agentsclimarketplace

Ai agent development

Skill pinkpixel-dev/skills-collection-1/SKILLS/ai-agent-development

Part 1 of a large AI and agent skills collection featuring 900+ reusable skill folders, prompt workflows, references, scripts, and assets across engineering, cloud, security, research, writing, design, and automation.

Install
npx -y skills add pinkpixel-dev/skills-collection-1 --skill ai-agent-development

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 7 stars7 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

SKILL.md

3.7 KB, 841 tokens by cl100k_base, as published. Nobody here has run it

AI Agent Development Workflow

Overview

Specialized workflow for building AI agents including single autonomous agents, multi-agent systems, agent orchestration, tool integration, and human-in-the-loop patterns.

When to Use This Workflow

Use this workflow when:

  • Building autonomous AI agents
  • Creating multi-agent systems
  • Implementing agent orchestration
  • Adding tool integration to agents
  • Setting up agent memory

Workflow Phases

Phase 1: Agent Design

Skills to Invoke

  • ai-agents-architect - Agent architecture
  • autonomous-agents - Autonomous patterns

Actions

  1. Define agent purpose
  2. Design agent capabilities
  3. Plan tool integration
  4. Design memory system
  5. Define success metrics

Copy-Paste Prompts

Use @ai-agents-architect to design AI agent architecture

Phase 2: Single Agent Implementation

Skills to Invoke

  • autonomous-agent-patterns - Agent patterns
  • autonomous-agents - Autonomous agents

Actions

  1. Choose agent framework
  2. Implement agent logic
  3. Add tool integration
  4. Configure memory
  5. Test agent behavior

Copy-Paste Prompts

Use @autonomous-agent-patterns to implement single agent

Phase 3: Multi-Agent System

Skills to Invoke

  • crewai - CrewAI framework
  • multi-agent-patterns - Multi-agent patterns

Actions

  1. Define agent roles
  2. Set up agent communication
  3. Configure orchestration
  4. Implement task delegation
  5. Test coordination

Copy-Paste Prompts

Use @crewai to build multi-agent system with roles

Phase 4: Agent Orchestration

Skills to Invoke

  • langgraph - LangGraph orchestration
  • workflow-orchestration-patterns - Orchestration

Actions

  1. Design workflow graph
  2. Implement state management
  3. Add conditional branches
  4. Configure persistence
  5. Test workflows

Copy-Paste Prompts

Use @langgraph to create stateful agent workflows

Phase 5: Tool Integration

Skills to Invoke

  • agent-tool-builder - Tool building
  • tool-design - Tool design

Actions

  1. Identify tool needs
  2. Design tool interfaces
  3. Implement tools
  4. Add error handling
  5. Test tool usage

Copy-Paste Prompts

Use @agent-tool-builder to create agent tools

Phase 6: Memory Systems

Skills to Invoke

  • agent-memory-systems - Memory architecture
  • conversation-memory - Conversation memory

Actions

  1. Design memory structure
  2. Implement short-term memory
  3. Set up long-term memory
  4. Add entity memory
  5. Test memory retrieval

Copy-Paste Prompts

Use @agent-memory-systems to implement agent memory

Phase 7: Evaluation

Skills to Invoke

  • agent-evaluation - Agent evaluation
  • evaluation - AI evaluation

Actions

  1. Define evaluation criteria
  2. Create test scenarios
  3. Measure agent performance
  4. Test edge cases
  5. Iterate improvements

Copy-Paste Prompts

Use @agent-evaluation to evaluate agent performance

Agent Architecture

User Input -> Planner -> Agent -> Tools -> Memory -> Response
              |          |        |        |
         Decompose   LLM Core  Actions  Short/Long-term

Quality Gates

  • Agent logic working
  • Tools integrated
  • Memory functional
  • Orchestration tested
  • Evaluation passing

Related Workflow Bundles

  • ai-ml - AI/ML development
  • rag-implementation - RAG systems
  • workflow-automation - Workflow patterns

Gives 0 of the 12 instructions most agent orchestration skills give in 841 tokens

Counted across 742 of the 995 authors here whose files we hold, read 2026-08-06

  • run the full test suite after integrating changesin 53 of 742, across 20 files
  • reference existing artifacts by path or URLin 52 of 742, across 22 files
  • dispatch one agent per independent problem domainin 50 of 742, across 17 files
  • verify fixes do not conflictin 45 of 742, across 13 files
  • include a suggested skills section in the documentin 45 of 742, across 15 files
  • redact sensitive informationin 41 of 742, across 11 files
  • save to the temporary directory of the operating systemin 39 of 742, across 9 files
  • tailor the document to user-provided focus argumentsin 39 of 742, across 9 files
  • spot check agent changes for systematic errorsin 34 of 742, across 7 files
  • write a handoff document summarising the current conversationin 31 of 742, across 6 files
  • assign each agent a specific scopein 23 of 742, across 8 files
  • provide specific scope and clear goalin 23 of 742, across 5 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.