: memU (NevaMind-AI)

Source: https://github.com/NevaMind-AI/memU

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: memu-proactive-memory

name: memu-proactive-memory description: > memU - 24/7 always-on proactive memory framework for AI agents. Continuously captures user intent without explicit commands. Hierarchical 3-layer memory (Resource/Item/Category). Dual pathways: memorize() continuous learning + retrieve() RAG fast or LLM deep reasoning. 92.09% accuracy on benchmarks. Reduces LLM token costs via cached insights. Storage: in-memory or PostgreSQL (pgvector). pip install -e . Python 3.13+. USE FOR:

• 24/7 always-on agent memory
• proactive user intent capture
• hierarchical memory for AI agents
• reduce LLM token costs via memory cache
• RAG-based fast context retrieval
• persistent agent memory PostgreSQL pgvector tags: [memU, proactive-memory, AI-ai-agents, hierarchical-memory, RAG, pgvector, token-reduction, 24-7] kind: framework category: ai-agent-builder

---

What Is memU?

24/7 proactive memory framework — AI agents learn continuously without explicit prompts.

• Repo: https://github.com/NevaMind-AI/memU
• Cloud: memu.so
• Python 3.13+ | OpenAI API key required

Install

pip install -e .
export OPENAI_API_KEY=your_api_key

Architecture

3-Layer Hierarchical Memory:
  Resource   → mountable data sources (docs, sessions, external feeds)
  Item       → individual memory facts with cross-references
  Category   → auto-organized topic clusters

Dual Retrieval Pathways:
  memorize() → continuous learning pipeline (immediate memory update)
  retrieve() → RAG fast (sub-second embedding) OR LLM deep (complex anticipation)

Storage Options

# In-memory (default)
python tests/test_inmemory.py

# PostgreSQL with pgvector (persistent)
docker run -d --name memu-postgres \
  -e POSTGRES_PASSWORD=postgres \
  -p 5432:5432 pgvector/pgvector:pg16
python tests/test_postgres.py

Examples

python examples/example_1_conversation_memory.py  # session persistence
python examples/example_2_skill_extraction.py     # learn user skills
python examples/example_3_multimodal_memory.py    # images + text

Performance

• 92.09% average accuracy on benchmark reasoning tasks
• Sub-second RAG retrieval
• Reduces redundant LLM calls via cached intent insights

---

KNOWLEDGE INJECTION: LangChain

Source: https://github.com/langchain-ai/langchain

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: langchain-framework

name: langchain-framework description: > LangChain - leading framework for building LLM-powered applications and agents. Core: chains (sequential LLM pipelines), agents (tool-using reasoning loops), memory (conversation + long-term), RAG (retrieval-augmented generation), 100+ integrations (OpenAI, Anthropic, HuggingFace, Pinecone, Chroma, etc.). Model-agnostic: swap providers without code changes. Ecosystem: LangGraph (orchestration), LangSmith (debug/deploy). pip install langchain. USE FOR:

• build LLM application chains
• tool-using agent with ReAct or function calling
• RAG pipeline retrieval augmented generation
• conversation memory management
• swap LLM providers without changing code
• integrate with vector stores Pinecone Chroma FAISS tags: [LangChain, LLM, agents, chains, RAG, memory, tools, OpenAI, Anthropic, LangGraph, LangSmith] kind: framework category: ai-agent-builder

---

What Is LangChain?

Framework for building LLM-powered applications — agents, chains, RAG, memory, tools.

• Repo: https://github.com/langchain-ai/langchain
• Stars: 100k+
• Ecosystem: LangChain + LangGraph + LangSmith

Install

pip install langchain
pip install langchain-openai        # OpenAI provider
pip install langchain-anthropic     # Anthropic/Claude
pip install langchain-community     # community integrations
# or
uv add langchain

Quick Start

from langchain.chat_models import init_chat_model

model = init_chat_model("openai:gpt-4o")
result = model.invoke("Explain quantum computing in one sentence")

# Or Claude:
model = init_chat_model("anthropic:claude-sonnet-4-6")

Core Components

Chains — Sequential LLM pipelines:

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_template("Translate to French: {text}")
chain = prompt | ChatOpenAI(model="gpt-4o")
result = chain.invoke({"text": "Hello world"})

Agents — Tool-using reasoning loops:

from langchain.agents import create_react_agent, AgentExecutor
from langchain_community.tools import DuckDuckGoSearchRun

tools = [DuckDuckGoSearchRun()]
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)
executor.invoke({"input": "What happened in AI this week?"})

Memory — Conversation persistence:

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain

memory = ConversationBufferMemory()
chain = ConversationChain(llm=llm, memory=memory)

RAG Pipeline:

from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.chains import RetrievalQA

vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
qa_chain.invoke({"query": "What does the document say about X?"})

Ecosystem

Tool	Purpose
LangGraph	Stateful multi-agent orchestration (cycles + branching)
LangSmith	LLM app monitoring, debugging, evaluation, deployment
LangServe	Deploy chains as REST APIs

100+ Integrations

Models: OpenAI, Anthropic, HuggingFace, Cohere, Google, Ollama, Mistral Vector Stores: Pinecone, Chroma, FAISS, Weaviate, Qdrant, pgvector Tools: DuckDuckGo, SerpAPI, Wikipedia, Python REPL, SQL, Playwright Document Loaders: PDF, Word, HTML, CSV, YouTube, Notion, GitHub

---

KNOWLEDGE INJECTION: Vercel AI SDK

Source: https://github.com/vercel/ai

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: vercel-ai-sdk

name: vercel-ai-sdk description: > Vercel AI SDK - TypeScript toolkit for building AI apps with React/Next.js/Vue/Svelte/Node.js. Unified API across 20+ providers (OpenAI, Anthropic, Google, Groq, Mistral, DeepSeek, etc.). Core: generateText, streamText, generateObject, streamObject, tool calling, embeddings. UI hooks: useChat, useCompletion. npm install ai. Two libraries: AI SDK Core + AI SDK UI. USE FOR:

• build AI app with Next.js React
• unified LLM API swap providers easily
• streaming text and structured objects
• tool calling function integration
• useChat hook chat UI React
• generateObject typed JSON from LLM tags: [Vercel-AI-SDK, TypeScript, Next.js, React, streaming, generateText, useChat, tool-calling, OpenAI, Anthropic] kind: framework category: ai-agent-builder

---

What Is the Vercel AI SDK?

TypeScript toolkit for building AI apps — unified API across 20+ LLM providers.

• Repo: https://github.com/vercel/ai
• Docs: https://ai-sdk.dev/docs
• Works with: React, Next.js, Vue, Svelte, Node.js

Install

npm install ai
npm install @ai-sdk/openai      # OpenAI provider
npm install @ai-sdk/anthropic   # Anthropic/Claude
npm install @ai-sdk/google      # Google Gemini

AI SDK Core

Text Generation:

import { generateText, streamText } from "ai";
import { anthropic } from "@ai-sdk/anthropic";

// Single response
const { text } = await generateText({
  model: anthropic("claude-sonnet-4-6"),
  prompt: "Explain quantum computing",
});

// Streaming
const result = streamText({
  model: anthropic("claude-sonnet-4-6"),
  prompt: "Write a short story",
});
for await (const chunk of result.textStream) {
  process.stdout.write(chunk);
}

Structured Output:

import { generateObject } from "ai";
import { z } from "zod";

const { object } = await generateObject({
  model: openai("gpt-4o"),
  schema: z.object({
    name: z.string(),
    age: z.number(),
    skills: z.array(z.string()),
  }),
  prompt: "Generate a fictional developer profile",
});

Tool Calling:

import { tool } from "ai";

const result = await generateText({
  model: openai("gpt-4o"),
  tools: {
    getWeather: tool({
      description: "Get weather for a city",
      parameters: z.object({ city: z.string() }),
      execute: async ({ city }) => fetchWeather(city),
    }),
  },
  prompt: "What is the weather in Paris?",
});

AI SDK UI (React Hooks)

useChat:

import { useChat } from "ai/react";

export default function ChatPage() {
  const { messages, input, handleInputChange, handleSubmit } = useChat({
    api: "/api/chat",
  });
  return (
    <div>
      {messages.map(m => <div key={m.id}>{m.role}: {m.content}</div>)}
      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
        <button type="submit">Send</button>
      </form>
    </div>
  );
}

Supported Providers (20+)

OpenAI · Anthropic · Google · Azure · Amazon Bedrock · Groq · Mistral Cohere · DeepSeek · xAI Grok · Together.ai · Fireworks · Perplexity · + more

Core Functions Reference

Function	Purpose
generateText	Single text response
streamText	Streaming text + tool calls
generateObject	Typed JSON (Zod schema)
streamObject	Streaming structured data
embed	Generate embeddings
embedMany	Batch embeddings
useChat	React hook for chat UI
useCompletion	React hook for text completion
useObject	React hook for streaming objects

---

KNOWLEDGE INJECTION: TensorZero

Source: https://github.com/tensorzero/tensorzero

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: tensorzero-llm-gateway

name: tensorzero-llm-gateway description: > TensorZero - open-source LLM gateway + optimization platform. Sub-millisecond routing to 18+ providers (Anthropic, OpenAI, Google, AWS, etc.). Observability (PostgreSQL storage + OpenTelemetry), optimization (fine-tuning, GEPA prompt engineering, dynamic in-context learning), evaluation (heuristic + LLM judge), A/B testing + adaptive routing + fallbacks. Docker deploy. OpenAI-compatible API. Fortune 50 production use. USE FOR:

• unified LLM gateway 18+ providers
• LLM observability and feedback collection
• prompt optimization and fine-tuning
• A/B testing LLM models
• adaptive routing fallbacks retries
• evaluate LLM outputs with judges tags: [TensorZero, LLM-gateway, optimization, observability, fine-tuning, A/B-testing, OpenAI-compatible] kind: platform category: ai-agent-builder

---

What Is TensorZero?

Production-grade LLM gateway with optimization, observability, evaluation, and experimentation.

