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/jianchen08/agent-os-open/code-self-reviewnpx skills add jianchen08/Agent-os-open --skill code-self-reviewgit clone --depth 1 https://github.com/jianchen08/Agent-os-openWrote 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/skills/jianchen08/agent-os-open/code-self-review)<a href="https://agentmods.dev/skills/jianchen08/agent-os-open/code-self-review"><img src="https://agentmods.dev/badge/skills/jianchen08/agent-os-open/code-self-review.svg" alt="Measured on agentmods" 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 | $0.00065 | $0.00769 |
| Opus 5 | $0.00032 | $0.00385 |
| Sonnet 5 | $0.00013 | $0.00154 |
| Haiku 4.5 | $0.00006 | $0.00077 |
Grade A, and why
代码自审 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 today.
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
代码自审
本技能是流程指引，不是逐条打勾清单：按场景判断使用，遇不适用情况保持裁量，不机械执行。
用途
编码完成、提交 task_evaluate 之前，对本次改动的代码做机械化检查。只跑确定性检查（工具能客观判定，不依赖"第二双眼睛"的逻辑判断），目的是拦截低级错误，省去对小改动派独立审查的开销。
能力边界：本技能只覆盖机械检查。需求追溯、架构边界四问、AC 符合度判断仍需独立 code_reviewer_agent。
检查项（物理保险 5 项）
| 检查项 | 怎么查 | 判定 |
|---|---|---|
| 模块边界物理化 | 检查是否有跨模块非法导入（绕过公共接口直接访问内部实现，如 import _ 前缀函数、直接访问内部属性） |
有则失败 |
| 架构约束测试 | 检查模块间依赖方向是否正确，是否存在循环依赖（可用 lsp_references / grep import 链） | 有循环则失败 |
| 需求覆盖扫描 | 检查新增代码是否关联了需求来源（AC编号或需求描述），识别无需求的孤儿代码 | 有孤儿代码则失败 |
| 安全与风格Lint | 运行 ruff/flake8/mypy，检查空 catch、遗留 print/console.log、无需求 TODO、硬编码密钥 | 有 error 级则失败 |
| 冗余模式检测 | 检测重复代码、翻译式注释、无效错误处理、死代码 | 有则失败 |
执行流程
- 用
bash_execute跑 lint/类型检查工具（ruff、mypy 等） - 用
enhanced_search/lsp_references检查跨模块导入和依赖方向 - 用
enhanced_search扫描无需求 TODO、硬编码密钥、遗留 print - 汇总结果，填写执行报告模板的「自审结论」章节
产出（填入执行报告）
self_review:
status: pass | fail # 任一物理保险失败 = fail
module_boundary: {status: pass|fail, violations: []}
architecture: {status: pass|fail, cycles: []}
security_lint: {status: pass|fail, issues: []}
redundancy: {status: pass|fail}
must_fix_before_submit: [] # status=fail 时必填，列出必须修的问题
规则
status=fail时，must_fix_before_submit中的问题必须全部修复后才能调用 task_evaluate- 自审结果写入执行报告的「自审结论」章节，供编排器核对
- 自审只覆盖机械检查；本任务是否还需独立 code_reviewer_agent，由执行 Agent 根据风险分级标签判断（见执行报告模板）
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.
- today Changed · +2 lines 7d09bd215975
- 5d ago First seen · 48 lines · 65 tokens per session scan A 4e87deadee62
代码自审 is a skill published in the GitHub repository jianchen08/Agent-os-open (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 65 tokens to every session and 769 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
background-task
Add or modify work that runs outside the request/response cycle — emails, document ingestion, webhooks, cleanups, scheduled jobs. Use when something is slow or fire-and-forget, or when adding a periodic/cron task. This project's queue is {{ cookiecutter.backgroundtasks }}.
agent-tool
Add a new tool/function the AI agent can call (e.g. look something up, hit an external API, perform an action). Use when extending the assistant's capabilities, wiring a new function into the agent, or when the model needs a new action. This project uses {{ cookiecutter.aiframework }}.
frontend-feature
Build a new page, view, or data-driven feature in the Next.js frontend. Use when adding a route under the dashboard/marketing area, wiring UI to a backend endpoint, adding client state, or creating a localized page. Covers App Router, data fetching, Zustand stores, and i18n.
rag-knowledge
Work with the RAG knowledge base — ingest documents, run semantic search, manage collections, or add a sync source/connector (Google Drive, S3). Use when populating or debugging the knowledge base, tuning retrieval, or adding a new document source. This project uses {{ cookiecutter.vectorstore }} + {{…
alembic-migration
Create, review, and apply database schema changes with Alembic. Use whenever a SQLAlchemy model is added or changed, a column/index/constraint needs to change, or a data backfill is required — anything that alters the PostgreSQL schema.
channel-bot
Work with messaging-channel bots (Telegram / Slack) — register a bot, route inbound messages through the AI agent, handle webhooks vs polling, or add a new channel adapter. Use when wiring chat into a messaging platform or debugging bot delivery.