Borrowing it
Nothing to install: this file belongs to shenjingnan/home-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/shenjingnan/home-mcp/main/.claude/commands/fix-audit.mdgit clone --depth 1 https://github.com/shenjingnan/home-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/shenjingnan/home-mcp/fix-audit)<a href="https://agentmods.dev/commands/shenjingnan/home-mcp/fix-audit"><img src="https://agentmods.dev/badge/commands/shenjingnan/home-mcp/fix-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/shenjingnan/home-mcp/fix-audit"><img src="https://agentmods.dev/badge/commands/shenjingnan/home-mcp/fix-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00003 | $0.00328 |
| Opus 5 | $0.00002 | $0.00164 |
| Sonnet 5 | $0.00001 | $0.00066 |
| Haiku 4.5 | $0.00000 | $0.00033 |
Grade A, and why
fix-audit 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 10d 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.
What it actually says
安全审计
请执行命令 pnpm audit --audit-level moderate 如果发现依赖需要更新，请帮我处理
完成以下步骤
- 执行 pnpm audit --audit-level moderate 检查依赖安全问题
- 分析审计结果并识别需要更新的依赖
- 更新有安全问题的依赖包
- 验证更新后的依赖是否正常工作
依赖更新策略
- 自动修复: 对于有修复版本的依赖，使用 pnpm audit --fix 自动更新
- 手动更新: 对于需要手动处理的依赖，分析兼容性后进行版本更新
- 特殊处理: 对于可能影响项目功能的重大版本更新，需要谨慎处理
验证和测试
- 运行 pnpm install 更新依赖
- 执行 pnpm build:all 确保构建正常
- 运行 pnpm test 确保所有测试通过
- 执行 pnpm type:check 确保类型检查无错误
代码质量检查
- 运行 pnpm check:fix 修复代码格式问题
- 执行 pnpm spell:check 检查拼写
风险控制
- 在更新前会检查依赖的兼容性
- 优先选择安全修复而非重大功能更新
- 确保更新后项目功能完全正常
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.
- 10d ago First seen · 39 lines · 3 tokens per session scan A 13ca160b4699
fix-audit is a command published in the GitHub repository shenjingnan/home-mcp (21 stars, last pushed 7mo ago), licensed MIT. It adds 3 tokens to every session and 328 once invoked, about $0.0000 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.