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Skillsaw Fix

Community
stbenjam
skillsaw-fix

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.

Overview

Publisherstbenjam
Repositoryskillsaw
Skill nameskillsaw-fix
Stars
69
Forks
15
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by stbenjam on GitHub. Read the source before you install it.

Installation

Install the Skillsaw Fix AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/stbenjam/skillsaw/tree/main/.agents/skills/skillsaw-fix
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/stbenjam/skillsaw.git /tmp/skillsaw
mkdir -p .claude/skills
cp -r /tmp/skillsaw/.agents/skills/skillsaw-fix .claude/skills/skillsaw-fix
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skillsaw Fix in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Skillsaw Fix on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Skillsaw Fix is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

skillsaw Fix

Review and fix skillsaw lint violations in this repo. skillsaw lints agentic contextual building blocks (CLAUDE.md, skills, plugins, agents, hooks, etc.). The tool applies deterministic fixes itself; handle everything else yourself — read the violation, load the rule's how-to-fix guidance, and make a targeted edit.

Follow the steps in order. Report progress to the user at each stage.

Step 1: Install and locate skillsaw

Run skillsaw --version. If the command is missing, use uvx skillsaw as the prefix for every command below (or pip install skillsaw if uvx is unavailable).

Step 2: Run the lint and build an inventory

Run skillsaw (lint) from the repo root and capture the output. Each violation line includes the severity, file path, line number, message, and rule ID — read them all.

If the lint exits 0 with no violations, tell the user the repo is clean and stop.

Build an inventory: group the violations by rule ID and check how many files each rule touches. Review this summary with the user before making changes.

Step 3: Run deterministic autofixes

Run skillsaw fix. This applies safe, structural fixes (missing frontmatter, kebab-case names, plugin registration, and similar).

Then run skillsaw fix --suggest --dry-run to preview the suggested tier. Suggested fixes (e.g. updating stale references after a rename) are mechanically derived but may over-match, so review each hunk in the diff individually: for a stale-reference rewrite, check whether the flagged text is really a reference to the skill or just the same word used generically — "the data-parser skill" is a reference; "our data parser" is not. If every hunk is correct, run skillsaw fix --suggest to apply the tier. If any hunk is wrong, skip the tier entirely, handle those violations manually in Step 5, and keep only the edits you judged correct.

Re-run skillsaw and report how many violations the autofixer resolved.

Step 4: Read fix guidance for each remaining rule

For every rule ID still in the inventory, run:

sh
skillsaw explain <rule-id>

Read the rule's rationale and its "How to fix" section from the output — this is the authoritative guidance for what a correct fix looks like. Read it before editing anything. Never guess at what a rule wants from its message alone.

Step 5: Review and fix the remaining violations

Handle the violations file by file. For each violation:

  1. Read the file and understand the context around the flagged line
  2. Follow the skillsaw explain guidance with a targeted edit — change only the text that triggers the violation, and keep the author's meaning, structure, and formatting
  3. Keep edits minimal — never rewrite a whole file to fix one line

After finishing a file, re-run skillsaw scoped to confirm the violations are gone and your edit introduced no new ones. Always re-lint even after an edit that looks obviously correct: wrap a path in a link, split a section, or add a directive, and you can trip a different rule you now have to fix.

Handle common cases

  • content-weak-language: remove hedges ("try to", "consider", "if possible") — use direct imperatives or explicit conditions instead.
  • content-tautological: delete empty truisms ("follow best practices"), or write the specific, actionable instruction the author meant instead.
  • content-negative-only: keep the prohibition, add what to do instead.
  • content-unlinked-internal-reference: wrap the path in [path](path) only when it resolves relative to the flagged file's own directory. Links resolve from the file, not the repo root — so never link a repo-root path mentioned in a doc nested under a subdirectory, since it does NOT resolve from there and a link to it trips content-broken-internal-reference (a warning, strictly worse than the info you started with). Keep those bare and suppress the rule instead (see "Suppressing violations you cannot fix in content"). Verify a path exists before you link it — never link stale paths that become broken links.
  • agentskill-rename-refs: single-word skill names are flagged but never rewritten automatically — the word is too ambiguous. Check each flagged line yourself: rewrite it only if the mention actually refers to the skill (/name invocations, skills/name/ paths, "the name skill"), and keep generic uses of the word unchanged.
  • Missing or invalid frontmatter fields: derive names from directory names, and write descriptions from the body content.
  • content-embedded-secrets: replace the secret with an environment variable placeholder, and always tell the user to rotate the credential — removing it from the file does not remove it from git history.

