Research lab
Full-stack AI research and data science AI covering 20+ specialist skills across data science, machine learning, AI agent architecture, multi-agent orchestration, LLM engineering, RAG systems, voice AI, prompt engineering, and scientific research methodology. Use for data analysis, predictive modeling, statistical inference, A/B testing, autonomous agent design, ReAct loops, multi-agent coordination (CrewAI, LangGraph), agent memory systems, RAG pipelines, prompt optimization, voice agents, agent evaluation, and research engineering. Trigger keywords: data science, machine learning, ML model, statistical analysis, A/B test, predictive model, AI agent, autonomous agent, multi-agent, orchestration, CrewAI, LangGraph, RAG, retrieval-augmented generation, prompt engineering, LLM app, agent memory, voice agent, voice AI, research engineer, scientific research, data scientist, computer use agent, agent evaluation, brainstorming, ideation.From its SKILL.md
npx -y skills add BigY0shi/super-skills --skill research-labAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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- 2 stars2 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.
SKILL.md
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Research Lab
A complete, modular AI research and engineering suite — from rigorous data science and statistical modeling to cutting-edge AI agent architecture, multi-agent systems, LLM engineering, and voice AI.
How to Use This Skill
- Identify the task category from the routing table below
- Load the matching sub-skill from
references/skills-catalog.md - Execute following the sub-skill's workflow
Quick Routing Table
📊 Data Science & Research
| Task | Load |
|---|---|
| Data analysis, ML modeling, statistical inference, A/B testing, visualization | data-scientist |
| Rigorous academic research engineering — formal correctness, HPC, proofs | research-engineer |
| Scientific methodology, research design, literature synthesis | scientific-research |
| Brainstorming, ideation, structured creative thinking | brainstorming |
🤖 AI Agent Architecture
| Task | Load |
|---|---|
| Designing autonomous AI agents — tool use, memory, planning, guardrails | ai-agents-architect |
| Autonomous agent loops — ReAct, Plan-Execute, reflection, self-correction | autonomous-agents |
| Reusable agent design patterns — subagent coordination, handoffs | autonomous-agent-patterns |
| Computer use agents — GUI automation, screen interaction, browser agents | computer-use-agents |
🌐 Multi-Agent Systems
| Task | Load |
|---|---|
| Multi-agent orchestration — optimizing performance across agent networks | agent-orchestration |
| Dispatching and routing tasks to parallel agents | dispatching-parallel-agents |
| Subagent-driven development — building with specialized subagents | subagent-driven-dev |
| CrewAI — role-based crew design, task delegation, agent collaboration | crewai |
| LangGraph — stateful agent graphs, conditional edges, human-in-the-loop | langgraph |
🧠 Memory & State
| Task | Load |
|---|---|
| Agent memory systems — episodic, semantic, procedural, long-term persistence | agent-memory-systems |
| MCP-based memory integration for agents | agent-memory-mcp |
⚗️ LLM Engineering
| Task | Load |
|---|---|
| Prompt engineering patterns — few-shot, CoT, structured output, debugging | prompt-engineering |
| Prompt library management — versioning, evaluation, organization | prompt-library |
| Prompt caching strategies — cost reduction, latency optimization | prompt-caching |
| RAG pipelines — chunking, embedding, retrieval, reranking, evaluation | rag-engineer |
| LLM application patterns — streaming, tool use, error handling, evals | llm-app-patterns |
🎙️ Voice & Multimodal AI
| Task | Load |
|---|---|
| Voice agent design — STT/TTS pipelines, latency, interruption handling | voice-agents |
| Voice AI development — real-time audio, WebRTC, telephony integration | voice-ai-development |
🔬 Evaluation
| Task | Load |
|---|---|
| Agent evaluation — benchmarks, metrics, failure analysis, test harnesses | agent-evaluation |
Loading Sub-Skills
All sub-skill instructions are in references/skills-catalog.md.
research-lab/
├── SKILL.md ← You are here (routing hub)
└── references/
└── skills-catalog.md ← Full instructions for all skills
Always read the relevant section of skills-catalog.md before executing any task.
Skill Sorting Notes
Skills from this folder that route to other suites:
analytics-tracking→ founders-suite (Segment CDP / event tracking)architecture-decision-records→ devops-suitebullmq-specialist→ devops-suite (message queue infrastructure)email-sequence→ marketing-suiteemail-systems→ coding-suiteemployment-contract-templates→ founders-suite (HR workflows)on-call-handoff-patterns→ devops-suiteplaid-fintech→ coding-suite (API integration)kaizen→ founders-suite (process improvement framework)
Universal Research Lab Standards
Correctness before performance — A fast wrong answer is worse than a slow right one. Validate assumptions, cite sources, acknowledge uncertainty.
Reproducibility is non-negotiable — Every experiment, prompt, and pipeline must be reproducible. Version your prompts, seed your random generators, log your hyperparameters.
Agents earn autonomy — Start constrained. Add autonomy only when reliability is proven. A 95% step success rate = 60% success by step 10.
Evaluate everything — Build evals before features. If you can't measure it, you can't improve it. Every LLM pipeline needs a test harness.
Data integrity first — Garbage in, garbage out. Understand your data distribution, validate your splits, and document your preprocessing.
Complexity when necessary, not as default — The simplest model that solves the problem is the right model. Add complexity only when it demonstrably improves outcomes.