claude-skill-forge 0.1.2

Turn a codebase, package, or doc into a valid Claude SKILL.md — offline, deterministic, and valid by construction. Optional Claude refinement. Zero runtime dependencies.

skill-forge

Point it at your code. Get a valid Claude skill. No API key required.

skill-forge turns a codebase, package, or doc into a well-formed Claude SKILL.md — and the skill it writes is valid by construction.

pip install claude-skill-forge       # the CLI it installs is `skill-forge`
skill-forge forge ./my-tool          # writes .claude/skills/my-tool/SKILL.md

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Why

Writing a good skill is fiddly: the frontmatter has to be exactly right, the name has to match its directory, and the description — the one field an agent actually reads to decide whether to load the skill — has to say when to trigger, inside a tight character budget. Get any of it wrong and the skill is silently undiscoverable.

Most "ask an LLM to write my SKILL.md" approaches are non-reproducible, need an API key, and still emit invalid frontmatter. skill-forge is different on two axes:

1. Offline & deterministic by default. It reads your source with static analysis — no code execution, no network, no key — and emits the skill. Same input → same output. The optional --llm flag only refines the prose; it never owns the structure.
2. Valid by construction. Every generated skill passes the built-in linter (the same rules a skill must satisfy to be discoverable). skill-forge refuses to write a skill that doesn't lint clean, so you never ship a broken one.

It's the forge half of a pair: forge generates, skillcheck checks. The linter is bundled here too (skill-forge lint) so the tool stands alone.

Install

The package is published on PyPI as claude-skill-forge; it installs a CLI named skill-forge (and the import package is skill_forge).

pip install claude-skill-forge               # core: zero runtime dependencies
pip install 'claude-skill-forge[anthropic]'  # adds the optional --llm refiner

Quickstart (30 seconds)

# From a Python/Node project, a package, or a single doc:
skill-forge forge ./my-tool                 # -> .claude/skills/my-tool/SKILL.md
skill-forge forge ./README.md               # generate from docs
skill-forge forge ./pkg --name my-skill     # override the skill name
skill-forge forge ./my-tool --stdout        # preview, don't write
skill-forge forge ./my-tool --llm           # sharpen the prose with Claude

# Validate any skill / folder of skills (the bundled checker):
skill-forge lint .claude/skills

# CI drift guard — fail if the skill no longer matches the code:
skill-forge check ./my-tool --name my-tool

What it extracts

Source	What it reads
Python	pyproject.toml / setup.cfg (name, description, keywords, [project.scripts]), __all__ and public defs/classes via ast, and argparse / click / typer subcommands — never importing or running your code
Node	package.json (name, description, keywords, bin), TS/JS detection, README
Docs	A markdown/rst/txt file: H1 title, first paragraph, section headings, fenced code blocks
Any directory	README + a language histogram of the file tree
A CLI tool	skill-forge forge --from-cli "mytool" captures and parses mytool --help

The result is a complete SKILL.md: trigger-oriented description, a ## When to use section, an overview, and ## Commands / ## API / ## Usage sections built from what was found.

Use it from Python

from skill_forge import forge, render_skill, write_skill

draft = forge("./my-tool")          # a validated SkillDraft
print(render_skill(draft))          # the SKILL.md text
write_skill(draft, ".claude/skills")

The --llm refiner (optional)

--llm sends the extracted signals (not your source) to Claude to sharpen the description's trigger phrasing and tighten the body. It is fail-safe: if the model is unavailable it tells you how to fix it, and if its output is anything but a valid improvement, skill-forge keeps the deterministic draft. You never get a worse skill than the offline path. Set ANTHROPIC_API_KEY and install the extra to use it.

CI: catch stale skills

skill-forge check regenerates the skill in memory and diffs it against the one on disk, exiting non-zero if they differ — so a skill that drifted from the code it describes fails the build:

- run: pip install claude-skill-forge
- run: skill-forge check ./my-tool --name my-tool

What this is not

• Not a replacement for judgment. Generated skills are a strong first draft. The body is assembled from your structure, not written from deep understanding — read it, trim it, and add the hard-won "do this, not that" guidance only you know.
• Not a runtime. It writes SKILL.md files; it does not execute skills.
• Not magic prose. The offline path is deterministic and a little formulaic by design. Reach for --llm when you want the description polished.
• It does not run your code. The only time it executes anything is the explicit --from-cli flag, which runs <cmd> --help with no shell and a timeout.

Configuration

Environment overrides (all optional):

Variable	Default	Purpose
SKILL_FORGE_OUTDIR	.claude/skills	Default output directory
SKILL_FORGE_VERSION	0.1.0	Version stamped on generated skills
SKILL_FORGE_MODEL_ID	claude-haiku-4-5	Model used by --llm
ANTHROPIC_API_KEY	—	Required for --llm

Development

pip install -e ".[dev]"
ruff check .
pytest -q

The design is pinned in SPEC.md — the single source of truth for the public API and behavior. See CONTRIBUTING.md before opening a PR.

License

MIT — see LICENSE.