• Repo: https://github.com/tensorzero/tensorzero
• Deploy: Docker
• API: OpenAI-compatible (drop-in replacement)

Quick Start

from openai import OpenAI

client = OpenAI(base_url="http://localhost:3000/openai/v1")
response = client.chat.completions.create(
    model="tensorzero::my_function::anthropic::claude-sonnet-4-6",
    messages=[{"role": "user", "content": "Hello!"}]
)

Core Capabilities

Feature	Details
Gateway	18+ providers, sub-ms overhead, tool use, structured outputs, multimodal, caching
Observability	Stores inferences + feedback in PostgreSQL, OpenTelemetry, Prometheus
Optimization	Fine-tuning, GEPA automated prompt engineering, dynamic in-context learning
Evaluation	Heuristic benchmarks + LLM-as-judge for individual inferences
Experimentation	Built-in A/B testing, adaptive routing, fallbacks, retries

Supported Providers

Anthropic · OpenAI · Google Vertex/Gemini · AWS Bedrock/SageMaker · Azure DeepSeek · Groq · Mistral · Together AI · vLLM · OpenAI-compatible APIs

---

KNOWLEDGE INJECTION: PentAGI (vxcontrol)

Source: https://github.com/vxcontrol/pentagi

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: pentagi-security-agents

name: pentagi-security-agents description: > PentAGI - autonomous AI penetration testing platform. Multi-agent architecture: Orchestrator + Researcher + Developer + Executor + Searcher/Enricher/Memorist/Reporter. 20+ built-in security tools (nmap, metasploit, sqlmap). Long-term vector memory (PostgreSQL). Knowledge graph (Neo4j). Sandboxed Docker execution. 10+ LLM providers. REST+GraphQL API. Grafana/Prometheus monitoring. For authorized security testing and research only. USE FOR:

• authorized automated penetration testing
• multi-agent security research platform
• vulnerability assessment automation
• nmap metasploit sqlmap integration
• AI-driven security tool orchestration tags: [PentAGI, penetration-testing, security, multi-agent, nmap, metasploit, Docker, authorized-testing] kind: platform category: ai-agent-builder

---

What Is PentAGI?

For authorized security testing only. Autonomous AI penetration testing platform.

• Repo: https://github.com/vxcontrol/pentagi
• Deploy: Docker Compose

Agent Architecture

Orchestrator
  Researcher    → target analysis, intelligence gathering
  Developer     → attack strategy planning
  Executor      → implements attack plan
  Searcher      → information retrieval
  Enricher      → context enrichment
  Memorist      → long-term memory management
  Reporter      → results documentation
  Adviser       → strategy recommendations
  Reflector     → execution review
  Planner       → task decomposition (3-7 steps)

Built-in Security Tools (20+)

nmap · metasploit · sqlmap · + 17 others

Memory & Knowledge

• Long-term memory: vector embeddings in PostgreSQL
• Knowledge graph: Neo4j for semantic relationships
• Mentor supervision: detects repetitive patterns, suggests alternatives

Supported LLM Providers

OpenAI · Anthropic · Google AI · AWS Bedrock · Ollama · + 5 others

Limits & Safety

• Tool call limits: 100 (general agents), 20 (limited agents)
• Sandboxed Docker execution (complete isolation)
• Execution monitoring with automatic mentor intervention

---

KNOWLEDGE INJECTION: Arize Phoenix

Source: https://github.com/Arize-ai/phoenix

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: arize-phoenix-observability

name: arize-phoenix-observability description: > Arize Phoenix - open-source AI observability platform. OpenTelemetry-based LLM tracing, LLM-powered evaluation (response + retrieval quality), version-controlled datasets, experiment tracking (prompts/models/retrieval). Integrates LangGraph, LlamaIndex, CrewAI, DSPy, Claude Agent SDK, OpenAI, Anthropic, Bedrock. Prompt playground + management. pip install arize-phoenix. Local/notebook/Docker/cloud. USE FOR:

• LLM application tracing and observability
• evaluate LLM response and retrieval quality
• version-controlled test datasets
• track prompt model retrieval experiments
• LangChain LangGraph LlamaIndex tracing
• Claude agent SDK observability tags: [Phoenix, Arize, observability, tracing, evaluation, LLM, OpenTelemetry, LangChain, LlamaIndex] kind: platform category: ai-agent-builder

---

What Is Arize Phoenix?

Open-source AI observability — trace, evaluate, and experiment on LLM applications.

• Repo: https://github.com/Arize-ai/phoenix
• Cloud: app.phoenix.arize.com
• Vendor and language agnostic

Install

pip install arize-phoenix

# Docker
docker pull arizephoenix/phoenix
docker run -p 6006:6006 arizephoenix/phoenix
# UI: http://localhost:6006

4 Core Capabilities

Capability	What It Does
Tracing	OpenTelemetry-based runtime tracing of LLM calls, chains, agents
Evaluation	LLM-powered benchmarks: response quality, retrieval accuracy
Datasets	Version-controlled example collections for testing + fine-tuning
Experiments	Track changes to prompts, models, retrieval — compare results

Quick Start

import phoenix as px
from openinference.instrumentation.langchain import LangChainInstrumentor

# Start local Phoenix
session = px.launch_app()

# Auto-instrument LangChain
LangChainInstrumentor().instrument()

# All LangChain calls now traced at http://localhost:6006

Supported Frameworks

LangGraph · LlamaIndex · CrewAI · DSPy · Claude Agent SDK OpenAI · Anthropic · Google GenAI · AWS Bedrock

Prompt Management

• Version-controlled prompt templates
• Tagging and rollback
• A/B comparison in playground
• Model comparison side-by-side

---

KNOWLEDGE INJECTION: Awesome Agent Skills (VoltAgent)

Source: https://github.com/VoltAgent/awesome-agent-skills

Routed to: claude-ai-tools.md

Date: 2026-03-18

Awesome Agent Skills — Curated Registry (549+ skills)

Real-world agent skills from 40+ organizations. Works with Claude Code, Codex, Gemini CLI, Cursor, GitHub Copilot.

Source: https://github.com/VoltAgent/awesome-agent-skills

Notable Skills by Category

Official / Anthropic

• anthropics/docx — Create, edit, analyze Word documents
• PDF, presentations, design, web artifacts

Infrastructure & DevOps

• Vercel, Cloudflare, Netlify, AWS, Google Cloud skills
• HashiCorp Terraform code generation
• Kubernetes, Docker, CI/CD workflows

Databases & Data

• Supabase, Neon, ClickHouse, Tinybird

Dev Frameworks

• React, Next.js, React Native, Expo, WordPress

AI/ML

• Hugging Face, Replicate, fal.ai, OpenAI integration

Security (Trail of Bits — 23 skills)

• Insecure defaults detection
• Property-based testing + smart contracts
• Semgrep rule creation for vulnerability detection

Product & SaaS

• Content strategy planning
• Pricing/packaging/monetization strategy
• CAC, LTV, payback period calculations (saas-economics-efficiency-metrics)
• Investor materials, fundraising

Web3/Crypto

• Binance trading tools
• Blockchain interaction skills

Security warning: Skills are curated, not audited. Review before install. Scanners: Snyk Skill Security Scanner, Agent Trust Hub.

---

KNOWLEDGE INJECTION: wshobson/ai-trading-crew (112 agents, 146 skills)

Source: https://github.com/wshobson/agents

Routed to: claude-ai-tools.md

Date: 2026-03-18

wshobson/ai-trading-crew — 112 Agents + 146 Skills System

Production Claude Code plugin system: 112 agents, 146 skills, 79 tools, 72 plugins.

Architecture

• 72 focused single-purpose plugins (1-6 per category)
• 23 categories, mix-and-match install
• 3-tier model: Opus (complex) / Sonnet (mid) / Haiku (simple)
• Progressive disclosure: skills load only when activated

Install

/plugin marketplace add wshobson/agents
/plugin install python-development
/plugin install security-audit

Key Plugin Categories

• Language specialists: Python, JS/TS, Go, Rust, systems
• Infrastructure: Kubernetes, cloud, CI/CD
• Security + compliance
• Data engineering + MLOps
• Full-stack + framework-specific
• Business ops + documentation
• Quantitative trading + risk management (limited)
• Payments: Stripe, PayPal, billing

---

KNOWLEDGE INJECTION: Ruflo (Enterprise Agent Orchestration)

Source: https://github.com/ruvnet/ruflo

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: ruflo-agent-orchestration

name: ruflo-agent-orchestration description: > Ruflo (formerly Claude Flow) - enterprise multi-agent orchestration for Claude Code. 60+ specialized agents in swarms (hierarchical/mesh/ring/star topologies). Self-learning SONA architecture, HNSW vector search (150x-12500x faster retrieval), EWC++ anti-forgetting, Mixture of Experts routing. MCP server. npx ruflo@latest init USE FOR:

• enterprise multi-agent orchestration
• Claude Code swarm of agents
• self-learning agent routing
• 60+ specialized agents
• MCP server for Claude Code tags: [ruflo, multi-agent, swarm, orchestration, Claude-Code, MCP, self-learning, HNSW] kind: framework category: ai-agent-builder

---

What Is Ruflo?

Enterprise AI agent orchestration platform built on Claude Code.

• Repo: https://github.com/ruvnet/ruflo
• Install: npx ruflo@latest init --wizard
• Claude Code MCP: claude mcp add ruflo -- npx ruflo@latest mcp start

Key Differentiators vs CrewAI/LangGraph

Feature	Ruflo	CrewAI	LangGraph
Self-learning	SONA (0.05ms adapt)	No	No
Anti-forgetting	EWC++	No	No
Vector retrieval	HNSW (12500x faster)	Basic	Basic
Agent topologies	4 (hier/mesh/ring/star)	Hierarchical	Graph
Swarm size	Unlimited	Limited	Limited

Installation

curl -fsSL https://cdn.jsdelivr.net/gh/ruvnet/claude-flow@main/scripts/install.sh | bash
# or
npx ruflo@latest init --wizard

Usage

npx ruflo@latest --agent coder --task "Implement user authentication"
npx ruflo@latest --list                    # list all 60+ agents
npx ruflo@latest mcp start                 # start MCP server

Swarm Topologies

Hierarchical: Queen agent → Worker agents (tree structure)
Mesh:         All agents peer-to-peer (collaborative)
Ring:         Sequential pipeline (A→B→C→A)
Star:         Central coordinator → spoke agents

---

KNOWLEDGE INJECTION: alirezarezvani/claude-skills (204 skills)

Source: https://github.com/alirezarezvani/claude-skills

Routed to: claude-ai-tools.md

Date: 2026-03-18

claude-skills — 204 Production Skills Library

Most comprehensive open-source Claude Code skills library (204 skills, 266 Python CLI tools). Compatible with 11 AI coding platforms.

