Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/shenjingnan/xiaozhi-client/commitnpx skills add shenjingnan/xiaozhi-client --skill commitgit clone --depth 1 https://github.com/shenjingnan/xiaozhi-clientWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00010 | $0.02823 |
| Opus 5 | $0.00005 | $0.01411 |
| Sonnet 5 | $0.00002 | $0.00565 |
| Haiku 4.5 | $0.00001 | $0.00282 |
Grade A, and why
commit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
我是 Git Commit 生成技能,专门为 xiaozhi-client 项目生成符合规范的、代码评审友好的 commit 信息,同时遵循务实开发理念。
技能使用原则
- 真实准确,但避免过度详细:基于实际代码改动生成信息,不虚构不存在的细节
- 评审友好优先,格式完美次之:让评审人快速理解改动比完美格式更重要
- 简单明确优于复杂说明:优先选择清晰直接的描述方式
- 务实开发指导:突出"为什么改"和"改了什么"两个核心问题
技能能力
1. 代码改动分析
核心能力:分析实际的代码改动,提取关键信息用于生成 commit 信息。
分析流程
# 1. 更新远程分支信息
git fetch origin
# 2. 获取主分支名称
MAIN_BRANCH=$(git symbolic-ref refs/remotes/origin/HEAD | sed 's@^refs/remotes/origin/@@')
# 3. 获取改动内容(优先检查未 add 的改动)
git diff --no-pager
git diff --cached --no-pager
# 4. 获取相对于主分支的改动
git diff --no-pager origin/$MAIN_BRANCH...HEAD
分析要点
- 改动范围:哪些文件、模块被修改
- 改动类型:新增功能、修复 bug、重构、文档更新等
- 影响分析:改动对现有功能的影响
- 验证方式:如何验证改动的正确性
2. Commit 类型判断
根据改动内容自动判断合适的 commit 类型。
Type 类型定义
feat:新增 featurefix:修复 bugdocs:仅仅修改了文档,比如 README、CHANGELOG、CONTRIBUTE 等等style:仅仅修改了空格、格式缩进、逗号等等,不改变代码逻辑refactor:代码重构,没有加新功能或者修复 bugperf:优化相关,比如提升性能、体验test:测试用例,包括单元测试、集成测试等chore:改变构建流程、或者增加依赖库、工具等revert:回滚到上一个版本
类型判断示例
// feat 判断标准
// - 新增文件
// - 新增公共方法
// - 新增配置选项
// - 新增 API 端点
// fix 判断标准
// - 修复 bug
// - 修复内存泄漏
// - 修复崩溃问题
// - 修复类型错误
// refactor 判断标准
// - 代码结构调整
// - 变量重命名
// - 代码提取/合并
// - 类型定义优化
// test 判断标准
// - 新增测试用例
// - 修复测试用例
// - 提高测试覆盖率
3. 分支名称生成
根据改动内容生成符合规范的分支名称。
分支命名规范
- 前缀类型:
feature/、bugfix/、docs/、test/、refactor/、chore/ - 格式要求:kebab-case 格式,简洁反映改动内容
- 长度限制:不超过 50 字符
分支命名示例
# 功能开发
feature/add-user-auth-api
feature/implement-websocket-adapter
feature/support-modelscope-integration
# Bug 修复
bugfix/fix-memory-leak
bugfix/fix-type-error-in-mcp-service
bugfix/fix-connection-timeout-issue
# 文档更新
docs/update-readme-installation
docs/add-mcp-protocol-guide
# 测试相关
test/add-unit-tests-for-connection-manager
test-improve-test-coverage
# 重构
refactor-simplify-mcp-manager
refactor-optimize-path-alias-system
4. Commit 信息生成
生成符合代码评审友好的 commit 信息。
Commit 信息结构
<type>(<scope>): <subject>
## 改动说明
- 为什么改:<改动动机和问题背景>
- 改了什么:<具体的改动内容>
- 影响范围:<受影响的模块和兼容性>
- 验证方式:<测试方法或验证点>
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 326 lines · 10 tokens per session scan A 5c423b6c7110
commit is a skill published in the GitHub repository shenjingnan/xiaozhi-client (337 stars, last pushed 7d ago), licensed MIT. It adds 10 tokens to every session and 2,823 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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