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Release

CommunityPopular
Yeachan-Heo
release

Generic release assistant — analyzes repo release rules, caches them in .omc/RELEASE_RULE.md, then guides the release

Overview

PublisherYeachan-Heo
Repositoryoh-my-claudecode
Skill namerelease
Stars
39.2K
Forks
3.5K
Bundled files
Instructions only
LicenseMIT
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 Yeachan-Heo on GitHub. Read the source before you install it.

Installation

Install the 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.
  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/Yeachan-Heo/oh-my-claudecode.git /tmp/oh-my-claudecode
mkdir -p .claude/skills
cp -r /tmp/oh-my-claudecode/skills/release .claude/skills/release
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Release Skill

A thin, repo-aware release assistant. On first run it inspects the project and CI to derive release rules, stores them in .omc/RELEASE_RULE.md for future use, then walks you through a release using those rules.

Usage

/oh-my-claudecode:release [version]
  • version is optional. If omitted the skill will ask. Accepts patch, minor, major, or an explicit semver like 2.4.0.
  • Add --refresh to force re-analysis of the repo even when a cached rule file exists.

Execution Flow

Step 0 — Load or Build Release Rules

Check whether .omc/RELEASE_RULE.md exists.

If it does NOT exist (or --refresh was passed): Run the full repo analysis below and write the file.

If it DOES exist: Read the file. Then do a quick delta check — scan .github/workflows/ (or equivalent CI dirs: .circleci/, .travis.yml, Jenkinsfile, bitbucket-pipelines.yml, gitlab-ci.yml) for any modifications newer than the last-analyzed timestamp in the rule file. If relevant workflow files changed, re-run the analysis for those sections and update the file. Report what changed.


Step 1 — Repo Analysis (first run or --refresh)

Inspect the repo and answer the following. Write answers into .omc/RELEASE_RULE.md.

1a. Version Sources
  • Locate all files that contain a version string matching the current version in package.json / pyproject.toml / Cargo.toml / build.gradle / VERSION file / etc.
  • List each file and the field or regex pattern used to find the version.
  • Detect whether there is a release automation script (e.g. scripts/release.*, Makefile release target, bump2version, release-it, semantic-release, changesets, goreleaser).
1b. Registry / Distribution
  • npm (package.json with publishConfig or npm publish in CI), PyPI (pyproject.toml + twine/flit), Cargo (Cargo.toml), Docker (Dockerfile + push step), GitHub Packages, other.
  • Is there a CI step that publishes automatically on tag push? Which workflow file and job?
1c. Release Trigger
  • Identify what starts the release: tag push (v*), manual dispatch (workflow_dispatch), merge to main/master, a release branch merge, a commit message pattern.
1d. Test Gate
  • Identify the test command and where it runs in CI.
  • Are tests required to pass before publish? Note any bypass flags.
1e. Release Notes / Changelog
  • Does a CHANGELOG.md or CHANGELOG.rst exist?
  • What convention is used: Keep a Changelog, Conventional Commits, GitHub auto-generated, none?
  • Is there a release body file (e.g. .github/release-body.md) committed pre-tag?
1f. First-Time User Check
  • Does a release workflow exist in .github/workflows/ (or equivalent)? If not, flag this and offer to scaffold one.
  • Is there a .gitignore entry preventing build artifacts from being committed? If not, flag it.
  • Are git tags being used? Run git tag --list to check. If no tags exist, flag and explain best practice.

Step 2 — Write .omc/RELEASE_RULE.md

Create or overwrite the file with this structure:

markdown
# Release Rules
<!-- last-analyzed: YYYY-MM-DDTHH:MM:SSZ -->

## Version Sources
<!-- list of files + patterns -->

## Release Trigger
<!-- what kicks off the release -->

## Test Gate
<!-- command + CI job name -->

## Registry / Distribution
<!-- npm, PyPI, Docker, etc. + CI job that publishes -->

## Release Notes Strategy
<!-- convention + files -->

## CI Workflow Files
<!-- paths to relevant workflow files -->

## First-Time Setup Gaps
<!-- any missing pieces found during analysis, or "none" -->

Step 3 — Determine Version

If the user provided a version argument, use it. Otherwise:

  1. Show the current version (from the primary version file).
  2. Show what patch, minor, and major would produce.
  3. Ask the user which to use.