Skill Domains

Domain	Skills	Notable
Engineering Core	25	Architecture, DevOps, security, AI/ML
Engineering POWERFUL	30	Advanced tier skills
Playwright Testing	9+	Test generation, migration
Product Management	13	Strategy, UX research, analytics
Marketing	43	7 specialized pods
Project Management	6	PM, scrum, Jira/Confluence
Regulatory/Quality	12	FDA, ISO, MDR, GDPR
C-Level Advisory	28	Full executive suite
Business & Growth	4	Customer success, sales
Finance	2	Financial analysis, SaaS metrics

Skill Structure

skill-name/
├── SKILL.md          # structured instructions + workflow
├── tools/            # Python CLI tools (standard library only)
│   └── tool_name.py
├── scripts/          # optional automation scripts
└── references/       # templates + domain knowledge

Install

# Claude Code plugin marketplace
/plugin install alirezarezvani/claude-skills/engineering

# Manual: copy skill folder to ~/.claude/skills/
cp -r marketing-pod ~/.claude/skills/

---

KNOWLEDGE INJECTION: ARIS (Auto Research in Sleep)

Source: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: aris-auto-research

name: aris-auto-research description: > ARIS - Autonomous ML research methodology using Claude Code + GPT adversarial reviewer. 4 workflows: idea discovery, experiment bridge, auto-review loop, paper writing. 20 composable skills. State file recovery (no daemon needed). GPU automation via SSH. Proven: borderline reject (5/10) -> submission-ready (7.5/10) in 4 overnight rounds. USE FOR:

• autonomous ML research overnight
• auto paper review and improvement loop
• GPU experiment automation SSH
• academic paper writing Claude Code
• cross-model adversarial review (Claude + GPT) tags: [research, autonomous, ML, paper-writing, GPU, Claude-Code, adversarial-review, overnight] kind: methodology category: ai-agent-builder

---

What Is ARIS?

Autonomous ML research methodology — Claude Code executes, GPT-5.4 reviews adversarially.

• Repo: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep
• Result: borderline reject → submission-ready in 4 overnight rounds (5/10 → 7.5/10)
• Philosophy: cross-model collaboration avoids local minima of self-critique

4 Workflows

Workflow 1:   Idea Discovery
              literature survey → 8-12 ideas → novelty check → GPU pilots → ranked report

Workflow 1.5: Experiment Bridge
              reads plan → implements code → validates small-scale → deploys full GPU suite

Workflow 2:   Auto Review Loop (key workflow)
              GPT-5.4 reviews paper → identifies gaps → Claude Code writes experiments
              → SSH deploys to GPU → monitors → rewrites sections → repeat (max 4 rounds)

Workflow 3:   Paper Writing
              narrative → claims-evidence matrix → figures/tables → LaTeX → PDF → 2-round auto-improve

Slash Commands

/research-pipeline "your direction"   # full pipeline
/idea-discovery "topic"               # workflow 1
/experiment-bridge                    # workflow 1.5
/auto-review-loop "paper scope"       # workflow 2 (overnight key)
/paper-writing "NARRATIVE_REPORT.md"  # workflow 3

# With overrides:
/research-pipeline "topic" -- AUTO_PROCEED: false, wandb: true

State Recovery (Persistence)

Each skill saves progress to JSON after every round:
  REVIEW_STATE.json
  AUTO_REVIEW.md

If session terminates mid-loop:
  Restart Claude Code → skill reads state → resumes from last checkpoint

GPU Automation

Configure in project CLAUDE.md:

SSH_HOST: your-gpu-server.com
SSH_USER: ubuntu
CONDA_ENV: research
SLURM: true  # if using SLURM cluster

Skills handle: ssh → rsync code → conda activate → run experiments → fetch results

---

KNOWLEDGE INJECTION: AI Maestro

Source: https://github.com/23blocks-OS/ai-maestro

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: ai-maestro-orchestration

name: ai-maestro-orchestration description: > AI Maestro - centralized dashboard for managing 35+ AI agents across machines. Peer mesh network (no central server), agent-to-agent messaging (AMP protocol), Code Graph visualization, Kanban task tracking, persistent memory, tmux auto-discovery. Gateway: Slack/Discord/Email/WhatsApp. curl install, Node.js 18+, tmux required. USE FOR:

• manage many AI agents from one dashboard
• agent-to-agent communication
• multi-machine AI coordination
• Code Graph codebase visualization
• tmux session management for agents tags: [AI-Maestro, multi-agent, dashboard, tmux, mesh-network, AMP, Code-Graph, Kanban] kind: tool category: ai-agent-builder

---

What Is AI Maestro?

Centralized dashboard for managing distributed AI agent workforces.

• Repo: https://github.com/23blocks-OS/ai-maestro
• Use case: built for devs managing 35+ agents across terminals

Install

curl -fsSL https://raw.githubusercontent.com/23blocks-OS/ai-maestro/main/scripts/remote-install.sh | sh
# Requirements: Node.js 18+, tmux

Core Features

Feature	Description
Auto-discovery	Finds existing tmux sessions automatically
Peer mesh	No central server — equal-status machines
AMP protocol	Agent-to-Agent Messaging with priority + crypto signatures
Code Graph	Interactive codebase visualization, delta indexing
Kanban	Task dependencies + status tracking
Persistent memory	Cross-session contextual continuity
War rooms	Multi-agent team assembly
Gateways	Slack, Discord, Email, WhatsApp routing

Agent Messaging Protocol (AMP)

Message types: command, query, response, broadcast
Priority: critical > high > normal > low
Signature: cryptographic for auth
Push notifications: real-time delivery

---

KNOWLEDGE INJECTION: Babysitter (Agent Workflow Control)

Source: https://github.com/a5c-ai/babysitter

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: babysitter-agent-control

name: babysitter-agent-control description: > Babysitter - deterministic agent workflow framework. Process-as-code (JS functions), mandatory stops between steps, enforced quality gates, event-sourced audit journal. 50-67% token compression. 2000+ pre-built process templates. 4 modes: interactive, autonomous (yolo), planning, continuous (forever monitoring). USE FOR:

• deterministic agent workflow (no hallucination drift)
• quality convergence loops
• human-in-the-loop agent control
• resume interrupted agent workflows
• parallel dependent task execution
• 2000+ pre-built process templates tags: [babysitter, deterministic, agent-control, quality-gates, audit-journal, workflow, Claude-Code] kind: framework category: ai-agent-builder

---

What Is Babysitter?

Deterministic agent workflow framework — agents can only execute what the process permits.

• Repo: https://github.com/a5c-ai/babysitter
• Install: via Claude Code plugin marketplace

Core Mechanisms

Process as Code  → JS functions define exactly what agents may do
Mandatory Stops  → every step halts; process logic decides next action
Enforced Gates   → quality thresholds block progression
Event Journal    → immutable audit trail, replay from any checkpoint

4 Execution Modes

/babysitter:call     # Interactive — pauses for human approval
/babysitter:yolo     # Autonomous — fully automatic
/babysitter:plan     # Planning — review before executing
/babysitter:forever  # Continuous — indefinite monitoring

Quality Convergence

// Automated refinement until threshold met
process.qualityGate({
  check: () => runTests(),
  threshold: 0.95,          // 95% pass rate required
  maxRounds: 5,             // max refinement attempts
  onFail: "refine-code"     // action if threshold not met
})

Token Compression

50-67% context reduction built-in — summarizes completed steps, retains only active context.

Run Resumption

/babysitter:observe    # live dashboard
/babysitter:resume     # continue interrupted workflow from checkpoint

Pre-Built Process Library

2,000+ templates covering:

• Code review + refactoring
• Test generation
• Documentation
• Security audits
• Data pipeline validation
• Content generation

---

KNOWLEDGE INJECTION: Awesome Claude Code (hesreallyhim)

Source: https://github.com/hesreallyhim/awesome-claude-code

Routed to: claude-ai-tools.md

Date: 2026-03-18

Awesome Claude Code — 400+ Resource Reference

Curated index of 400+ Claude Code extensions, skills, tools, hooks, and workflows.

Agent Skills (Highlights)

• Trail of Bits Security Skills — 12+ skills for code auditing + vulnerability detection
• Everything Claude Code — Wide-ranging engineering domain skills
• Fullstack Dev Skills — 65 skills across full-stack frameworks + Jira
• Claude Scientific Skills — Research, engineering, finance, writing
• Superpowers — SDLC planning through debugging competencies
• TACHES Resources — Sub agents, skills, meta-skills, workflow adaptation
• AgentSys — Task-to-production, PR management, multi-agent review
• Compound Engineering Plugin — Error-to-learning discipline agents

Orchestrators

• Claude Swarm — Multi-agent session orchestration
• Claude Squad — Terminal app, multiple agents in separate workspaces
• Claude Task Master — Task management for AI-driven development
• Auto-Claude — Multi-agent kanban UI for full SDLC automation
• Claude Code Flow — Code-first orchestration for autonomous agent cycles
• TSK — Rust CLI delegating to sandboxed Docker agents
• Happy Coder — Multi-instance control from phone/desktop

Key Tools

• claudekit — CLI toolkit: auto-save, quality hooks, 20+ subagents
• SuperClaude — Configuration framework with commands and personas
• cchistory — Session command history like shell history
• cclogviewer — HTML UI for JSONL conversation files
• recall — Full-text session search with terminal interface
• Container Use — Safe multi-agent isolation environments
• Rulesync — Auto-generates configs for Claude Code, Cursor, others
• claude-code-karma — Analytics dashboard for sessions/tokens/costs (see separate entry)
• Vibe-Log — Local prompt analysis with session analytics

Hooks (Highlights)

• parry — Prompt injection scanner detecting attacks/exfiltration
• TDD Guard — Blocks TDD-violating changes in real-time
• Dippy — Auto-approve safe bash, prompt for destructive operations
• CC Notify — Desktop notifications for input needs + task completion
• cchooks — Lightweight Python SDK with clean API

Status Lines

• claude-code-statusline — 4-line statusline with themes and cost tracking
• ccstatusline — Customizable: model, branch, token usage
• claude-powerline — Vim-style powerline with real-time tracking
• CCometixLine — High-performance Rust statusline with git integration

