Release Management logo

Release Management

Community
yonatangross
release-management

Automates GitHub releases with semantic versioning, changelog generation from merged PRs, and gh CLI integration. Supports draft, prerelease, and standard release workflows with task-tracked multi-phase execution. Use when creating releases, tagging versions, or publishing changelogs.

Overview

Publisheryonatangross
Repositoryorchestkit
Skill namerelease-management
Stars
298
Forks
35
Bundled files
11
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.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Release Management 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/yonatangross/orchestkit/tree/main/plugins/ork/skills/release-management
  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/yonatangross/orchestkit.git /tmp/orchestkit
mkdir -p .claude/skills
cp -r /tmp/orchestkit/plugins/ork/skills/release-management .claude/skills/release-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Release Management 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 Management 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 Management 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 Management

Automate releases with gh release, semantic versioning, and changelog generation.

CC ≥ 2.1.118 (M122): claude plugin tag <version> validates the plugin manifest hierarchy (marketplace.json, plugin.json, package.json, version.txt) before tagging. Runs --dry-run in CI before release-please opens its release PR; catches manifest drift early. See src/skills/chain-patterns/references/plugin-tag.md.

CRITICAL: Task Management is MANDATORY (CC 2.1.16)

BEFORE doing ANYTHING else, create tasks to track progress:

python
# 1. Create main task IMMEDIATELY
TaskCreate(
  subject="Release: {version}",
  description="Creating release with semantic versioning and changelog",
  activeForm="Releasing {version}"
)

# 2. Create subtasks for each release phase
TaskCreate(subject="Version & changelog", activeForm="Determining version and generating changelog")
TaskCreate(subject="Create release", activeForm="Creating GitHub release")
TaskCreate(subject="Verify & announce", activeForm="Verifying release and announcing")

# 3. Set dependencies for sequential phases
TaskUpdate(taskId="3", addBlockedBy=["2"])
TaskUpdate(taskId="4", addBlockedBy=["3"])

# 4. Update status as you progress
TaskUpdate(taskId="2", status="in_progress")  # When starting
TaskUpdate(taskId="2", status="completed")    # When done

Quick Reference

Create Release

bash
# Auto-generate notes from PRs
gh release create v1.2.0 --generate-notes

# With custom title
gh release create v1.2.0 --title "Version 1.2.0: Performance Update" --generate-notes

# Draft release (review before publishing)
gh release create v1.2.0 --draft --generate-notes

# Pre-release (beta, rc)
gh release create v1.2.0-beta.1 --prerelease --generate-notes

# With custom notes
gh release create v1.2.0 --notes "## Highlights
- New auth system
- 50% faster search"

# From notes file
gh release create v1.2.0 --notes-file RELEASE_NOTES.md

List & View Releases

bash
# List all releases
gh release list

# View specific release
gh release view v1.2.0

# View in browser
gh release view v1.2.0 --web

# JSON output
gh release list --json tagName,publishedAt,isPrerelease

Verify Releases (gh CLI 2.86.0+)

bash
# Verify release attestation (sigstore)
gh release verify v1.2.0

# Verify specific asset
gh release verify-asset v1.2.0 ./dist/app.zip

# Verify with custom trust policy
gh release verify v1.2.0 --owner myorg

Manage Releases

bash
# Edit release
gh release edit v1.2.0 --title "New Title" --notes "Updated notes"

# Delete release
gh release delete v1.2.0

# Upload assets
gh release upload v1.2.0 ./dist/app.zip ./dist/app.tar.gz

Semantic Versioning

text
MAJOR.MINOR.PATCH
  │     │     │
  │     │     └── Bug fixes (backwards compatible)
  │     └──────── New features (backwards compatible)
  └────────────── Breaking changes

Examples:
  1.0.0 → 1.0.1  (patch: bug fix)
  1.0.1 → 1.1.0  (minor: new feature)
  1.1.0 → 2.0.0  (major: breaking change)

Pre-release:
  2.0.0-alpha.1  (early testing)
  2.0.0-beta.1   (feature complete)
  2.0.0-rc.1     (release candidate)

Release Workflows

Standard and hotfix release procedures using git tags and gh release.

Load Read("references/release-workflows.md") for step-by-step standard and hotfix release workflows.


Changelog Generation

Auto-generated from PRs, custom .github/release.yml templates, and manual CHANGELOG.md format.

Load Read("references/changelog-generation.md") for changelog template examples and Keep-a-Changelog format.


Release Automation & Checklist

GitHub Actions workflow for tag-triggered releases, version bumping script, and pre/post-release checklist.

Load Read("references/release-automation.md") for CI workflow, bump script, and release checklist.


Best Practices

  1. Use semantic versioning - Communicate change impact
  2. Draft releases first - Review notes before publishing
  3. Generate notes from PRs - Accurate, automatic history
  4. Close milestone on release - Track completion
  5. Tag main only - Never tag feature branches
  6. Announce breaking changes - Prominent in release notes

Related Skills

  • ork:github-operations: Milestones, issues, and CLI reference
  • ork:github-operations: Branch management and git operations

References

Load on demand with Read("references/<file>"):

FileContent
semver.mdSemantic versioning rules and decision tree
release-workflows.mdStandard and hotfix release procedures
changelog-generation.mdAuto-generated, template, and manual changelog formats
release-automation.mdGitHub Actions workflow, bump script, and checklist

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Release Management AI skill do?

Automates GitHub releases with semantic versioning, changelog generation from merged PRs, and gh CLI integration. Supports draft, prerelease, and standard release workflows with task-tracked multi-phase execution. Use when creating releases, tagging versions, or publishing changelogs.

Why use Release Management on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/release-management. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Release Management?

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 Management?

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

Is the Release Management AI skill free?

Yes. It is published on GitHub by yonatangross 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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