Use when modifying agentic contextual building blocks like skills (SKILL.md), slash commands, agents, hooks, plugins, marketplaces, and instruction files (CLAUDE.md, AGENTS.md, GEMINI.md, QWEN.md, Cursor, Copilot, Cline, or Kiro rules), tool configuration such as an OpenCode `opencode.json`, Muse Code hooks (`.muse/hooks.json`), a Grok Build `.grok/` project layer or a Google Antigravity customization root (`.agents/`, `.agent/`, `_agents/`, `_agent/` — hooks, MCP config, rules, registries and plugins), and committed project memory (`.agents/memory/`). Run skillsaw on the files you touched, apply autofixes, resolve remaining violations with `skillsaw explain` guidance, and re-lint until clean before considering the work complete.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
You just authored or edited agentic context — a skill, slash command, agent,
hook, plugin, marketplace, or instruction file (CLAUDE.md, AGENTS.md,
GEMINI.md, Cursor/Copilot/Cline/Kiro rules), or the configuration that
loads it (an OpenCode opencode.json). That content feeds an agent's
context window, and defects in it — weak language, contradictions,
placeholder text, instructions buried in attention dead zones — degrade
every future session that loads it. Lint it with skillsaw and improve
it before you report the work as done.
Run the skillsaw CLI for every step below; it is the interface for linting, fixing, and explaining — replace any hand-rolled check with it.
Run skillsaw --version. If the command is missing, check the project for a
pinned version before installing — look in the Makefile, pyproject.toml, or
an existing .venv (e.g. .venv/bin/skillsaw --version). Use that version
with uvx skillsaw==<version> as the prefix for every command below (or
pip install skillsaw==<version> if uvx is unavailable).
Run the linter scoped to the files or directories you created or edited:
skillsaw lint <path>
Read each violation line: it carries the severity, file path, line number,
message, and rule ID. If the repository defines its own lint entry point (a
Makefile lint target that runs skillsaw, for example), run that instead —
it may pin a version or pass flags like --strict.
If the lint exits 0 with no violations, your work is clean — stop here and report done.
skillsaw fix <path>
This applies safe, structural fixes (missing frontmatter, kebab-case names,
and similar). Run skillsaw fix --dry-run first to inspect the diff before
anything is written. Run skillsaw fix --suggest for a second tier of
mechanically derived fixes (stale-reference updates after a rename, for
example) — review each hunk in that tier before applying it.
For each rule ID still reported, load its guidance:
skillsaw explain <rule-id>
Read the rule's rationale and its "How to fix" section, then apply that
guidance with a targeted edit. Change only the text that triggers the
violation and keep the meaning you intended when you wrote it. Do not guess
a fix from the violation message alone; run skillsaw explain and follow
its guidance.
Re-run skillsaw lint <path> after your edits and repeat Steps 3–4 until it
exits 0. Then run skillsaw from the repository root to confirm your changes
introduced no violations elsewhere. The lint output ends with a letter grade
for the repository's agentic content — leave it the same or better than you
found it.
skillsaw-fix
skill, which runs a deeper inventory, fix, and verify loop across a whole
repository.No reviews yet. Tell others what you thought.
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Create a skillsaw rule plugin — a pip-installable Python package that adds custom lint rules to skillsaw. Walks through scaffolding the package, writing rules against the lint tree, testing, and publishing to PyPI. Use when a user wants to share skillsaw rules across repositories or publish them for others.
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Survey the AI coding assistant and agentic tool ecosystem, assess skillsaw's competitive position, identify emerging patterns and missing capabilities, and produce a prioritized strategic report as a GitHub issue. Use when evaluating ecosystem trends and product gaps.
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Fix skillsaw lint violations — apply deterministic autofixes, then resolve the remaining violations with targeted edits guided by `skillsaw explain`. Use when skillsaw reports violations, when asked to clean up lint findings, or after `skillsaw fix` leaves violations behind.
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Use when skillsaw reports a likely false positive, misses an edge case, behaves incorrectly, or needs an RFE. Gather a safe report and, only with explicit user permission, create a feedback bundle or open a GitHub issue.
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Pick up open GitHub issues from collaborators and create PRs to solve them. Use when triaging and resolving reported bugs or feature requests.
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Triage a GitHub issue filed against skillsaw — classify it (bug, feature, docs, question, other), verify its claims against the codebase, enrich it with missing details, and post one structured triage comment. Use when triaging and labeling incoming issues, not to fix them.
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