Key Slash Commands

Command	Purpose
/commit	Conventional commit with emojis
/create-pr	PR creation workflow
/tdd	TDD enforcement
/check	Code quality + security checks
/context-prime	Comprehensive project priming
/prd-generator	Product Requirements Document generation
/optimize	Performance bottleneck identification
/mermaid	ER diagram generation from SQL

Workflow Systems (Highlights)

• RIPER Workflow — Research, Innovate, Plan, Execute, Review phases
• AB Method — Spec-driven, large problems into focused missions
• Ralph Wiggum Techniques — Autonomous loop with exit detection + safety
• Claude Code PM — Project management with specialized agents + commands
• Simone — Broader PM with documents, guidelines, processes
• Spec-Flow — Phase-gated structured development (see separate entry)

Usage Monitors

• Claude Code Usage Monitor — Real-time terminal with burn rate predictions
• CC Usage — Dashboard for cost + token analysis
• ccflare — Web UI usage dashboard with comprehensive metrics

Full index: https://github.com/hesreallyhim/awesome-claude-code

---

KNOWLEDGE INJECTION: Refly (refly-ai)

Source: https://github.com/refly-ai/refly

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: refly-agent-skill-builder

name: refly-agent-skill-builder description: > Refly - open-source platform transforming enterprise workflows into versioned agent skills. Copilot-led DSL compiler: describe workflow in natural language, compiles in <3 minutes. Intervenable runtime (pause/audit/redirect mid-execution). Exports to Claude Code, Cursor, MCP. 3000+ native integrations: Stripe, Slack, Salesforce, GitHub, MCP servers. Central skill registry, version-controlled, team-shareable. USE FOR:

• codify business workflows as agent skills
• natural language to agent skill compiler
• intervenable runtime pause audit redirect
• deploy skills as API webhook or Claude Code tool
• 3000+ tool integrations for agent skills tags: [Refly, agent-skills, enterprise, workflow, DSL, MCP, Claude-Code, versioned, registry] kind: platform category: ai-agent-builder

---

What Is Refly?

Open-source platform for building versioned enterprise agent skills.

• Repo: https://github.com/refly-ai/refly
• Install: npm install -g @powerformer/refly-cli

Architecture

Input Layer:     3000+ tools, MCP servers, private connectors
Processing Layer: Vibe-driven DSL compiler + stateful intervenable runtime
Output Layer:    Claude Code, Cursor, APIs, webhooks, agent frameworks

Usage

npm install -g @powerformer/refly-cli

# Install a skill from registry
refly skill install <skill-id>

# Publish your skill
refly skill publish <skill-id>

# Execute workflow via API
curl -X POST https://instance/api/v1/workflows/{WORKFLOW_ID}/execute \
  -H "Authorization: Bearer API_KEY"

Key Differentiators

• Under 3 minutes from natural language description to deployed skill
• Intervenable: pause, audit, redirect agent logic during execution (compliance-friendly)
• Universal export: skills become APIs, webhooks, or native Claude Code/Cursor tools
• Central registry: version-controlled skills shareable across teams

---

KNOWLEDGE INJECTION: prompts.chat (f/prompts.chat)

Source: https://github.com/f/prompts.chat

Routed to: claude-ai-tools.md

Date: 2026-03-18

prompts.chat — World Largest Open-Source Prompt Library

143,000+ GitHub stars. Works with ChatGPT, Claude, Gemini, Llama, Mistral. Originally launched as Awesome ChatGPT Prompts (Dec 2022).

Access

# CLI
npx prompts.chat

# Claude Code plugin
/plugin install prompts.chat

# MCP server
# add to MCP config

# Browse online: https://prompts.chat/prompts
# CSV/Markdown/Hugging Face dataset available

Key Facts

• 143,000+ GitHub stars
• 40+ academic citations
• Most liked dataset on Hugging Face
• CC0 1.0 license (public domain, unrestricted use)
• Featured by Forbes, Harvard, Columbia
• Self-hosting option available

Categories

• Act As prompts (persona-based): Linux terminal, SQL terminal, JavaScript console, Excel sheet, etc.
• Writing and communication
• Analysis and research
• Code generation and review
• Educational explanations

Prompt Engineering Guide

Free interactive guide with 25+ chapters on techniques:

• Zero-shot, few-shot, chain-of-thought
• Role prompting, output formatting
• Task decomposition, context injection

Full prompt browser: https://prompts.chat/prompts

---

KNOWLEDGE INJECTION: AntV MCP Server Chart

Source: https://github.com/antvis/mcp-server-chart

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: mcp-server-chart

name: mcp-server-chart description: > AntV MCP Server Chart - MCP server generating 26+ chart types via AntV. Tools: generate_bar_chart, generate_line_chart, generate_pie_chart, generate_network_graph, generate_sankey, generate_treemap, generate_spreadsheet, etc. npx @antv/mcp-server-chart. Works with Claude, VSCode, Dify. USE FOR:

• generate charts via MCP
• bar/line/pie/scatter chart from data
• network graph visualization
• sankey treemap funnel chart
• Claude generates charts automatically tags: [MCP, charts, AntV, visualization, bar, line, pie, network-graph, sankey] kind: tool category: mcp-integration

---

What Is mcp-server-chart?

MCP server generating 26+ visualization types using AntV.

• Repo: https://github.com/antvis/mcp-server-chart
• Install: npm install -g @antv/mcp-server-chart

MCP Config (Claude Code / Desktop)

{
  "mcpServers": {
    "mcp-server-chart": {
      "command": "npx",
      "args": ["-y", "@antv/mcp-server-chart"]
    }
  }
}

Available Tools (generate_* pattern)

Standard:    area, bar, column, line, pie, scatter, dual_axes
Statistical: boxplot, histogram, violin
Flow:        funnel, sankey, treemap
Hierarchy:   mind_map, fishbone, org_chart
Network:     network_graph, venn
Geographic:  district_map, path_map, pin_map
Other:       radar, word_cloud, liquid, spreadsheet

Usage in Claude

User: "Plot this data as a bar chart: [data]"
Claude: calls generate_bar_chart({ data: [...], xField: "x", yField: "y" })
→ returns chart image/URL

---

KNOWLEDGE INJECTION: Dify

Source: https://github.com/langgenius/dify

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: dify-llm-platform

name: dify-llm-platform description: > Dify - open-source LLM app development platform. Visual canvas for AI workflows, RAG pipelines (PDF/PPT ingestion), agent builder (50+ tools: Google, DALL-E, Wolfram), LLMOps observability, BaaS APIs. Supports GPT, Claude, Llama3, Mistral, 100+ models. Self-host (Docker) or cloud (200 free GPT-4 calls). USE FOR:

• build LLM app with visual workflow
• RAG pipeline from documents
• AI agent with tools
• self-hosted ChatGPT alternative
• LLMOps monitoring tags: [Dify, LLM, RAG, agent, workflow, visual, self-hosted, open-source, GPT, Claude] kind: platform category: ai-agent-builder

---

What Is Dify?

Open-source LLM application development platform.

• Repo: https://github.com/langgenius/dify
• Stars: 100k+
• Deploy: Docker Compose (2 CPU, 4GB RAM) or dify.ai cloud

Core Capabilities

• Visual Workflow Canvas: drag-and-drop LLM pipeline builder
• RAG: ingest PDFs, PPTs, web pages → vector search → grounded answers
• Agent Builder: Function Calling or ReAct agents + 50+ built-in tools
• Model Hub: GPT-4o, Claude, Llama3, Mistral, Gemini, + OpenAI-compatible
• LLMOps: trace every call, monitor cost, replay prompts
• BaaS API: REST API for any app to call your workflow

Docker Install

git clone https://github.com/langgenius/dify
cd dify/docker
cp .env.example .env
docker compose up -d
# Access: http://localhost/install

Agent Tools (50+)

Google Search, Bing, DuckDuckGo, Wikipedia, DALL-E, Stable Diffusion, WolframAlpha, Weather API, News API, Code execution, Web scraping, + custom tools

---

KNOWLEDGE INJECTION: Open WebUI

Source: https://github.com/open-webui/open-webui

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: open-webui

name: open-webui description: > Open WebUI - self-hosted, offline-capable AI platform. Ollama + OpenAI-compatible backends. RAG with 9 vector DBs, web search (15+ providers), image gen (DALL-E/ComfyUI), voice/video chat, Python function calling, enterprise auth (LDAP/OAuth/SCIM). Docker install. Privacy-first local AI deployment. USE FOR:

• self-hosted ChatGPT alternative
• local Ollama web interface
• offline AI with RAG
• multi-model comparison
• enterprise private AI deployment tags: [Open-WebUI, Ollama, self-hosted, RAG, local-AI, privacy, ChatGPT-alternative] kind: platform category: ai-agent-builder

---

What Is Open WebUI?

Extensible self-hosted AI platform — runs fully offline.

• Repo: https://github.com/open-webui/open-webui
• Supports: Ollama (local LLMs) + any OpenAI-compatible API

Quick Install

# With Ollama bundled
docker run -d -p 3000:8080 --gpus=all   -v ollama:/root/.ollama -v open-webui:/app/backend/data   --name open-webui ghcr.io/open-webui/open-webui:ollama

# Existing Ollama
docker run -d -p 3000:8080   --add-host=host.docker.internal:host-gateway   -v open-webui:/app/backend/data   --name open-webui ghcr.io/open-webui/open-webui:main
# Access: http://localhost:3000

Key Features vs ChatGPT

Feature	Open WebUI	ChatGPT
Self-hosted	Yes	No
Offline	Yes	No
Local models	Ollama	No
RAG	9 vector DBs	Limited
Web search	15+ providers	Yes
Image gen	DALL-E/ComfyUI/A1111	DALL-E only
Python tools	Native	Sandboxed
Cost	Free	$20/mo

---

KNOWLEDGE INJECTION: Awesome MCP Servers (Reference)

Source: https://github.com/punkpeye/awesome-mcp-servers

Routed to: claude-ai-tools.md

Date: 2026-03-18

Awesome MCP Servers — Ecosystem Reference

500+ MCP servers across 40+ categories. Key ones by domain:

Development & Code

Server	What It Does
GitHub MCP	Repos, PRs, issues, code search
GitLab MCP	GitLab API integration
Filesystem MCP	Local file read/write/search
Git MCP	git log, diff, branch operations
Docker MCP	Container management
Kubernetes MCP	Cluster insights, kubectl

AI & Agents

Server	What It Does
Memory MCP	Persistent knowledge graph
Sequential Thinking	Chain-of-thought reasoning
Fetch/Browser	Web content retrieval
Playwright MCP	Browser automation
AgentShield	Security vulnerability scanning

Data & Databases

Server	What It Does
PostgreSQL MCP	Schema inspection + queries
MongoDB MCP	Document DB queries
Elasticsearch MCP	Search and analytics
Snowflake MCP	Data warehouse queries
SQLite MCP	Local database

Communication

Server	What It Does
Gmail MCP	Email read/send/search
Slack MCP	Channel messages, search
Telegram MCP	Bot messages
Discord MCP	Server interaction

Productivity

Server	What It Does
Notion MCP	Pages, databases, blocks
Jira MCP	Issues, sprints, projects
Google Calendar MCP	Events, scheduling
Airtable MCP	Base/table operations

Finance & Trading

Server	What It Does
Crypto APIs	Price feeds, portfolio
Payment MCP	Stripe, payment processing
Multi-cloud cost	Cloud cost analysis

Search & Data

Server	What It Does
Brave Search	Web search
Firecrawl	Deep web scraping
EXA MCP	AI-powered search
75+ data extractors	Specialized data sources

Full list: https://github.com/punkpeye/awesome-mcp-servers

---

KNOWLEDGE INJECTION: Everything Claude Code

Source: https://github.com/affaan-m/everything-claude-code

Routed to: claude-ai-tools.md

Date: 2026-03-18

Everything Claude Code — Production Optimization Reference

Complete system for maximizing Claude Code performance (10+ months production-tested).

