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A zero-dependency skill memory, testing, and refinement engine for AI agents.

Project description

Agent Skill System

Structured, self-evolving skill memory for AI agents — with built-in regression tests.

Python License Zero Deps

中文文档


AI agents forget. Teach one to review contracts, and tomorrow it makes the same mistakes — no memory across sessions, no way to catch degradation.

agent-skill-system turns corrected behavior into versioned, testable skill packages. Each skill bundles instructions, accumulated experience, and regression tests into a standalone directory. Next time, the engine matches the task type, loads context, and avoids repeating past errors.

vs Addy Osmani's agent-skills

agent-skills (63K stars) is a catalog of human-written prompt templates for one-shot tasks.

This solves a different problem: persistent, self-evolving skill memory.

  • Self-evolving.memory.md auto-logs successes/failures so skills improve with use instead of going stale.
  • Regression tests — 8 assertion patterns verify no degradation.
  • Auto-repairrefiner.py diagnoses failures, patches SKILL.md, reruns tests (up to 3 rounds).
  • Portable — Each skill is a directory. cp -r to any agent.

Think agent-skills = recipe book. This = chef's notebook that learns.


Quick start

pip install agent-skill-system

agent-skill list                   # what skills are available?
agent-skill search "contract"      # find the right skill
agent-skill load contract-review   # print skill + memory context

With LLM backend (for creating/refining skills):

export OPENAI_API_KEY="sk-..."
export LLM_MODEL="gpt-4o"

agent-skill health contract-review  # run regression tests
agent-skill register my-new-skill   # register a new skill
agent-skill scan                    # auto-register skills added to skills/

Lifecycle: Create → Evaluate → Refine → Register → Use → Remember

Stage Engine What
Create creator.py Conversation trace → SKILL.md + test cases
Evaluate test_runner.py 8 assertion patterns verify the skill
Refine refiner.py Diagnose failures → patch → retry (max 3×)
Remember memory.py .memory.md accumulates successes/failures

Skill structure

skills/contract-review/
├── SKILL.md       # How to do the task correctly
├── .memory.md     # What went right/wrong — auto-accumulated
├── config.json    # Trigger keywords, version, metadata
└── tests/         # Regression tests (8 assertion types)

Each skill is a standalone directory — no framework lock-in.


vs other approaches

Prompt Eng RAG Cursor Rules This
Creates from experience
Independent memory
Automated tests
Self-healing on failure
Cross-agent portable Manual Tied to DB Tied to editor cp
Training needed None None None None

License

MIT

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