Handle violations you cannot fix in content

Some violations are correct signal but the flagged text is deliberate — a repo-root reference that must stay bare, a template that is intentionally terse, prose where a term is idiomatic. Keep the content and suppress the rule instead of distorting it. Which mechanism you use depends on whether the violation carries a line number.

Line-numbered violations — use an inline HTML comment directive to suppress them:

markdown
<!-- skillsaw-disable-next-line rule-id -->
the flagged line

<!-- skillsaw-disable rule-id -->
...a flagged region...
<!-- skillsaw-enable rule-id -->

Avoid three syntax traps that make directives silently misfire:

  • Keep the directive comment to only skillsaw-disable <rule-ids>. Never add trailing prose (skillsaw-disable rule-id — because ...) — it fails the match and the directive does nothing. Keep human rationale in a separate comment.
  • Use disable-next-line for exactly one following line. If the flagged path or phrase sits on the second line of a wrapped sentence, merge it onto the directive's target line or use a disable/enable block instead.
  • Never place a comment between list items or inside a paragraph — it splits the markdown into separate blocks. Keep block disable/enable markers outside the list or paragraph, or keep the flagged text on one line.

File-level violations (no line number) — some rules always score the whole file and never report a line: e.g. content-cognitive-chunks and content-inconsistent-terminology. Inline directives cannot suppress these — a <!-- skillsaw-disable --> comment has no effect on them. Use config in .skillsaw.yaml instead:

yaml
rules:
  content-cognitive-chunks:
    # Scoped per-rule exclude — keeps the rule enforced everywhere else
    exclude: ["**/references/**", "REVIEW.md"]
  content-inconsistent-terminology:
    # Disable only a group that legitimately does not apply
    groups:
      function/method: off

Prefer a scoped per-rule exclude over disabling the rule globally, so the rule keeps working where it belongs. Always escalate or baseline (Step 6) when even that is too broad, or the call is the maintainer's to make.

Step 6: Escalate decisions only the user can make

Some violations need decisions only the user can make — for example, which of two contradictory instructions is correct, or whether a flagged reference is intentional. Never invent an answer. List these violations with your recommendation, and ask the user.

If the user wants to accept some violations for now, run skillsaw baseline so only new violations fail future lints, and remind them to commit .skillsaw-baseline.json.

Step 7: Verify and summarize

Run the repo's own lint entry point if it defines one — check the Makefile (or equivalent task runner) for a skillsaw target such as lint or lint-fix and use that, since it may pin a version or pass flags like --strict. Otherwise run skillsaw directly.

Check whether the repo has a grade badge (.skillsaw-badge.json exists, or the lint target depends on a badge target), refresh it: run make badge if defined, else run skillsaw badge. The badge reflects committed state, so include the updated .skillsaw-badge.json in the files to commit.

Review and report:

  • Violations fixed automatically (Step 3)
  • Violations fixed by edits (Step 5)
  • Violations escalated or baselined (Step 6)
  • Files modified
  • Final exit status

If the lint still fails, always show the remaining violations and explain why each one is still open.

Frequently asked questions

What does the Skillsaw Fix AI skill do?

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.

Why use Skillsaw Fix on TypingMind?

Because you install it once and use it with any model. Skillsaw Fix is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Skillsaw Fix in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/stbenjam/skillsaw/tree/main/.agents/skills/skillsaw-fix. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Skillsaw Fix?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Skillsaw Fix?

As many as you like. As long as a model supports skills, you can use Skillsaw Fix with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Skillsaw Fix AI skill free?

Yes. It is published on GitHub by stbenjam under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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