Components Overview

Component	Count	Purpose
Agents	21-25	Specialized subagents (planner, architect, reviewer, security, language-specific)
Skills	102+	Domain workflows (TDD, security review, frontend/backend patterns)
Commands	52-57	Slash commands: /tdd, /plan, /code-review, /build-fix, /e2e
Rules	29-34	Universal + language-specific (TS, Python, Go, Swift, PHP, Java)
Hooks	8-20+	PreToolUse, PostToolUse, Stop, SessionStart automations
MCP	14+	Pre-configured: GitHub, Supabase, Vercel, Railway

Key Slash Commands

/tdd           - Test-driven development workflow
/plan          - Architecture planning
/code-review   - Security + quality review
/build-fix     - Build error diagnosis
/e2e           - End-to-end test generation
/multi-plan    - Multi-agent orchestration
/instinct-status - Check learned patterns
/evolve        - Extract patterns from session into skills

Token Optimization Strategies

• Use Haiku/Sonnet for simple tasks, Opus for complex reasoning
• Compress context with /compact before long sessions
• Use subagents with limited scope (avoid full context bleed)
• Skills reduce re-explanation overhead
• Auto-extract patterns → reusable skills over time

Hooks Patterns

{
  "hooks": {
    "PreToolUse": ["secret-detector", "format-checker"],
    "PostToolUse": ["session-persist", "pattern-extractor"],
    "Stop": ["summary-generator"],
    "SessionStart": ["context-loader", "instinct-injector"]
  }
}

Specialized Agents

• planner: breaks task into subtasks, creates task graph
• architect: system design, file structure decisions
• code-reviewer: security + style + logic review
• security-reviewer: OWASP, injection, auth vulnerabilities
• go/python/ts-reviewer: language-specific best practices

---

KNOWLEDGE INJECTION: Cherry Studio

Source: https://github.com/CherryHQ/cherry-studio

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: cherry-studio

name: cherry-studio description: > Cherry Studio - desktop AI client for Windows/Mac/Linux. Unifies 20+ AI providers: OpenAI, Anthropic Claude, Gemini, Ollama (local), LM Studio, Qwen, Kimi. 300+ pre-configured assistants, multi-model simultaneous chat, MCP server support, document processing (PDF/Office/images), no environment setup needed. USE FOR:

• unified desktop AI client
• multi-model comparison chat
• local + cloud AI in one app
• 300+ pre-built AI assistants
• MCP integration desktop tags: [Cherry-Studio, desktop, multi-model, Ollama, Claude, GPT, Gemini, MCP, assistants] kind: tool category: ai-agent-builder

---

What Is Cherry Studio?

Unified desktop AI client — no setup required.

• Repo: https://github.com/CherryHQ/cherry-studio
• Platforms: Windows, macOS, Linux
• Models: 20+ providers, local (Ollama/LM Studio) + cloud

Supported Providers

OpenAI (GPT-4o) · Anthropic (Claude) · Google (Gemini) · Mistral Ollama (local) · LM Studio (local) · Qwen · Kimi · Baidu · iFlytek OpenRouter · Perplexity · Poe · + more

Key Features

• 300+ assistants: pre-configured prompts for coding, writing, analysis
• Multi-model chat: send same prompt to multiple models simultaneously
• Document processing: PDF, Word, Excel, PowerPoint, images
• MCP support: connect MCP servers (with Marketplace planned)
• WebDAV sync: sync conversations across devices
• No setup: download → login/API key → use immediately

---

KNOWLEDGE INJECTION: AionUi

Source: https://github.com/iOfficeAI/AionUi

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: aionui-desktop-agent

name: aionui-desktop-agent description: > AionUi - free open-source multi-agent desktop platform (macOS/Windows/Linux). Auto-detects Claude Code, Codex, Qwen Code CLI tools. 20+ AI providers. Cron scheduling for 24/7 unattended automation. File management, Excel AI, PowerPoint generation, image recognition. Remote access via Telegram/Lark/DingTalk. USE FOR:

• multi-agent desktop automation
• unify Claude Code + Codex in one UI
• cron scheduled AI tasks
• file batch rename organize
• Excel AI processing
• remote AI control via Telegram tags: [AionUi, desktop-agent, multi-agent, Claude-Code, automation, scheduling, Telegram, remote] kind: tool category: ai-agent-builder

---

What Is AionUi?

Multi-agent desktop platform — AI agents operate autonomously on your computer.

• Repo: https://github.com/iOfficeAI/AionUi
• Platforms: macOS · Windows · Linux
• Install: download desktop app, sign in with Google or API key

Key Differentiators

• Auto-detects existing Claude Code, Codex, Qwen Code CLIs → unifies them
• Built-in agent: no CLI setup needed (full file/web/code capabilities)
• Multi-agent: run Claude Code + Codex simultaneously, independent contexts

Supported Models (20+ providers)

Gemini · Anthropic Claude · OpenAI · Qwen · Kimi · Baidu · Ollama · LM Studio

Automation Capabilities

Feature	Description
Cron scheduling	Natural language task descriptions, runs 24/7
File management	Batch rename, intelligent classification
Excel processing	AI analysis, formatting, report generation
Document generation	PowerPoint, Word, Markdown automated creation
Image operations	Text-to-image, editing, recognition

Remote Access

• WebUI (browser access)
• Telegram bot integration
• Lark/Feishu (enterprise)
• DingTalk

---

KNOWLEDGE INJECTION: geo-lint (IJONIS)

Source: https://github.com/IJONIS/geo-lint

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: geo-lint

name: geo-lint description: > geo-lint - first open-source linter for GEO (Generative Engine Optimization). 97 rules across 5 categories: GEO (AI citation readiness, E-E-A-T, RAG optimization), SEO (metadata/schema/keywords), content quality, technical (broken links/perf), i18n. Agent-first JSON output for auto-fix loops. Ensures content gets cited by ChatGPT, Perplexity, Google AI Overviews, Gemini. npm install -D @ijonis/geo-lint. Claude Code skill: /geo-lint audit. USE FOR:

• optimize content for AI citation (GEO)
• lint markdown for AI search visibility
• automated fix loop until zero violations
• E-E-A-T signal validation
• RAG optimization for content
• SEO + GEO combined content audit tags: [geo-lint, GEO, SEO, AI-citation, content-optimization, RAG, E-E-A-T, Claude-Code, linter] kind: tool category: ai-agent-builder

---

What Is geo-lint?

First open-source linter for Generative Engine Optimization — ensures content gets cited by AI search engines.

• Repo: https://github.com/IJONIS/geo-lint
• Install: npm install -D @ijonis/geo-lint
• Node.js 18+ required, zero peer dependencies

Install

npm install -D @ijonis/geo-lint

# Claude Code users:
curl -fsSL https://raw.githubusercontent.com/IJONIS/geo-lint/main/install.sh | bash

Commands

npx geo-lint                  # human-readable output
npx geo-lint --format=json    # machine-readable for AI agents
/geo-lint audit               # Claude Code skill: full site scan + fix
/content-creator setup        # create SEO/GEO-optimized content

97 Rules Across 5 Categories

Category	Rules	Focus
GEO	36	AI citation readiness, E-E-A-T signals, RAG optimization
SEO	34	Metadata, schema markup, keywords
Content Quality	14	Readability, word count, jargon
Technical	10	Broken links, performance
i18n	3	Translation pairs

Agent-First Fix Loop

geo-lint --format=json → violations with fix suggestions
→ AI agent modifies content
→ geo-lint re-runs
→ repeat until violations == 0

Each violation includes: file location, rule name, severity, plain-language fix instruction, machine-readable fix pattern.

Supported Formats

• Markdown / MDX (native)
• Extensible: Astro, HTML, CMS platforms

---

KNOWLEDGE INJECTION: LangChain

Source: https://github.com/langchain-ai/langchain

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: langchain-framework

name: langchain-framework description: > LangChain - leading framework for building LLM-powered applications and agents. Core: chains (sequential LLM pipelines), agents (tool-using reasoning loops), memory (conversation + long-term), RAG (retrieval-augmented generation), 100+ integrations (OpenAI, Anthropic, HuggingFace, Pinecone, Chroma, etc.). Model-agnostic: swap providers without code changes. Ecosystem: LangGraph (orchestration), LangSmith (debug/deploy). pip install langchain. USE FOR:

• build LLM application chains
• tool-using agent with ReAct or function calling
• RAG pipeline retrieval augmented generation
• conversation memory management
• swap LLM providers without changing code
• integrate with vector stores Pinecone Chroma FAISS tags: [LangChain, LLM, agents, chains, RAG, memory, tools, OpenAI, Anthropic, LangGraph, LangSmith] kind: framework category: ai-agent-builder

---

What Is LangChain?

Framework for building LLM-powered applications — agents, chains, RAG, memory, tools.

