skillsaw Create Plugin

Create a skillsaw rule plugin: a Python package that adds lint rules to skillsaw. Once you build and publish to PyPI, anyone can pip install it and skillsaw picks up the rules automatically — no config required. Prefer this to per-repo custom-rules: files when sharing rules across repos.

Keep the terminology straight: a skillsaw plugin adds rules to the skillsaw linter itself. Never confuse it with the Claude Code plugins that skillsaw lints.

Review this reference material while you work:

• Read the plugin guide: https://skillsaw.org/plugins/
• Follow the rule-writing guide (lint tree, block types): https://skillsaw.org/custom-rules/ and https://skillsaw.org/lint-tree/
• Read the complete working example: examples/plugins/skillsaw-example-plugin/ in the skillsaw repo

Follow each step in order, and keep the user updated on progress at each stage.

Step 1: Gather requirements

Review the following with the user:

1. What should each rule check? Get concrete examples of content that should pass and content that should fail.
2. Rule IDs — use kebab-case, descriptive (e.g. no-todo-instructions). Run skillsaw list-rules and confirm no ID collides with a builtin rule or another installed plugin; skillsaw never loads colliding plugin rules.
3. Severity — set each rule to error (must fix), warning (should fix), or info (advisory).
4. Package name — follow the convention skillsaw-<name> on PyPI with module skillsaw_<name> (e.g. skillsaw-acme-rules / skillsaw_acme_rules).
5. Tunable settings — set anything a user could reasonably want to adjust (patterns, thresholds, allowlists) in config_schema; never hardcode it.

Step 2: Create the package layout

Create this layout:

skillsaw-<name>/
├── pyproject.toml
├── README.md
├── src/
│   └── skillsaw_<name>/
│       ├── __init__.py
│       └── rules.py
└── tests/
    ├── fixture/
    │   └── CLAUDE.md          # realistic fixture the rules run against
    └── test_rules.py

Write pyproject.toml — the entry point is what makes it a plugin:

[build-system]
requires = ["setuptools>=61"]
build-backend = "setuptools.build_meta"

[project]
name = "skillsaw-<name>"
version = "0.1.0"
description = "<one line: what the rules enforce>"
readme = "README.md"
requires-python = ">=3.9"
dependencies = ["skillsaw>=0.20.0"]

[project.entry-points."skillsaw.plugins"]
<name> = "skillsaw_<name>"

[tool.setuptools.packages.find]
where = ["src"]

Set the entry point name (left of =) as the plugin's short name — it appears in skillsaw plugins output and in plugins: {disable: [...]} config. Use it to point at the module that declares the rules.

Write src/skillsaw_<name>/__init__.py:

from .rules import MyFirstRule

SKILLSAW_RULES = [MyFirstRule]

Declare every rule class the plugin provides in SKILLSAW_RULES — the explicit list skillsaw looks for.

Step 3: Write the rules

Read references/rule-authoring.md in this skill's directory for the full rule template, the requirements every rule must meet (lint tree discovery, line numbers, config_schema, repo_types, default_enabled, YAML and markdown parsing rules), and the deterministic-autofix template with its scoping and idempotency requirements. Follow it exactly — the requirements match what skillsaw's own review process enforces.

Implement each rule as a subclass of skillsaw.Rule in src/skillsaw_<name>/rules.py: use context.lint_tree.find(NodeType) to find files, add violations via self.violation(message, block=block, line=i), and set tunable settings through config_schema.

Add optional capabilities

Read references/extensions.md when the plugin needs any of:

• A CLI subcommand — add a skillsaw-<name> console script, dispatched as skillsaw <name> [args...] (registered plugins only).
• A custom repo type — declare a detector in SKILLSAW_REPO_TYPES; detected types scope rules (string entries in repo_types) and pull the type's content_paths into content linting.
• Lint tree nodes — use SKILLSAW_TREE_CONTRIBUTORS to attach plugin-defined blocks (ContentBlock for prose, JsonConfigBlock for machine config), and let rules read them with lint_tree.find().

Step 4: Write tests

Write tests in tests/test_rules.py that cover at minimum:

1. Verify the rule fires on the fixture with the expected message and line number.
2. Check that clean content produces no violations.
3. Verify config_schema settings change behavior.
4. If the rule has autofix: check the fix removes only the flagged lines, re-checking fixed content finds nothing, and fixing twice is a no-op. Call invalidate_read_caches() (from skillsaw.rules.builtin.utils) — always call it before you re-check, since skillsaw caches file reads within a process.

Keep the fixture realistic — a CLAUDE.md that looks like a real project's, not a one-line stub. Follow the example plugin's tests for the full pattern.

Run the tests:

$ python -m venv .venv && .venv/bin/pip install -e . pytest
$ .venv/bin/pytest

Step 5: Verify the plugin end-to-end

Install the package with pip install -e . in the same environment as skillsaw:

1. Run skillsaw plugins — the plugin appears with its rules and no ERROR lines.
2. Run skillsaw lint <repo-with-violations> — the rule fires, and the violation's source is plugin:<name> in --format json output; verify it.
3. Run skillsaw explain <rule-id> — it shows the rule's description and config.
4. If it has autofix: run skillsaw fix --rule <rule-id> to apply it; a second run changes nothing.
5. Run skillsaw lint --no-plugins — the rule disappears, confirming it loads via the entry point and never some other path.

Step 6: Write the README

Write the plugin's README.md, documenting:

• Include what the rules enforce and why, with a violating and a passing example.
• Install instructions (pip install skillsaw-<name>).
• Include each rule's ID, default severity, and config_schema options with a .skillsaw.yaml snippet.
• Include how to disable: plugins: {disable: [<name>]} or per-rule enabled: false.

Step 7: Build and publish to PyPI

Ask the user whether to publish now; if yes, run:

$ pip install build twine
$ python -m build
$ twine upload dist/*

For a GitHub-hosted plugin, offer to set up trusted publishing with a release workflow instead of long-lived API tokens; always pin action SHAs.

After publishing, verify a fresh pip install from PyPI works in a clean environment:

$ pip install skillsaw-<name>
$ skillsaw plugins

Step 8: Wrap up

Report to the user:

• Package name, module, and entry point name
• Rules created (ID, severity, autofix support, config options)
• Test results
• Where it was published (or how to publish later)
• How users consume it: pip install skillsaw-<name> next to skillsaw, then rules run automatically; configure per rule ID in .skillsaw.yaml