Release logo

Release

Organization
kansoku-trade
release

Use when the user wants to release a new desktop app version (发版 / release / 发布新版本) — bumps apps/desktop version, writes user-facing release notes into CHANGELOG.md, and opens the release PR that drives the automated tag → build → publish pipeline

Overview

Publisherkansoku-trade
Repositorykansoku
Skill namerelease
Stars
329
Forks
40
Bundled files
Instructions only
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 kansoku-trade 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.
    https://github.com/kansoku-trade/kansoku/tree/main/.claude/skills/release
  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/kansoku-trade/kansoku.git /tmp/kansoku
mkdir -p .claude/skills
cp -r /tmp/kansoku/.claude/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.

Desktop Release

Opens a version-bump PR for the Kansoku desktop app. Everything after the PR merge is automated:

this skill → release PR (ci.yml gates it) → merge to main
  → desktop-tag.yml creates desktop-vX.Y.Z + dispatches desktop-release.yml
  → build, sign, appcast, publish GitHub Release (notes from CHANGELOG.md)
  → users receive the Sparkle update

Optional argument: patch | minor | major — skips the bump suggestion step.

Steps

1. Preflight (stop with an explanation if any fails)

bash
git status --porcelain        # must be empty
git branch --show-current     # must be main
git fetch origin main && git rev-list --count main..origin/main   # must be 0

If apps/pro is present (linked worktree), also pin the release to that commit before opening the PR. desktop-release.yml fetches Pro at the GitHub Actions variable KANSOKU_PRO_REF and then runs pnpm install --frozen-lockfile. A stale pin (the SHA from the last release) against a lockfile that already absorbed a newer apps/pro/package.json fails install. This is what hung v0.41.0.

bash
git -C apps/pro status --porcelain        # must be empty
git -C apps/pro fetch origin
PRO_SHA="$(git -C apps/pro rev-parse HEAD)"
git -C apps/pro merge-base --is-ancestor "$PRO_SHA" origin/main   # HEAD must already be on origin/main
gh variable set KANSOKU_PRO_REF --body "$PRO_SHA"
gh variable get KANSOKU_PRO_REF             # must print $PRO_SHA

2. Collect what shipped

bash
LAST_TAG=$(git describe --tags --match 'desktop-v*' --abbrev=0)
git log "$LAST_TAG"..HEAD --oneline -- apps/ packages/ patches/ scripts/

If git describe finds no tag, this is the first release — use the full history of those paths. If there are no commits touching apps/, packages/, patches/, or scripts/ since the last tag, stop: nothing to release.

Read the actual diffs of significant commits when the one-line messages aren't enough to describe user-visible changes.

3. Decide the version

Current version: node -p "require('./apps/desktop/package.json').version".

If the user passed patch/minor/major, apply it directly. Otherwise suggest one — any feat → minor, only fixes/refactors/chores → patch, breaking changes to user data or workflows → major — and ask the user to confirm the version number before continuing.

4. Write the release notes

Prepend a section to apps/desktop/CHANGELOG.md (below the file header, above the previous version's section):

markdown
## X.Y.Z — YYYY-MM-DD

- 更新点……

Rules for the notes (they become the GitHub Release body verbatim, and users read them):

  • 中文白话, user-facing. Describe what the user will notice, not what the code did. "图表页新增指标开关" not "refactor indicator pipeline".
  • Skip internal-only changes (refactors, CI, tests) unless they change behavior users can feel (e.g. faster startup).
  • A handful of bullet points; group related commits into one bullet.
  • Tickers, CLI/API names, file paths stay in English; no finance jargon without a plain-Chinese gloss.

5. Bump the version

Update version in apps/desktop/package.json to the confirmed X.Y.Z.

6. Open the PR

bash
git checkout -b release/desktop-vX.Y.Z
git add apps/desktop/package.json apps/desktop/CHANGELOG.md
git commit -m "release(desktop): vX.Y.Z"
git push -u origin release/desktop-vX.Y.Z
gh pr create --title "release(desktop): vX.Y.Z" --body "<release notes section>"

PR body = the new CHANGELOG section, plus one line noting that merging will auto-tag and publish the release.

7. Hand off

Show the PR link and remind: merge = release. After merge, desktop-tag.yml tags and dispatches desktop-release.yml; the release publishes automatically and the Sparkle feed goes live immediately.

Then switch back to main: git checkout main.

Guards

  • Never tag manually here — tagging is desktop-tag.yml's job after merge.
  • Never skip the CHANGELOG section — desktop-release.yml fails the build if the ## X.Y.Z section is missing.
  • Version in package.json and the CHANGELOG heading must match exactly (CI cross-checks tag vs package.json).
  • Never open the release PR with a stale KANSOKU_PRO_REF. The pin must be apps/pro HEAD, already on origin/main, and must match the lockfile's apps/pro specifiers.

Frequently asked questions

What does the Release AI skill do?

Use when the user wants to release a new desktop app version (发版 / release / 发布新版本) — bumps apps/desktop version, writes user-facing release notes into CHANGELOG.md, and opens the release PR that drives the automated tag → build → publish pipeline

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/kansoku-trade/kansoku/tree/main/.claude/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?

It is published on GitHub by kansoku-trade. Check the repository for licensing terms. 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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