• Repo: https://github.com/langchain-ai/langchain
• Stars: 100k+
• Ecosystem: LangChain + LangGraph + LangSmith

Install

pip install langchain
pip install langchain-openai        # OpenAI provider
pip install langchain-anthropic     # Anthropic/Claude
pip install langchain-community     # community integrations
# or
uv add langchain

Quick Start

from langchain.chat_models import init_chat_model

model = init_chat_model("openai:gpt-4o")
result = model.invoke("Explain quantum computing in one sentence")

# Or Claude:
model = init_chat_model("anthropic:claude-sonnet-4-6")

Core Components

Chains — Sequential LLM pipelines:

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_template("Translate to French: {text}")
chain = prompt | ChatOpenAI(model="gpt-4o")
result = chain.invoke({"text": "Hello world"})

Agents — Tool-using reasoning loops:

from langchain.agents import create_react_agent, AgentExecutor
from langchain_community.tools import DuckDuckGoSearchRun

tools = [DuckDuckGoSearchRun()]
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)
executor.invoke({"input": "What happened in AI this week?"})

Memory — Conversation persistence:

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain

memory = ConversationBufferMemory()
chain = ConversationChain(llm=llm, memory=memory)

RAG Pipeline:

from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.chains import RetrievalQA

vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
qa_chain.invoke({"query": "What does the document say about X?"})

Ecosystem

Tool	Purpose
LangGraph	Stateful multi-agent orchestration (cycles + branching)
LangSmith	LLM app monitoring, debugging, evaluation, deployment
LangServe	Deploy chains as REST APIs

100+ Integrations

Models: OpenAI, Anthropic, HuggingFace, Cohere, Google, Ollama, Mistral Vector Stores: Pinecone, Chroma, FAISS, Weaviate, Qdrant, pgvector Tools: DuckDuckGo, SerpAPI, Wikipedia, Python REPL, SQL, Playwright Document Loaders: PDF, Word, HTML, CSV, YouTube, Notion, GitHub

---

KNOWLEDGE INJECTION: LobeHub

Source: https://github.com/lobehub/lobehub

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: lobehub-platform

name: lobehub-platform description: > LobeHub - open-source AI platform for human-agent co-evolution. Web app + desktop + PWA. Multi-model (OpenAI, Ollama, 100+ providers), 10,000+ MCP plugins marketplace, agent builder, agent groups, personal memory, real-time internet search, chain-of-thought visualization, voice TTS/STT, image generation, knowledge base. Self-host via Docker or Vercel one-click. USE FOR:

• self-hosted multi-model AI platform
• agent builder with customizable skills
• MCP marketplace 10000+ plugins
• team agent groups collaboration
• personal memory adaptive learning
• chain-of-thought visualization tags: [LobeHub, LobeChat, multi-model, self-hosted, agents, MCP, Ollama, GPT, Claude, voice] kind: platform category: ai-agent-builder

---

What Is LobeHub?

Open-source AI platform — web app, desktop, PWA with 10,000+ MCP plugins.

• Repo: https://github.com/lobehub/lobehub
• Main product: LobeChat — https://github.com/lobehub/lobe-chat

Quick Deploy

# Docker
docker run -d -p 3210:3210 lobehub/lobe-chat

# Vercel one-click (or Zeabur, Sealos, Alibaba Cloud)
# Set OPENAI_API_KEY or other provider credentials

Key Features

Feature	Details
Multi-model	OpenAI, Ollama (local), Anthropic, Gemini, 100+ providers
MCP Marketplace	10,000+ compatible plugins
Agent Builder	Custom agents with tools, memory, persona
Agent Groups	Team-based agent collaboration
Personal Memory	Adaptive learning across sessions
Internet Search	Real-time web integration
Chain of Thought	Visualize AI reasoning steps
Voice	TTS/STT conversational interface
Image Gen	Generation + visual recognition
Knowledge Base	File uploads, RAG

---

KNOWLEDGE INJECTION: claude-mem (thedotmack)

Source: https://github.com/thedotmack/claude-mem

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: claude-mem

name: claude-mem description: > claude-mem - persistent memory compression system for Claude Code. Auto-captures tool usage, generates semantic summaries, persists across sessions. Progressive disclosure (layered retrieval with token costs). mem-search skill for natural language history queries. 4 MCP tools: search, timeline, get_observations. Web viewer at localhost:37777. Private tag for sensitive content. Install via /plugin marketplace add thedotmack/claude-mem. USE FOR:

• persistent Claude Code memory across sessions
• semantic session history search
• context compression and retrieval
• web UI for memory visualization
• MCP tools for memory queries tags: [claude-mem, persistent-memory, Claude-Code, session-continuity, compression, MCP, search] kind: tool category: ai-agent-builder

---

What Is claude-mem?

Persistent memory compression system — context survives Claude Code session restarts.

• Repo: https://github.com/thedotmack/claude-mem
• Web UI: http://localhost:37777

Install

# Via Claude Code Plugin Marketplace (recommended)
/plugin marketplace add thedotmack/claude-mem
/plugin install claude-mem
# Restart Claude Code

# Via OpenClaw Gateway
curl -fsSL https://install.cmem.ai/openclaw.sh | bash

Features

• Automatic: captures tool usage, generates semantic summaries with no manual action
• Progressive Disclosure: layered retrieval showing token costs per memory level
• mem-search skill: natural language queries into project history
• Privacy: wrap content in <private> tags to exclude from memory
• Citations: reference past observations by ID

MCP Tools

Tool	Purpose
search	Query memory index by natural language
timeline	Chronological context view
get_observations	Fetch full details by observation ID

Web Viewer (localhost:37777)

Real-time memory stream visualization + settings management.

---

KNOWLEDGE INJECTION: Continue (continuedev)

Source: https://github.com/continuedev/continue

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: continue-ai-code-review

name: continue-ai-code-review description: > Continue - open-source AI-powered code review platform running agents on every PR as GitHub status checks. Review rules stored as markdown in .continue/checks/ directories (source-controlled). Returns green pass or red with suggested diffs. VS Code extension available. CLI via npm @continuedev/cli (cn command). macOS/Linux/Windows install. Custom checks for security, best practices, team conventions. USE FOR:

• AI code review on every pull request
• source-controlled review rules markdown
• GitHub status checks from AI agents
• custom security and best practice checks
• automated PR quality enforcement tags: [Continue, AI-code-review, PR, GitHub-status-checks, CI, VS-Code, open-source] kind: tool category: ai-agent-builder

---

What Is Continue?

Open-source AI code review platform — agents run on every PR as GitHub status checks.

• Repo: https://github.com/continuedev/continue
• CLI command: cn

Install

# macOS/Linux
curl -fsSL https://install.continue.dev | bash

# Windows (PowerShell)
iwr https://install.continue.dev/windows -useb | iex

# Node.js alternative (Node 20+)
npm install -g @continuedev/cli

How It Works

1. Store review rules as markdown in .continue/checks/
2. Continue runs agents on every PR
3. Returns GitHub status check: green (pass) or red (with suggested diffs)
4. Teams enforce rules as required status checks

Review Rule Structure

.continue/
  checks/
    security.md       # security vulnerability rules
    best-practices.md # team coding standards
    custom-check.md   # any custom requirement

VS Code Extension

Available through VS Code marketplace — provides IDE-level AI assistance integrated with the same agent framework.

---

KNOWLEDGE INJECTION: memU (NevaMind-AI)

Source: https://github.com/NevaMind-AI/memU

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: memu-proactive-memory

name: memu-proactive-memory description: > memU - 24/7 always-on proactive memory framework for AI agents. Continuously captures user intent without explicit commands. Hierarchical 3-layer memory (Resource/Item/Category). Dual pathways: memorize() continuous learning + retrieve() RAG fast or LLM deep reasoning. 92.09% accuracy on benchmarks. Reduces LLM token costs via cached insights. Storage: in-memory or PostgreSQL (pgvector). pip install -e . Python 3.13+. USE FOR:

• 24/7 always-on agent memory
• proactive user intent capture
• hierarchical memory for AI agents
• reduce LLM token costs via memory cache
• RAG-based fast context retrieval
• persistent agent memory PostgreSQL pgvector tags: [memU, proactive-memory, AI-ai-agents, hierarchical-memory, RAG, pgvector, token-reduction, 24-7] kind: framework category: ai-agent-builder

---

What Is memU?

24/7 proactive memory framework — AI agents learn continuously without explicit prompts.

• Repo: https://github.com/NevaMind-AI/memU
• Cloud: memu.so
• Python 3.13+ | OpenAI API key required

Install

pip install -e .
export OPENAI_API_KEY=your_api_key

Architecture

3-Layer Hierarchical Memory:
  Resource   → mountable data sources (docs, sessions, external feeds)
  Item       → individual memory facts with cross-references
  Category   → auto-organized topic clusters

Dual Retrieval Pathways:
  memorize() → continuous learning pipeline (immediate memory update)
  retrieve() → RAG fast (sub-second embedding) OR LLM deep (complex anticipation)

Storage Options

# In-memory (default)
python tests/test_inmemory.py

# PostgreSQL with pgvector (persistent)
docker run -d --name memu-postgres \
  -e POSTGRES_PASSWORD=postgres \
  -p 5432:5432 pgvector/pgvector:pg16
python tests/test_postgres.py

Examples

python examples/example_1_conversation_memory.py  # session persistence
python examples/example_2_skill_extraction.py     # learn user skills
python examples/example_3_multimodal_memory.py    # images + text

Performance

• 92.09% average accuracy on benchmark reasoning tasks
• Sub-second RAG retrieval
• Reduces redundant LLM calls via cached intent insights

---

KNOWLEDGE INJECTION: Skills Manager (jiweiyeah)

Source: https://github.com/jiweiyeah/Skills-Manager

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: skills-manager-desktop

name: skills-manager-desktop description: > Skills Manager - desktop app (Tauri 2.0 + React 19) for managing AI skills across multiple coding assistants. Write a skill once, sync via symlinks to Claude Code, Codex, Opencode simultaneously. Granular enable/disable per tool. Cross-platform (macOS/Windows/Linux). Monaco Editor for in-app skill editing. USE FOR:

• manage Claude Code skills across multiple AI tools
• sync skills via symlinks no duplication
• enable/disable skills per tool
• visual skill editor desktop app
• cross-tool skill management tags: [skills-manager, Claude-Code, Codex, skills-sync, symlinks, Tauri, desktop, cross-platform] kind: tool category: ai-agent-builder

---

What Is Skills Manager?

