agenthatch 0.9.14

Turn any SKILL.md into a runnable AI Agent

Turn any SKILL.md into a standalone, runnable AI Agent.

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The problem with SKILL.md

SKILL.md promised a lot. Write a markdown file, tell your agent what to do, and it works. In practice, anyone who has used more than three skills across Claude Code, Codex CLI, or OpenClaw knows the friction:

Pain point	What actually happens
No isolation	Skills leak into each other. A file-organizer skill and a git-ops skill share the same context window. The agent confuses instructions meant for one with the other.
Reference book, not operating manual	Agents treat SKILL.md as a loose suggestion, not a contract. Given a long skill, the model skim-reads it. It picks the parts that seem relevant and ignores the rest.
Token waste	Every SKILL.md lives in the system prompt. Add 5 skills at 3KB each and you just burned 15KB of context before the conversation even starts. On long-running tasks this compounds fast.
No validation	A typo in a tool name, a missing parameter, an ambiguous instruction. The agent won't catch any of it until runtime, and by then the conversation is 20 turns deep.
Scale decays	Skills work at 1–3. At 10+ they become unmanageable. No dependency graph, no conflict detection, no way to tell which skill overrides which.

The core issue isn't the format. It's that SKILL.md is prompt engineering, not software engineering. You're asking an LLM to interpret human prose at runtime, every time, with no compilation, no type checking, no contract.

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What agenthatch does

agenthatch treats a SKILL.md as source code — not a prompt. It compiles it through a deterministic pipeline into a standalone Python agent that you can import, ship, and run anywhere.

SKILL.md  →  Parse  →  6-Harness LLM Pipeline  →  Code Generation  →  Runnable Agent
   (input)   (Phase 1)    (Phase 2: AI inference)     (Phase 3: Jinja2)     (output)

The result is a self-contained Python package with its own pyproject.toml, a CLI entry point, typed tool definitions, MCP integration, and a runtime configuration. It's not a wrapper around your skill — it is the skill, compiled into code.

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Quick Start

# Install
pip install agenthatch

# Initialize with your LLM provider
agenthatch init

# Add a SKILL.md
agenthatch skills add ./my-skill/SKILL.md

# Hatch it into an agent
agenthatch hatch my-skill

# Run it
agenthatch run my-skill

Three steps from markdown to running agent. The hatched agent lives in your skillhouse and can be re-run anytime.

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SKILL.md vs agenthatch

	SKILL.md (raw)	agenthatch (hatched)
Execution	Interpreted at runtime by LLM	Compiled into standalone Python package
Isolation	All skills share one context window	Each agent has its own runtime, tools, and config
Validation	None. Typos and ambiguities caught at runtime.	Schema-validated AHSSPEC before code generation
Token cost	Full skill body in system prompt every turn	~150 bytes of runtime config
Tool definitions	Prose descriptions, LLM guesses how to call	Type-annotated Python functions with JSON Schema
MCP	Manual wiring per agent	Auto-detected, auto-configured
Determinism	LLM interprets differently each time	Same SKILL.md → same AHSSPEC structure (low-temp inference)
Multi-skill scaling	Degrades past 3–5 skills	Unlimited. Each agent is a separate process.
Debugging	Read the LLM's chain-of-thought and pray	Standard Python debugging, logging, tests

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Architecture

agenthatch runs a 3-phase pipeline with 6 AI harnesses working in parallel:

Phase 1: Deterministic Parse (no AI)

The SKILL.md is parsed for frontmatter, body, and directory files. No AI involved. A pure file-system operation. The output is a ContextPack with zero semantic transformation.

Phase 2: 6-Harness LLM Inference

Six specialized AI harnesses process the skill, each with its own persona and temperature profile:

Harness	Role	Temp
A — Identity	Extract name, version, description from frontmatter	0.1
B — Intent	Infer trigger phrases and user intents	0.5
C — Interface	Design tool signatures, parameters, and return types	0.5
D — Base	Detect runtime base class and instruction structure	0.3
E — Assembly	Cross-validate all harness outputs, produce AHSSPEC	0.2
F — MCP	Detect and configure MCP server connections	0.3

Each harness runs an Analyze → Infer → Self-Validate → Correct loop with up to 2 internal retries. Harness E cross-validates the other five outputs and produces a unified AHSSPEC (Agent Hatch Standard Specification).

