Update a repository to the newest skillsaw — upgrade the install, report new rules and their findings in your repo, and bump version pins (GitHub Action SHAs, Makefile targets, pre-commit hooks). Use when a new skillsaw release is out and you want its latest checks.
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Review the instructions the agent will receive before you install.
Update this repository from its installed skillsaw to the newest release: upgrade the local install, report which rules are new and what they find in this repository, and bump every pinned skillsaw version (GitHub Actions SHAs, Makefile targets, pre-commit hooks, container tags).
Ask one routing question at a time and wait for the answer. An explicit choice in the user's request already counts as an answer. Read a reference only after its condition or a yes answer routes to it; do not read the reference to formulate the question. After completing it, return here. If the answer is no, continue to the next checkpoint without reading it. Carry forward the command prefixes, versions, rule lists, and changed-file list.
Resolve every references/... path relative to the directory containing this
SKILL.md, never relative to the target repository or the process's current
working directory. If this file was fetched from the web, resolve each
reference against the parent URL of this file and fetch it from that sibling
location.
Replace brace-delimited fields below with facts from the repository or scan; never show placeholders to the user, and render singular or plural wording naturally.
Read versions. It records the installed and latest versions, captures the old rule list, upgrades the install (or selects the new command prefix), and verifies the result. Report both versions before offering changes.
Read new rules. It diffs the old and new rule lists, explains each added rule, and scans the repository with the new version so every new rule is presented with its actual findings here.
If the repository pins skillsaw anywhere (GitHub Actions workflows, Makefile targets, pre-commit hooks, container image tags), ask:
I found skillsaw pinned in {locations}. Bumping them to {latest} keeps CI and local runs on the version just installed. Should I update those pins?
If yes, read pins. If no, preserve the locations.
If the new version reports findings from added rules, read triage and present its summary table for confirmation before making changes. Carry the agreed buckets forward.
After all accepted routes finish, always read verification.
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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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