Centralized desktop app to manage AI assistant skills across Claude Code, Codex, Opencode, etc.

• Repo: https://github.com/jiweiyeah/Skills-Manager
• Stack: Tauri 2.0 (Rust) + React 19 + TypeScript + Tailwind CSS v4 + Radix UI + Monaco Editor
• Install: download from Releases (.dmg / .msi / .exe / .deb / .AppImage / .rpm)

Key Features

• Unified hub: one place to write, edit, and organize skills
• Smart sync: symlinks prevent file duplication across tools
• Granular control: enable/disable skills per AI tool independently
• In-app editor: Monaco Editor for rich code/markdown editing
• Auto-detect: finds installed AI tools and their skill directories automatically

Supported Tools

• Claude Code: ~/.claude/skills/
• Codex CLI: ~/.codex/skills/
• Opencode: ~/.opencode/skills/
• Custom tools: configurable paths

Windows Note

Requires Administrator privileges or Developer Mode enabled for symlink creation.

---

KNOWLEDGE INJECTION: Claude Code Karma

Source: https://github.com/JayantDevkar/claude-code-karma

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: claude-code-karma

name: claude-code-karma description: > Claude Code Karma - local-first analytics dashboard for Claude Code sessions. Reads ~/.claude/ JSONL files, serves via FastAPI (port 8000), displays in SvelteKit UI (port 5173). No cloud, no accounts, no telemetry. Shows token usage, costs, cache hit rates, tool/agent distribution, file operations, plugin/skill/hook inventory, live session monitoring. USE FOR:

• visualize Claude Code session analytics
• token usage and cost tracking
• cache hit rate analysis
• tool and agent usage statistics
• file operation monitoring
• plugin/skill/hook inventory dashboard tags: [claude-code-karma, analytics, dashboard, token-usage, costs, sessions, FastAPI, SvelteKit] kind: tool category: ai-agent-builder

---

What Is Claude Code Karma?

Local analytics dashboard for your Claude Code sessions.

• Repo: https://github.com/JayantDevkar/claude-code-karma
• Architecture: ~/.claude/ JSONL → FastAPI (8000) → SvelteKit (5173) → SQLite index
• No external calls — completely local

Install

git clone https://github.com/JayantDevkar/claude-code-karma.git
cd claude-code-karma

# Terminal 1 — backend
cd api && pip install -e ".[dev]" && pip install -r requirements.txt
uvicorn main:app --reload --port 8000

# Terminal 2 — frontend
cd frontend && npm install && npm run dev
# Access: http://localhost:5173

Dashboard Features

View	What It Shows
Session Browser	All sessions with search/filter, real-time status
Analytics	Token usage, costs, cache hit rates, tool distribution
Project Organization	Workspaces by git repo + activity tracking
Tool & Agent Tracking	Built-in + MCP tools + custom agents + usage stats
File & Task Management	File operations, task creation/completion
Live Monitoring	Real-time via Claude Code hooks (optional)
Plugin Ecosystem	Installed plugins, skills, commands, hooks

---

KNOWLEDGE INJECTION: Swing Skills (whynowlab)

Source: https://github.com/whynowlab/swing-skills

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: swing-trade-psychology-coach-firewall

name: swing-trade-psychology-coach-firewall description: > Swing - 6-skill AI trade-psychology-coach firewall suite. Prevents systematic AI reasoning failures: ambiguous execution, unverified claims, anchoring, sycophancy, hidden reasoning, optimism bias. Skills: swing-clarify (5W1H), swing-research (4-stage verification), swing-options (5 alternatives), swing-review (steel-man + 3-vector critique), swing-trace (assumption mapping), swing-mortem (5-category failure projection). npx skills add whynowlab/swing-skills --all. USE FOR:

• prevent AI hallucination and overconfidence
• 5W1H request decomposition before execution
• source-tiered research with cross-validation
• generate 5 probability-weighted alternatives
• steel-man critique of decisions
• assumption mapping and confidence analysis
• pre-mortem failure projection tags: [swing, trade-psychology-coach-firewall, clarify, research, options, review, trace, mortem, reasoning, Claude-Code] kind: skills-suite category: ai-agent-builder

---

What Is Swing?

6-skill trade-psychology-coach firewall that addresses systematic AI reasoning failures.

• Repo: https://github.com/whynowlab/swing-skills
• Compatible: Claude Code (primary), Cursor, GitHub Copilot, Codex CLI

Install

npx skills add whynowlab/swing-skills --all   # all 6 skills

# Individual:
npx skills add whynowlab/swing-skills/swing-clarify
npx skills add whynowlab/swing-skills/swing-research

# Manual:
cp -r swing-skills/skills/* ~/.claude/skills/

The 6 Skills

Skill	Problem Solved	Method
swing-clarify	Ambiguous requests rushed	5W1H decomposition
swing-research	Unverified claims	4-stage verification, S/A/B/C source grading
swing-options	Anchoring on obvious answer	5 probability-weighted alternatives
swing-review	Sycophancy / no critique	Steel-man then 3-vector critical analysis
swing-trace	Hidden reasoning	Assumption map, decision forks, weakest-link
swing-mortem	Optimism bias	5-category failure projection + leading indicators

Usage

/swing-clarify Build me an auth system
/swing-research Is gRPC better than REST for mobile?
/swing-review We chose Kubernetes for our 3-person startup
/swing-options Which database for real-time leaderboard?
/swing-trace Why do you recommend microservices?
/swing-mortem We are migrating to microservices in Q3

Recommended Chain

clarify -> (research decision) -> options -> research -> review
        -> (risk analysis)     -> mortem

---

KNOWLEDGE INJECTION: Spec-Flow (echoVic)

Source: https://github.com/echoVic/spec-flow

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: spec-flow

name: spec-flow description: > Spec-Flow - structured feature development workflow for AI coding agents. 5 sequential phases with approval gates: Proposal -> Requirements (EARS syntax) -> Design -> Tasks -> Implementation. Generates living markdown docs in .spec-flow/ directory. 3 modes: Step, Batch, Phase. Triggers: "spec-flow", "spec mode", "need a plan", "structured development". Install: git clone into ~/.claude/skills. USE FOR:

• structured feature development with AI
• requirements specification EARS syntax
• phase-gated AI coding workflow
• living documentation .spec-flow directory
• proposal -> requirements -> design -> tasks -> implementation tags: [spec-flow, structured-development, requirements, EARS, phases, AI-workflow, Claude-Code] kind: skill category: ai-agent-builder

---

What Is Spec-Flow?

Phase-gated structured development workflow for AI coding agents.

• Repo: https://github.com/echoVic/spec-flow
• Install: cd ~/.claude/skills && git clone https://github.com/echoVic/spec-flow.git
• Trigger: "spec-flow", "spec mode", "need a plan", or Chinese: "写个方案"

5 Phases (with human approval gates)

1. Proposal       → overview, scope, success criteria
2. Requirements   → EARS syntax specs (SHALL/WHEN/WHERE/IF clauses)
3. Design         → architecture, components, data models, interfaces
4. Tasks          → numbered implementation checklist, dependencies
5. Implementation → execute tasks one by one, verify each

Execution Modes

# Default (step-by-step, confirmation at each phase)
spec-flow: Build user authentication system

# Fast (skip confirmations)
spec-flow --fast: Add dark mode toggle

# Simple (skip design phase)
spec-flow --skip-design: Fix the login bug

Documentation Structure

.spec-flow/
  steering/     # project context, conventions
  active/       # in-progress feature docs
    feature-name/
      proposal.md
      requirements.md
      design.md
      tasks.md
  archive/      # completed features

EARS Requirements Format

WHEN user submits login form
IF credentials are valid
THE SYSTEM SHALL authenticate the user and redirect to dashboard

WHERE the user is not authenticated
THE SYSTEM SHALL redirect to login page

---

KNOWLEDGE INJECTION: HAM — Hierarchical Agent Memory

Source: https://github.com/kromahlusenii-ops/ham

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: ham-hierarchical-memory

name: ham-hierarchical-memory description: > HAM (Hierarchical Agent Memory) - reduces Claude Code token consumption 50% by replacing one massive CLAUDE.md with small scoped CLAUDE.md files at each directory level. Agent loads only 2-3 relevant files per session. Auto-detects project stack, self-maintaining (updates decision/pattern files as work progresses). Analytics dashboard at localhost:7777. Install: git clone into ~/.claude/skills/ham. USE FOR:

• reduce Claude Code token usage by 50%
• hierarchical scoped CLAUDE.md files
• context reduction hundreds vs thousands of tokens
• self-maintaining memory decision files
• ham dashboard analytics token savings
• benchmark baseline vs HAM performance tags: [HAM, hierarchical-memory, token-reduction, CLAUDE.md, context-management, Claude-Code, analytics] kind: skill category: ai-agent-builder

---

What Is HAM?

Hierarchical Agent Memory — cuts Claude Code starting context from thousands of tokens to hundreds.

• Repo: https://github.com/kromahlusenii-ops/ham
• Install: git clone https://github.com/kromahlusenii-ops/ham.git ~/.claude/skills/ham
• Token reduction: up to 50%

Core Concept

Before HAM:  One massive CLAUDE.md → loads entire project context every session
After HAM:   Small CLAUDE.md at each directory level → loads only 2-3 relevant files

Commands

# Setup
go ham              # auto-configures everything
ham update          # refresh memory files
ham status          # show current memory state
ham route           # show which files will load for current directory

# Analytics (web UI at localhost:7777)
ham dashboard       # open analytics dashboard
ham savings         # show token savings report
ham carbon          # environmental impact calculator

# Benchmarking
ham benchmark       # run benchmark vs baseline
ham baseline start  # start baseline measurement
ham baseline stop   # stop baseline, compute metrics

# Maintenance
ham audit           # check for stale/missing memory files
ham commands        # list all available commands

Memory Structure

project/
  CLAUDE.md              # global conventions, project overview
  src/
    CLAUDE.md            # frontend/backend patterns
    components/
      CLAUDE.md          # component-specific conventions
    api/
      CLAUDE.md          # API patterns, auth flows
  tests/
    CLAUDE.md            # testing patterns, fixtures

Self-Maintaining Cycle

Read:  agent reads directory CLAUDE.md before starting work
Write: agent updates CLAUDE.md after completing work
       (new patterns, decisions, gotchas discovered)

---

KNOWLEDGE INJECTION: Arize Phoenix

Source: https://github.com/Arize-ai/phoenix

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: arize-phoenix-observability

name: arize-phoenix-observability description: > Arize Phoenix - open-source AI observability platform. OpenTelemetry-based LLM tracing, LLM-powered evaluation (response + retrieval quality), version-controlled datasets, experiment tracking (prompts/models/retrieval). Integrates LangGraph, LlamaIndex, CrewAI, DSPy, Claude Agent SDK, OpenAI, Anthropic, Bedrock. Prompt playground + management. pip install arize-phoenix. Local/notebook/Docker/cloud. USE FOR:

• LLM application tracing and observability
• evaluate LLM response and retrieval quality
• version-controlled test datasets
• track prompt model retrieval experiments
• LangChain LangGraph LlamaIndex tracing
• Claude agent SDK observability tags: [Phoenix, Arize, observability, tracing, evaluation, LLM, OpenTelemetry, LangChain, LlamaIndex] kind: platform category: ai-agent-builder

---

What Is Arize Phoenix?