Phase 3: Code Generation

Jinja2 templates render the AHSSPEC into a complete Python agent package:

hatched-agent/
├── pyproject.toml          # pip-installable package
├── runtime.toml            # LLM provider, model, API keys
├── README.md               # Generated usage docs
├── agenthatch.yaml         # AHSSPEC manifest
└── src/{package_name}/
    ├── __init__.py
    ├── agent.py            # Agent class (extends AHCoreAgent)
    ├── tools.py            # Type-annotated tool implementations
    └── references.py       # AI-extracted structured data

Runtime: PlanLayer

Generated agents use the PlanLayer state machine — a 6-state planning engine that runs STARTING → PLANNING → EXECUTING → VERIFYING → REPLANNING → DONE. It adapts mid-task: merges completed steps, branches on failure, and degrades gracefully when tools time out.

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How it works under the hood

Click to expand: the full pipeline in detail

Step 1: agenthatch init

Sets up ~/.agenthatch/ with your LLM provider configuration. Supports OpenAI, DeepSeek, Anthropic, and any OpenAI-compatible endpoint. The config file is TOML. Readable, versionable, easy to share.

Step 2: agenthatch skills add <path>

Copies the SKILL.md and its directory into the skillhouse index. The skillhouse tracks every skill you've added, its hatch status, and where its generated agent lives.

Step 3: agenthatch hatch <name>

The full pipeline runs:

Phase 1 (deterministic): Parse SKILL.md → ContextPack
Phase 2 (AI): 6 harnesses → HarnessOutput → Assembly → AHSSPEC
Phase 3 (Jinja2): AHSSPEC → agent package

Flags:

• --no-generate — skip Phase 3, review the AHSSPEC first
• --force — overwrite existing hatched agent
• --dry-run — preview without writing files

Step 4: agenthatch run <name>

Launches the hatched agent in interactive TUI mode. The agent loads its runtime config, connects to its LLM provider, and starts a conversation loop with tool calling, context compaction, and PlanLayer-driven execution.

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CLI Reference

Command	What it does
agenthatch init	Initialize config and provider setup
agenthatch skills add <path>	Register a SKILL.md in the skillhouse
agenthatch skills list	List all registered skills
agenthatch skills delete <name>	Remove a skill from the skillhouse
agenthatch hatch <name>	Run the full pipeline (parse → harness → generate)
agenthatch run <name>	Launch a hatched agent in interactive TUI
agenthatch search <query>	Search the skillhouse index
agenthatch doctor	Diagnose environment and dependencies
agenthatch assemble	Re-assemble an existing skillhouse agent

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Installation

pip install agenthatch

Requires Python 3.11 or later.

For development:

git clone https://github.com/agenthatch/agenthatch.git
cd agenthatch
pip install -e ".[dev]"

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Documentation

Document	Link
Contributing Guide	CONTRIBUTING.md
Security Policy	SECURITY.md
Support	SUPPORT.md
Roadmap	ROADMAP.md
Code of Conduct	CODE_OF_CONDUCT.md
Changelog	CHANGELOG.md

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Community

• GitHub Discussions — questions, ideas, roadmap
• GitHub Issues — bugs and feature requests
• Discord
• X (Twitter)

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Contributing

agenthatch is a solo project looking for its first contributors. Issues, pull requests, documentation, design -- every bit moves the project forward.

See CONTRIBUTING.md for dev setup, the quality gate (hatch run quality:check), and PR guidelines.

AI-assisted contributions are welcome. Run the quality gate before submitting — that's all that matters.

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FAQ

Who is this for?

Anyone who maintains more than 3 SKILL.md files and feels the friction. Claude Code users, Codex CLI users, OpenClaw users — if you've ever thought "I wish this skill was a real program," this is for you.

Can I use this with Claude Code / Codex / OpenClaw?

Yes. The hatched agent is a standalone Python package. You can run it as a CLI, import it as a library, or wrap it as an MCP server. It doesn't depend on any specific agent platform.

What MCP servers are supported?

Any MCP server that speaks the standard protocol. Harness F auto-detects MCP servers referenced in your SKILL.md and configures them in the generated agent's runtime.

Does this replace SKILL.md?

No. SKILL.md is the input format. agenthatch is the compiler. You still write skills in markdown — agenthatch turns them into agents.

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License

MIT — see LICENSE for details.

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_{📖 简体中文版请见 README_CN.md}