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Rea Changelog Update

CommunityPopular
morluto
rea-changelog-update

Prepare or rewrite REA release changelogs and GitHub release notes from pinned Git history, with verified contributor thanks and Release Please synchronization.

Overview

Publishermorluto
Repositoryrea
Skill namerea-changelog-update
Stars
110.4K
Forks
24.5K
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 morluto on GitHub. Read the source before you install it.

Installation

Install the Rea Changelog Update 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/morluto/rea/tree/main/.agents/skills/rea-changelog-update
  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/morluto/rea.git /tmp/rea
mkdir -p .claude/skills
cp -r /tmp/rea/.agents/skills/rea-changelog-update .claude/skills/rea-changelog-update
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rea Changelog Update 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 Rea Changelog Update 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 Rea Changelog Update 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.

REA changelog update

Use this for release-note preparation and contributor attribution. Read docs/releasing.md from the repository root for publication authority and the existing checkpoint checks. This skill prepares notes; it does not authorize merging, tagging, or publishing.

Inventory before prose

Fetch complete history and tags. Select the published baseline tag from .release-please-manifest.json on the release PR's base branch, and pin that branch's exact SHA as the editorial target. REA tags are rea-agents-VERSION. Use the proposed version, including its prerelease suffix; do not change the always-bump-minor strategy to imitate OpenClaw's calendar versions.

bash
node scripts/release-notes.mjs inventory \
  --repository morluto/rea --base rea-agents-PREVIOUS \
  --target FULL_BASE_BRANCH_SHA --version VERSION \
  --manifest /tmp/rea-VERSION.contributions.json
node scripts/release-notes.mjs record \
  --manifest /tmp/rea-VERSION.contributions.json \
  --summary \
  --output /tmp/rea-VERSION.record.md

Read the inventory, not just the existing bot draft. It includes side-branch commits, GitHub-associated PRs whose merge commits are in the range, commit authors/co-authors, and confirmed closing issues with their reporters. GitHub connections are paginated. Network errors stop generation; rerun into a new output file after recovery. Existing output files are never overwritten.

Unresolved commit identities are retained as names/emails with user: null; never guess a handle or thank the merger as a substitute. The report preserves unknowns. This is a reviewed metadata snapshot, not independent proof that a human authored a patch. For unattributed identities, unusual merge strategies, backports, reversions, or references that GitHub did not associate, inspect the source PRs and diffs and report the limitation. The current collector uses confirmed PR-closing relationships for issue-reporter credit; unrelated issue mentions are not automatically credited. Do not claim that merely inventoried work still ships after a revert.

Write for users and retain credit

Rewrite only the newest ## [VERSION] section in CHANGELOG.md, preserving its version/date/link heading. Use single-line Markdown list items under:

  • ### Highlights: a few release-defining user outcomes; no quota for small releases.
  • ### Changes: capabilities and meaningful behavior changes.
  • ### Fixes: reliability, correctness, compatibility, and safety fixes.
  • ### ⚠ BREAKING CHANGES: retain when applicable, with migrations and original PR/commit references required by the existing checkpoint validator.

Group related work by analyst workflow or user impact. Explain the resulting behavior in short, concrete entries. Highlights summarize the release; avoid repeating their full descriptions in Changes. Internal cleanup, tests, CI, and routine documentation generally belong only in the provenance file. Read ambiguous PRs/diffs before making claims. Distinguish simulated-provider checks from real-provider verification.

Credit each represented contribution once in the public notes with its link and Thanks @... for its verified human author, known co-authors, and confirmed closing-issue reporters. Grouping preserves all those credits in one line. When Highlights, detailed entries or migrations reference the same work, put its thanks in one of those entries, not all of them. Include issue links. Direct-commit bullets carry the commit link and known human credit. Exclude GitHub bots and known agent accounts. REA's observed Anthropic and Cursor co-author trailers resolve to @claude and @cursoragent with GitHub type User; the credit policy excludes them while retaining their metadata. At the REA maintainer's request, omit @morluto self-thanks while preserving authorship in the inventory. Other human contributors remain eligible. Use explicit GitHub links for cross-repository references to avoid ambiguous #NNN numbers. Unknown handles stay unknown; they are not fabricated credit.

Append the generated record --summary provenance link verbatim at the end of this release. The linked inventory accounts for every discovered in-range PR and direct contribution, including internal work omitted from the prose. Do not paste the full record output into public notes: it duplicates thanks and obscures the user-facing changes. Full records remain available for review. Do not hand-edit generated credits to make validation pass. Refresh the inventory when product history changes; a prose-only correction can reuse the snapshot.

Finalize with Release Please

Before changing the live release PR, have authorization for that PR update. Local preparation and validation can proceed independently.

  1. Add rea:release-notes-finalized to the selected open release PR before editing its branch/body. Main proposal pushes respect this label. This is our workflow guard, not Release Please's autorelease: snooze mechanism. A proposal already running must finish before editing. Do not dispatch manual preparation over finalized notes.

  2. On that release branch, save the reviewed inventory as docs/releases/VERSION.contributions.json. This is durable public release provenance, not a temporary planning file. Do not add credentials or local environment dumps. Keep the existing version artifacts aligned.

  3. Run the existing checkpoint validator and the new checker:

    bash
    node scripts/release-notes.mjs check \
      --manifest docs/releases/VERSION.contributions.json
    node scripts/release-notes.mjs render \
      --manifest docs/releases/VERSION.contributions.json \
      --output /tmp/rea-VERSION.notes.md

    The checker supports inline PR bodies, including their surrounding metadata, up to GitHub's 65,536-character limit. It fails on larger notes rather than truncating credit; Release Please's overflow-link bodies are not integrated.

  4. Update the release PR body with the rendered section while preserving its Release Please header/footer and component/version structure. Use a body file for gh pr edit --body-file; changing only CHANGELOG.md does not change Release Please's release body. Push only authorized changes.

  5. Freshly fetch the PR body to a file and run check --pr-body FILE. Review the complete diff and wait for current CI. Publication checks the body again before release creation. Avoid further body edits while publishing.

The finalization label freezes bot refreshes, not main. New product changes after the inventory target require refreshing the notes before publication. The workflow checks target ancestry and allows only release metadata paths after that target. It also requires the inventory target to equal the reviewed merge's first parent, so intervening main commits require a fresh inventory. Review diffs within the allowed release metadata paths too: the path guard does not prove that package dependency/script edits are version-only changes. To resume automatic proposals, remove the finalization label and let the bot regenerate; expect editorial changes to be replaced.

Existing releases without a contribution inventory retain their current publication path. Never retag or republish a package to correct release prose.

Frequently asked questions

What does the Rea Changelog Update AI skill do?

Prepare or rewrite REA release changelogs and GitHub release notes from pinned Git history, with verified contributor thanks and Release Please synchronization.

Why use Rea Changelog Update on TypingMind?

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

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

Which AI models can use Rea Changelog Update?

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 Rea Changelog Update?

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

Is the Rea Changelog Update AI skill free?

Yes. It is published on GitHub by morluto 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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