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Changelog Composer

Community
Mathews-Tom
changelog-composer

Generates structured changelogs and release notes from git history and PRs, classifying breaking changes, features, fixes, performance, docs. Triggers on: "generate changelog", "write release notes", "what changed since", "prepare release", "release notes for", "diff since tag".

Overview

PublisherMathews-Tom
Repositoryarmory
Skill namechangelog-composer
Stars
318
Forks
47
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

    Published by Mathews-Tom on GitHub. Read the source before you install it.

Installation

Install the Changelog Composer 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/Mathews-Tom/armory.git /tmp/armory
mkdir -p .claude/skills
cp -r /tmp/armory/skills/changelog-composer .claude/skills/changelog-composer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Changelog Composer 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 Changelog Composer 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 Changelog Composer 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.

Changelog Composer

Transforms raw git history and PR descriptions into polished, audience-appropriate changelogs. Parses conventional commits, classifies changes by impact category, filters internal-only modifications, and produces structured release notes with PR links — following Keep a Changelog conventions.

Reference Files

FileContentsLoad When
references/conventional-commits.mdCommit type parsing, scope extraction, breaking change indicatorsRepository uses conventional commits
references/categorization-rules.mdChange classification logic, audience filtering, severity orderingAlways
references/audience-filter.mdUser-facing vs internal change detection, exclusion patternsAlways
references/changelog-formats.mdKeep a Changelog, GitHub Releases, announcement copy templatesFormat selection needed

Prerequisites

  • git — access to the repository history
  • gh (optional) — GitHub CLI for PR description extraction
  • A tagging strategy (semver tags) for identifying release boundaries

Workflow

Phase 1: Gather Raw Changes

Collect all changes between the previous release and the current state:

  1. Identify boundaries — Find the last release tag: git describe --tags --abbrev=0. If no tags exist, use the initial commit or a user-specified starting point.
  2. Extract commitsgit log <last-tag>..HEAD --oneline --no-merges
  3. Extract PR titlesgh pr list --state merged --base main --search "merged:>YYYY-MM-DD" or parse merge commit messages.
  4. Parse conventional commits — If the repository follows conventional commits (feat:, fix:, docs:, etc.), extract type, scope, and description. See references/conventional-commits.md.
  5. Collect breaking change indicators — Look for BREAKING CHANGE: in commit bodies, ! after type (feat!:), or explicit annotations in PR descriptions.

Phase 2: Classify Changes

Categorize each change by its impact:

CategoryConventional Commit TypeIndicators
Breaking Changesfeat!:, BREAKING CHANGE:API removal, signature change, behavior change
Featuresfeat:New capability, new endpoint, new command
Fixesfix:Bug correction, error handling improvement
Performanceperf:Speed improvement, memory reduction
Documentationdocs:README, API docs, guides
Internalchore:, ci:, refactor:, test:, build:No user-facing impact

For repositories without conventional commits, classify by reading the commit message and changed files. Code changes to public API → Feature or Fix. Test-only changes → Internal.

Phase 3: Filter for Audience

  1. Exclude internal changes by default:

    • CI/CD configuration changes
    • Test additions/modifications
    • Dependency bumps (unless security-relevant)
    • Code refactoring with no behavior change
    • Build system changes
  2. Include internal changes only when:

    • They represent significant architecture shifts users should know about
    • They affect development workflow (contributing guide changes)
    • The changelog targets developers, not end-users
  3. Highlight breaking changes prominently — always at the top, always with migration guidance.

Phase 4: Compose Entries

For each included change, write a human-readable description:

  1. Lead with the impact — "Users can now..." or "Fixed issue where..."
  2. Be specific — "Reduced memory usage by 40% for large file processing" not "Performance improvements"
  3. Include migration guidance for breaking changes — what the user must change
  4. Link to source — PR number, issue number, or commit hash

Phase 5: Output

Assemble the changelog in the requested format, ordered by severity:

  1. Breaking Changes (always first)
  2. Features
  3. Fixes
  4. Performance
  5. Documentation

Output Format

text
## [{version}] - {YYYY-MM-DD}

### Breaking Changes
- **`function_name` parameter renamed** — `old_param` is now `new_param`.
  Migration: find/replace `old_param=` with `new_param=` in all call sites. ([#{pr}]({url}))

### Features
- **{Feature name}** — {What it enables and why it matters}. ([#{pr}]({url}))

### Fixes
- Fixed {symptom} when {condition}. ([#{pr}]({url}))

### Performance
- {Operation} is now {X}x faster / uses {X}% less memory. ([#{pr}]({url}))

### Documentation
- Added {guide/reference} for {topic}. ([#{pr}]({url}))

Configuring Scope

ModeInputOutputWhen to Use
releaseTag-to-HEADFull changelog entryPreparing a versioned release
sprintDate range or commit rangeSummary of changesSprint review, status update
prSingle PROne-line changelog entryPR description template

Calibration Rules

  1. User impact first. Every entry should answer "what does this mean for the user?" not "what did the developer do?"
  2. Breaking changes are non-negotiable. Never omit or bury breaking changes. They go first, with migration guidance.
  3. Specific over vague. "Fixed login timeout on slow connections" beats "Fixed bug." "Added CSV export for reports" beats "New feature."
  4. Link everything. Every entry links to its source PR or issue. Users who want details can follow the link.
  5. Exclude noise. Internal refactoring, dependency bumps, and CI changes do not belong in user-facing changelogs unless they have user-visible impact.

Error Handling

ProblemResolution
No tags exist in repositoryAsk for a starting commit or date. Default to the initial commit if the repository is small.
Repository doesn't use conventional commitsClassify by reading commit messages and changed files. Note reduced classification accuracy.
PR descriptions are empty or low-qualityFall back to commit messages. Flag entries that may need manual review.
Ambiguous change classificationDefault to "Features" for additions, "Fixes" for modifications. Mark uncertain entries for review.
Too many changes for a single releaseGroup by component/module. Consider whether the release should be split.
Merge commits obscure individual changesUse --no-merges to skip merge commits. Parse individual commits within merged PRs.

When NOT to Compose

Push back if:

  • The user wants to auto-publish release notes without review — changelogs require human judgment
  • The repository has no meaningful commit history (single "initial commit" with everything)
  • The request is for marketing copy, not technical release notes — different skill
  • The changes are not yet merged — changelog is for shipped changes, not in-progress work

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 Changelog Composer AI skill do?

Generates structured changelogs and release notes from git history and PRs, classifying breaking changes, features, fixes, performance, docs. Triggers on: "generate changelog", "write release notes", "what changed since", "prepare release", "release notes for", "diff since tag".

Why use Changelog Composer on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mathews-Tom/armory/tree/main/skills/changelog-composer. 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 Changelog Composer?

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 Changelog Composer?

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

Is the Changelog Composer AI skill free?

Yes. It is published on GitHub by Mathews-Tom 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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