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Skillsaw Release

Community
stbenjam
skillsaw-release

Bump the version, generate release notes, and publish a new skillsaw release to PyPI. Use when cutting a new release.

Overview

Publisherstbenjam
Repositoryskillsaw
Skill nameskillsaw-release
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 Release 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-release
  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-release .claude/skills/skillsaw-release
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skillsaw Release 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 Release 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 Release 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 Release

Follow these steps to release a new version of the skillsaw linter.

Step 1: Run pre-flight checks

Before releasing, verify:

  1. Check you are on the main branch, it is clean (git status), and up to date with origin/main (git pull origin main).

  2. Verify the current main commit has a successful push run of the Tests workflow. Do not accept the reduced PR test run: pull requests run Python 3.9 plus the 3.14 coverage job, while a main push must have passed Python 3.9, 3.10, 3.11, 3.12, 3.13, and 3.14. The workflow run must test the exact HEAD being released:

    bash
    main_sha=$(git rev-parse HEAD)
    run_id=$(gh run list --workflow test.yml --branch main --commit "$main_sha" \
      --event push --status success --limit 1 --json databaseId --jq '.[0].databaseId')
    test -n "$run_id" && test "$run_id" != "null" || {
      echo "No successful Tests workflow run exists for main at $main_sha" >&2
      exit 1
    }
    gh run view "$run_id" --json jobs | jq -e '
      [.jobs[] | select(.name | test("^test \\(3\\.(9|10|11|12|13|14)\\)$"))]
      | length == 6 and all(.[]; .conclusion == "success")
    ' >/dev/null || {
      echo "The full Python 3.9–3.14 main test matrix did not pass" >&2
      exit 1
    }
  3. All local tests pass: make test.

  4. Formatting is clean: make lint.

  5. Determine which version to release. If none was specified, review the commits since the last tag. Add a minor bump for new features or rules; keep a patch bump for fixes only (the bump script defaults to patch when given no argument).

The Makefile creates the .venv and installs dependencies automatically; run make venv first if needed.

Step 2: Run the bump script

Run the bump script:

bash
.venv/bin/python -c "pass" || make venv
bash scripts/bump-version.sh [version]

Verify the script updated pyproject.toml, src/skillsaw/__init__.py, and the documented install pins. The action installs its checkout by default; its empty version input stays unchanged during a release. If you pass no version argument, the script increments the patch version automatically.

Then regenerate generated files, refresh the human contributor list from GitHub, and check for pinned version references the script does NOT update:

bash
make update
make update-contributors
grep -rn "{old_version}" README.md docs/

The contributor update combines commit contributors and issue filers, removes bots and known automation accounts, and writes the generated README table. Run it before committing the version-bump PR so every release thanks the current community without a scheduled workflow holding repository write permissions.

Update every stale pin by hand — currently pip install skillsaw==X.Y.Z in README.md and docs/ci.md, and the pre-commit rev: vX.Y.Z in README.md and docs/pre-commit.md. Never touch historical "Since vX.Y.Z" lines in docs/rules/.

Also verify the documented plugin dependency floor: grep -rn "skillsaw>=" docs/ skills/ examples/. The bump script rewrites skillsaw>= floors alongside the other pins, and tests/test_release_metadata.py asserts the floor never exceeds the project version — a floor above the released version makes every scaffolded plugin uninstallable, so investigate any grep hit the bump did not move.

Step 3: Create a version-bump PR

main is branch-protected — a direct push is rejected, so the bump goes through a PR. First verify remotes with git remote -v (origin should be stbenjam/skillsaw). Then run:

bash
git checkout -b release/{version}-bump
git add -A
git commit -m "[Auto] Bump version to {version}"
git push -u origin release/{version}-bump
gh pr create --title "[Auto] Bump version to {version}" --body "Version bump for the {version} release."

Wait for all required checks to pass, merge the PR, then update local main:

bash
gh pr checks {pr} --watch
gh pr merge {pr} --squash
git checkout main && git pull origin main

Step 4: Write the release notes

Read all commits since the last tag with git log:

bash
git log $(git describe --tags --abbrev=0)..HEAD --oneline --no-merges

Review the commits and group them into categories:

  • New features — new rules, new functionality.
  • Fixes — bug fixes, validation corrections.
  • Other — refactoring, CI changes, docs.

Write the release notes in markdown. Keep it concise — one line per change. Include a "## What's New" header. Skip trivial commits (version bumps, merge commits, CI-only changes).

Step 5: Create the GitHub release

Create the release only after the bump PR has merged, so the tag includes the bumped version files:

bash
gh release create v{version} --title "v{version}" --notes "{release_notes}" --target main

The tag triggers the release.yml workflow to build and publish to PyPI via OIDC trusted publishing.

Step 6: Verify the publish

Check the release workflow completes:

bash
gh run list --workflow release.yml --limit 1 --json status,conclusion

Check the conclusion: if success, the release is live on PyPI. If it failed, review the workflow logs and report the error.

Frequently asked questions

What does the Skillsaw Release AI skill do?

Bump the version, generate release notes, and publish a new skillsaw release to PyPI. Use when cutting a new release.

Why use Skillsaw Release on TypingMind?

Because you install it once and use it with any model. Skillsaw Release 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 Release in TypingMind?

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

Which AI models can use Skillsaw Release?

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 Release?

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

Is the Skillsaw Release 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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