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Release

Community
312362115
release

发版自动化技能:CalVer 版本号 + 从 git log 生成 changelog + GitHub Releases。 覆盖完整发版流程:版本号生成→changelog 生成→git tag→GitHub Release(或本地 changelog 文件)。 在发版前自动触发 security-audit 安全审查(如果配置了 CI/CD 则检查 CI 状态)。 触发词:发版、发布、release、生成 changelog、打 tag、版本发布。 触发场景:功能开发完成准备发布、用户主动要求发版、里程碑完成。

Overview

Publisher312362115
Repositoryclaude
Skill namerelease
Stars
107
Forks
17
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 312362115 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/312362115/claude/tree/main/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/312362115/claude.git /tmp/claude
mkdir -p .claude/skills
cp -r /tmp/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.

发版(Release)

发版不只是打 tag,是一个质量门禁流程:安全审查→生成版本→创建 Release。


第一步:发版前检查

发版前必须确认以下条件:

准备发版
  │
  ├─ 1. 代码状态检查
  │   ├─ 工作区干净?(无未提交改动)
  │   ├─ 在正确的分支上?(main / release branch)
  │   └─ 已合并所有需要的 PR?
  │
  ├─ 2. CI 状态检查(如果有)
  │   └─ GitHub Actions 全绿?
  │
  ├─ 3. 安全审查
  │   └─ 提示用户:是否需要跑 security-audit?(上线服务强烈建议)
  │
  └─ 4. 测试验证
      └─ 核心功能是否经过验证?

检查命令:

bash
# 工作区状态
git status

# CI 状态(GitHub)
gh run list --limit 5

# 未合并的 PR
gh pr list

任何检查不通过,提示用户处理后再继续,不强行发版。


第二步:确定版本号

CalVer 格式

YYYY.MM.PATCH
部分含义示例
YYYY年份2026
MM月份(不补零)4
PATCH当月第几次发布(从 1 开始)1, 2, 3...

版本号生成逻辑:

bash
# 获取当前年月
YEAR=$(date +%Y)
MONTH=$(date +%-m)

# 查找当月已有的最大版本号
LATEST=$(git tag --list "${YEAR}.${MONTH}.*" --sort=-version:refname | head -1)

if [ -z "$LATEST" ]; then
  # 当月第一次发布
  VERSION="${YEAR}.${MONTH}.1"
else
  # 递增 PATCH
  PATCH=$(echo "$LATEST" | awk -F. '{print $3 + 1}')
  VERSION="${YEAR}.${MONTH}.${PATCH}"
fi

echo "版本号: $VERSION"

示例:

  • 2026 年 4 月第 1 次发布 → 2026.4.1
  • 2026 年 4 月第 2 次发布 → 2026.4.2
  • 2026 年 5 月第 1 次发布 → 2026.5.1

第三步:生成 Changelog

从 git log 提取

bash
# 获取上一个 tag 到 HEAD 的所有提交
PREV_TAG=$(git describe --tags --abbrev=0 2>/dev/null || echo "")

if [ -z "$PREV_TAG" ]; then
  # 没有历史 tag,取所有提交
  COMMITS=$(git log --oneline --pretty=format:"%s")
else
  COMMITS=$(git log ${PREV_TAG}..HEAD --oneline --pretty=format:"%s")
fi

按 type 分组

根据 commit message 的 type(scope): desc 格式自动分组:

markdown
# Changelog — v2026.4.1

## 新功能(Features)
- **auth**: 支持 OAuth2 第三方登录
- **export**: 用户数据导出功能

## 修复(Fixes)
- **login**: 修复密码错误时返回 500 的问题
- **cache**: 修复缓存 key 冲突

## 重构(Refactoring)
- **api**: 统一错误响应格式

## 其他
- **docs**: 更新 API 文档
- **test**: 补充登录模块测试用例

分组规则:

commit typechangelog 分组
feat新功能(Features)
fix修复(Fixes)
refactor重构(Refactoring)
perf性能优化(Performance)
docs, test, chore, ci其他(如果没有 feat/fix/refactor 可省略)

Changelog 输出

  • 有 GitHub:嵌入 GitHub Release 的 body
  • 无 GitHub:写入 docs/changelog/YYYY-MM-DD-v<版本号>.md

第四步:创建 Release

方式 A:GitHub Release(优先)

bash
# 创建 tag
git tag -a "v${VERSION}" -m "Release v${VERSION}"
git push origin "v${VERSION}"

# 创建 GitHub Release
gh release create "v${VERSION}" \
  --title "v${VERSION}" \
  --notes "$(cat <<'EOF'
<changelog 内容>
EOF
)"

方式 B:本地 Changelog 文件

如果项目不在 GitHub 或不需要 GitHub Release:

bash
# 创建 tag
git tag -a "v${VERSION}" -m "Release v${VERSION}"

# 写入 changelog 文件
# 存入 docs/changelog/YYYY-MM-DD-v<版本号>.md

第五步:发版后

  1. 通知用户:发版完成,输出版本号和 Release URL
  2. 更新 package.json / pyproject.toml(如果项目需要版本号同步到配置文件)
  3. 提醒:如果有需要手动操作的部署步骤,列出来

完整发版流程

用户:发个版
  │
  ├─ Step 1: 发版前检查(代码状态 + CI + 安全)
  ├─ Step 2: 生成版本号(CalVer)
  ├─ Step 3: 生成 Changelog(从 git log)
  ├─ Step 4: 创建 Release(GitHub 或本地文件)
  └─ Step 5: 发版后处理

与其他 skill 的关系

task-finish(功能收尾)
  ↓
security-audit(发版前安全审查)
  ↓ 审查通过
release(本 skill — 发版)

Frequently asked questions

What does the Release AI skill do?

发版自动化技能:CalVer 版本号 + 从 git log 生成 changelog + GitHub Releases。 覆盖完整发版流程:版本号生成→changelog 生成→git tag→GitHub Release(或本地 changelog 文件)。 在发版前自动触发 security-audit 安全审查(如果配置了 CI/CD 则检查 CI 状态)。 触发词:发版、发布、release、生成 changelog、打 tag、版本发布。 触发场景:功能开发完成准备发布、用户主动要求发版、里程碑完成。

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/312362115/claude/tree/main/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?

Yes. It is published on GitHub by 312362115 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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