Validate the chosen version is a valid semver string.


Step 4 — Pre-Release Checklist

Present a checklist derived from the release rules. At minimum:

  • All changes intended for this release are committed and pushed
  • CI is green on the target branch
  • Tests pass locally (run the test gate command)
  • Version bump applied to all version source files
  • Release notes / changelog prepared (see Step 5)

Ask the user to confirm before proceeding, or run each step if they say "go ahead".


Step 5 — Release Notes Guidance

Help the user write good release notes. Apply whichever convention the repo uses. Default guidance when no convention is detected:

What makes a good release note:

  • Lead with what changed for users, not internal implementation details.
  • Group by type: New Features, Bug Fixes, Breaking Changes, Deprecations, Internal / Chores.
  • For each item: one sentence, link to the PR or issue, credit the author if external.
  • Breaking changes go first and must include a migration path.
  • Omit changes users never see (refactors, CI tweaks, test-only changes) unless they affect build reproducibility.

Example entry format:

### Bug Fixes
- Fix session drop on token expiry (#123) — @contributor

If the repo uses Conventional Commits, generate a draft changelog from git log <prev-tag>..HEAD --no-merges --format="%s" grouped by commit type. Show it to the user and let them edit.


Step 6 — Execute Release

Using the rules discovered, walk through:

  1. Bump version — apply to each version source file.
  2. Run tests — execute the test gate command.
  3. Commitgit add <version files> CHANGELOG.md and commit with chore(release): bump version to vX.Y.Z.
  4. Taggit tag -a vX.Y.Z -m "vX.Y.Z" (annotated tags are preferred over lightweight).
  5. Pushgit push origin <branch> && git push origin vX.Y.Z.
  6. CI takes over — if the release trigger is a tag push, remind the user that CI will handle the rest (publish, GitHub release creation). Show the expected CI workflow file.
  7. Manual publish — if no CI automation exists, list the manual publish command (e.g. npm publish --access public, twine upload dist/*).

Step 7 — First-Time Setup Suggestions

If gaps were found in Step 1f, offer concrete help:

No release workflow:

Your repo doesn't have a release CI workflow. A GitHub Actions workflow triggered on v* tag push is the most common best practice. It can:

  • Run tests
  • Publish to npm/PyPI/etc.
  • Create a GitHub Release with your release notes

Want me to scaffold a .github/workflows/release.yml for your stack?

No git tags:

This appears to be the first release. Git tags let GitHub, npm, and other tools understand your version history. We'll create your first tag in Step 6.

Build artifacts not gitignored:

Build artifacts are present in git history or not gitignored. This inflates repo size and creates merge conflicts. Want me to add them to .gitignore?


Step 8 — Verify

After the push:

  • Check CI status: gh run list --workflow=<release workflow> --limit=3 (if gh is available).
  • Check the registry (npm, PyPI) for the new version after a few minutes.
  • Confirm a GitHub Release was created: gh release view vX.Y.Z.

Report success or flag any failures.


Notes

  • This skill does not hardcode any project-specific version files or commands. Everything is derived from repo inspection.
  • .omc/RELEASE_RULE.md is a local cache. Commit it to your repo if you want to share the derived rules with your team, or add it to .gitignore if you prefer it stays local.
  • For complex monorepos or multi-package workspaces, the skill will detect workspace patterns (npm workspaces, pnpm workspaces, Cargo workspace) and adapt accordingly.

Frequently asked questions

What does the Release AI skill do?

Generic release assistant — analyzes repo release rules, caches them in .omc/RELEASE_RULE.md, then guides the release

Why use Release on TypingMind?

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

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

Which AI models can use 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 Release?

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

Is the Release AI skill free?

Yes. It is published on GitHub by Yeachan-Heo under the MIT 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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