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Magnus Hedemark 738ec715e7 Add human-focused README.md to every skill and bundle directory

Each README is written for a human audience, explaining:
- What the skill does (not what format it follows)
- What benefit the user gets from installing it
- Quick setup and usage patterns
- When to load/trigger the skill
- What scripts, references, and templates it ships

data-scientist already had a README — left unchanged.

48 READMEs added across all skill and bundle directories.

2026-07-09 22:30:12 -04:00

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LangGraph — Stateful Multi-Agent Orchestration

Build multi-agent AI systems with LangGraph — the low-level orchestration framework for stateful, graph-based agent workflows. The foundation for agents in the LangChain ecosystem.

Why Install This Skill

When your agent loads this skill, it becomes a LangGraph architect who can:

Design graph topologies — nodes, edges, state schemas, reducers
Implement multi-agent patterns — supervisor, swarm, and hierarchical orchestration
Add persistence — checkpointers and stores for long-running agents
Handle production complexity — branching, cycles, parallel execution, human-in-the-loop
Evaluate agent performance — systematic eval methodology
Debug production failures — common failure modes and how to trace them

What You Get

Directory	Purpose
`SKILL.md`	Quick start, design principles, pattern selection guide
`scripts/`	Supervisor scaffold, swarm scaffold, eval generator
`templates/`	3 runnable template implementations
`references/`	8 reference files: architecture, each pattern in depth, evals, production failures, troubleshooting

Triggers

Load this when designing agent architectures that need cycles, conditional branching, parallel execution, or human-in-the-loop patterns.

Requirements

Python 3.8+ with langgraph, langchain, and langchain-openai packages.

1.4 KiB Raw Blame History