Open-source AI observability — trace, evaluate, and experiment on LLM applications.

• Repo: https://github.com/Arize-ai/phoenix
• Cloud: app.phoenix.arize.com
• Vendor and language agnostic

Install

pip install arize-phoenix

# Docker
docker pull arizephoenix/phoenix
docker run -p 6006:6006 arizephoenix/phoenix
# UI: http://localhost:6006

4 Core Capabilities

Capability	What It Does
Tracing	OpenTelemetry-based runtime tracing of LLM calls, chains, agents
Evaluation	LLM-powered benchmarks: response quality, retrieval accuracy
Datasets	Version-controlled example collections for testing + fine-tuning
Experiments	Track changes to prompts, models, retrieval — compare results

Quick Start

import phoenix as px
from openinference.instrumentation.langchain import LangChainInstrumentor

# Start local Phoenix
session = px.launch_app()

# Auto-instrument LangChain
LangChainInstrumentor().instrument()

# All LangChain calls now traced at http://localhost:6006

Supported Frameworks

LangGraph · LlamaIndex · CrewAI · DSPy · Claude Agent SDK OpenAI · Anthropic · Google GenAI · AWS Bedrock

Prompt Management

• Version-controlled prompt templates
• Tagging and rollback
• A/B comparison in playground
• Model comparison side-by-side

---

KNOWLEDGE INJECTION: TensorZero

Source: https://github.com/tensorzero/tensorzero

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: tensorzero-llm-gateway

name: tensorzero-llm-gateway description: > TensorZero - open-source LLM gateway + optimization platform. Sub-millisecond routing to 18+ providers (Anthropic, OpenAI, Google, AWS, etc.). Observability (PostgreSQL storage + OpenTelemetry), optimization (fine-tuning, GEPA prompt engineering, dynamic in-context learning), evaluation (heuristic + LLM judge), A/B testing + adaptive routing + fallbacks. Docker deploy. OpenAI-compatible API. Fortune 50 production use. USE FOR:

• unified LLM gateway 18+ providers
• LLM observability and feedback collection
• prompt optimization and fine-tuning
• A/B testing LLM models
• adaptive routing fallbacks retries
• evaluate LLM outputs with judges tags: [TensorZero, LLM-gateway, optimization, observability, fine-tuning, A/B-testing, OpenAI-compatible] kind: platform category: ai-agent-builder

---

What Is TensorZero?

Production-grade LLM gateway with optimization, observability, evaluation, and experimentation.

• Repo: https://github.com/tensorzero/tensorzero
• Deploy: Docker
• API: OpenAI-compatible (drop-in replacement)

Quick Start

from openai import OpenAI

client = OpenAI(base_url="http://localhost:3000/openai/v1")
response = client.chat.completions.create(
    model="tensorzero::my_function::anthropic::claude-sonnet-4-6",
    messages=[{"role": "user", "content": "Hello!"}]
)

Core Capabilities

Feature	Details
Gateway	18+ providers, sub-ms overhead, tool use, structured outputs, multimodal, caching
Observability	Stores inferences + feedback in PostgreSQL, OpenTelemetry, Prometheus
Optimization	Fine-tuning, GEPA automated prompt engineering, dynamic in-context learning
Evaluation	Heuristic benchmarks + LLM-as-judge for individual inferences
Experimentation	Built-in A/B testing, adaptive routing, fallbacks, retries

Supported Providers

Anthropic · OpenAI · Google Vertex/Gemini · AWS Bedrock/SageMaker · Azure DeepSeek · Groq · Mistral · Together AI · vLLM · OpenAI-compatible APIs

---

KNOWLEDGE INJECTION: PentAGI (vxcontrol)

Source: https://github.com/vxcontrol/pentagi

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: pentagi-security-agents

name: pentagi-security-agents description: > PentAGI - autonomous AI penetration testing platform. Multi-agent architecture: Orchestrator + Researcher + Developer + Executor + Searcher/Enricher/Memorist/Reporter. 20+ built-in security tools (nmap, metasploit, sqlmap). Long-term vector memory (PostgreSQL). Knowledge graph (Neo4j). Sandboxed Docker execution. 10+ LLM providers. REST+GraphQL API. Grafana/Prometheus monitoring. For authorized security testing and research only. USE FOR:

• authorized automated penetration testing
• multi-agent security research platform
• vulnerability assessment automation
• nmap metasploit sqlmap integration
• AI-driven security tool orchestration tags: [PentAGI, penetration-testing, security, multi-agent, nmap, metasploit, Docker, authorized-testing] kind: platform category: ai-agent-builder

---

What Is PentAGI?

For authorized security testing only. Autonomous AI penetration testing platform.

• Repo: https://github.com/vxcontrol/pentagi
• Deploy: Docker Compose

Agent Architecture

Orchestrator
  Researcher    → target analysis, intelligence gathering
  Developer     → attack strategy planning
  Executor      → implements attack plan
  Searcher      → information retrieval
  Enricher      → context enrichment
  Memorist      → long-term memory management
  Reporter      → results documentation
  Adviser       → strategy recommendations
  Reflector     → execution review
  Planner       → task decomposition (3-7 steps)

Built-in Security Tools (20+)

nmap · metasploit · sqlmap · + 17 others

Memory & Knowledge

• Long-term memory: vector embeddings in PostgreSQL
• Knowledge graph: Neo4j for semantic relationships
• Mentor supervision: detects repetitive patterns, suggests alternatives

Supported LLM Providers

OpenAI · Anthropic · Google AI · AWS Bedrock · Ollama · + 5 others

Limits & Safety

• Tool call limits: 100 (general agents), 20 (limited agents)
• Sandboxed Docker execution (complete isolation)
• Execution monitoring with automatic mentor intervention

---

KNOWLEDGE INJECTION: Vercel AI SDK

Source: https://github.com/vercel/ai

Routed to: claude-ai-tools.md

Date: 2026-03-18

SKILL: vercel-ai-sdk

name: vercel-ai-sdk description: > Vercel AI SDK - TypeScript toolkit for building AI apps with React/Next.js/Vue/Svelte/Node.js. Unified API across 20+ providers (OpenAI, Anthropic, Google, Groq, Mistral, DeepSeek, etc.). Core: generateText, streamText, generateObject, streamObject, tool calling, embeddings. UI hooks: useChat, useCompletion. npm install ai. Two libraries: AI SDK Core + AI SDK UI. USE FOR:

• build AI app with Next.js React
• unified LLM API swap providers easily
• streaming text and structured objects
• tool calling function integration
• useChat hook chat UI React
• generateObject typed JSON from LLM tags: [Vercel-AI-SDK, TypeScript, Next.js, React, streaming, generateText, useChat, tool-calling, OpenAI, Anthropic] kind: framework category: ai-agent-builder

---

What Is the Vercel AI SDK?

TypeScript toolkit for building AI apps — unified API across 20+ LLM providers.

• Repo: https://github.com/vercel/ai
• Docs: https://ai-sdk.dev/docs
• Works with: React, Next.js, Vue, Svelte, Node.js

Install

npm install ai
npm install @ai-sdk/openai      # OpenAI provider
npm install @ai-sdk/anthropic   # Anthropic/Claude
npm install @ai-sdk/google      # Google Gemini

AI SDK Core

Text Generation:

import { generateText, streamText } from "ai";
import { anthropic } from "@ai-sdk/anthropic";

// Single response
const { text } = await generateText({
  model: anthropic("claude-sonnet-4-6"),
  prompt: "Explain quantum computing",
});

// Streaming
const result = streamText({
  model: anthropic("claude-sonnet-4-6"),
  prompt: "Write a short story",
});
for await (const chunk of result.textStream) {
  process.stdout.write(chunk);
}

Structured Output:

import { generateObject } from "ai";
import { z } from "zod";

const { object } = await generateObject({
  model: openai("gpt-4o"),
  schema: z.object({
    name: z.string(),
    age: z.number(),
    skills: z.array(z.string()),
  }),
  prompt: "Generate a fictional developer profile",
});

Tool Calling:

import { tool } from "ai";

const result = await generateText({
  model: openai("gpt-4o"),
  tools: {
    getWeather: tool({
      description: "Get weather for a city",
      parameters: z.object({ city: z.string() }),
      execute: async ({ city }) => fetchWeather(city),
    }),
  },
  prompt: "What is the weather in Paris?",
});

AI SDK UI (React Hooks)

useChat:

import { useChat } from "ai/react";

export default function ChatPage() {
  const { messages, input, handleInputChange, handleSubmit } = useChat({
    api: "/api/chat",
  });
  return (
    <div>
      {messages.map(m => <div key={m.id}>{m.role}: {m.content}</div>)}
      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
        <button type="submit">Send</button>
      </form>
    </div>
  );
}

Supported Providers (20+)

OpenAI · Anthropic · Google · Azure · Amazon Bedrock · Groq · Mistral Cohere · DeepSeek · xAI Grok · Together.ai · Fireworks · Perplexity · + more

Core Functions Reference

Function	Purpose
generateText	Single text response
streamText	Streaming text + tool calls
generateObject	Typed JSON (Zod schema)
streamObject	Streaming structured data
embed	Generate embeddings
embedMany	Batch embeddings
useChat	React hook for chat UI
useCompletion	React hook for text completion
useObject	React hook for streaming objects