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Managing Python Releases

CommunityPopular
kajisho5
managing-python-releases

Manages Python library releases including semantic versioning, changelog maintenance (Keep a Changelog format), release automation with GitHub Actions, and deprecation workflows. Use when planning releases, writing changelogs, automating release pipelines, or communicating breaking changes.

Overview

Publisherkajisho5
Repositoryffmpeg-skill
Skill namemanaging-python-releases
Stars
1.8K
Forks
144
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 kajisho5 on GitHub. Read the source before you install it.

Installation

Install the Managing Python Releases 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/kajisho5/ffmpeg-skill/tree/main/.claude/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/kajisho5/ffmpeg-skill.git /tmp/ffmpeg-skill
mkdir -p .claude/skills
cp -r /tmp/ffmpeg-skill/.claude/skills/release-management .claude/skills/managing-python-releases
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Managing Python Releases 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 Managing Python Releases 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 Managing Python Releases 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

Semantic Versioning

MAJOR.MINOR.PATCH (e.g., 1.2.3)

PATCH: Bug fixes, no API changes
MINOR: New features, backward compatible
MAJOR: Breaking changes

Changelog Format (Keep a Changelog)

markdown
# Changelog

## [Unreleased]
### Added
- New `batch_encode()` function

## [1.2.0] - 2024-03-15
### Added
- Support for custom formats (#123)

### Fixed
- Edge case at -180 longitude (#145)

### Deprecated
- `old_function()` - use `new_function()` instead

[Unreleased]: https://github.com/user/repo/compare/v1.2.0...HEAD
[1.2.0]: https://github.com/user/repo/releases/tag/v1.2.0

Categories: Added, Changed, Deprecated, Removed, Fixed, Security

Version in Code

python
# src/package/__init__.py
__version__ = "1.2.3"

# Or use importlib.metadata
from importlib.metadata import version
__version__ = version("my-package")

GitHub Actions Release (PyPI example)

A tag-triggered workflow builds and publishes to a registry via trusted publishing (no stored token) is the general shape — swap the publish step for whatever registry the project actually uses (PyPI, npm, crates.io, ...):

yaml
# .github/workflows/release.yml
on:
  push:
    tags: ['v*']

jobs:
  release:
    runs-on: ubuntu-latest
    permissions:
      contents: write      # create the GitHub release
      id-token: write      # trusted publishing (no token)
    steps:
      - uses: actions/checkout@v4
      - uses: astral-sh/setup-uv@v5
      - run: uv build
      - uses: softprops/action-gh-release@v2
        with:
          files: dist/*
      - uses: pypa/gh-action-pypi-publish@release/v1

Deprecation Process

Warn with stacklevel=2 so the message points at the caller.

python
import warnings

def old_function():
    """Deprecated: Use new_function() instead."""
    warnings.warn(
        "old_function() deprecated, will be removed in 2.0.0",
        DeprecationWarning,
        stacklevel=2,
    )
    return new_function()

Release Process

bash
# 1. Update CHANGELOG.md (move Unreleased to version)
# 2. Bump version in the project manifest
# 3. Commit and tag
git commit -am "Release v1.2.0"
git tag -a v1.2.0 -m "Release v1.2.0"
git push origin main --tags
# 4. CI publishes automatically (if automated) or publish manually

Checklist

Before Release:
- [ ] All tests pass
- [ ] CHANGELOG updated
- [ ] Version bumped
- [ ] Documentation current

After Release:
- [ ] Registry shows new version
- [ ] A fresh install actually works (not just that the tag/publish succeeded)
- [ ] GitHub release created
- [ ] Docs updated

Note for this repository (ffmpeg-skill)

CHANGELOG.md here already follows Keep a Changelog's dated-section format (## 0.10.0 — 2026-09-06 — ...), so the format guidance above matches this repo exactly. Two real differences from the generic PyPI example:

  • No automated release workflow exists. There is no .github/workflows/release.yml — the 0.10.0 release this session did every step by hand: bump package.json's version, convert the CHANGELOG's ## Unreleased into a dated section, git tag, create the GitHub Release, then npm publish separately. This is exactly the gap managing-python-releases is meant to close with automation — worth considering if releases become frequent enough that a manual miss (like 0.9.2 sitting un-published to npm for a while, discovered this session) becomes a recurring problem.
  • The registry is npm, not PyPI, and there is no __version__ in code — scripts/_contract.py's skill_version() and doctor's version field both read package.json directly (see this repo's own concurrent-branches and build-artifacts skill notes for the related merge-conflict and publish-verification patterns this touches).

Source: wdm0006/python-skills (MIT).

Frequently asked questions

What does the Managing Python Releases AI skill do?

Manages Python library releases including semantic versioning, changelog maintenance (Keep a Changelog format), release automation with GitHub Actions, and deprecation workflows. Use when planning releases, writing changelogs, automating release pipelines, or communicating breaking changes.

Why use Managing Python Releases on TypingMind?

Because you install it once and use it with any model. Managing Python Releases 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 Managing Python Releases in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kajisho5/ffmpeg-skill/tree/main/.claude/skills/release-management. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Managing Python Releases?

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 Managing Python Releases?

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

Is the Managing Python Releases AI skill free?

Yes. It is published on GitHub by kajisho5 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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