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 skills add aAAaqwq/AGI-Super-Team --skill codex-cc-guidegit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/codex-cc-guide)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/codex-cc-guide"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/codex-cc-guide.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 | $0.00029 | $0.01220 |
| Opus 5 | $0.00015 | $0.00610 |
| Sonnet 5 | $0.00006 | $0.00244 |
| Haiku 4.5 | $0.00003 | $0.00122 |
Grade A, and why
codex-cc-guide 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 yesterday.
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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACP 调用 Claude Code / Codex 写代码指南
核心：sessions_spawn + runtime: "acp"
在 OpenClaw 里，agent 可以直接用 sessions_spawn 调起 Claude Code 或 Codex，写代码像发指令一样简单。
最常用：Claude Code（已装在 /usr/bin/claude）
sessions_spawn({
task: "用 TypeScript 重构 ~/project/src/api.ts，加入错误处理和类型定义",
runtime: "acp",
agentId: "claude", // 或 "claude-code"
label: "重构-api",
mode: "run", // run=一次性，session=持久会话
cleanup: "delete" // 完成后删除 session
})
Codex（需要额外配置）
sessions_spawn({
task: "写一个 Python FastAPI CRUD 接口",
runtime: "acp",
agentId: "codex",
model: "openai/gpt-5.4",
label: "fastapi-crud"
})
两种模式：一次性 vs 持久会话
| 模式 | 参数 | 适用场景 |
|---|---|---|
| 一次性 | mode: "run" |
单个任务，完成即删，最省资源 |
| 持久会话 | mode: "session" |
多步骤迭代、需要上下文累积 |
// 一次性：适合简单任务
sessions_spawn({
task: "在 /tmp 创建一个 React 组件 Counter.tsx",
runtime: "acp",
agentId: "claude",
mode: "run"
})
// 持久会话：适合复杂项目
sessions_spawn({
task: "继续上次的工作，修复登录页面的 bug",
runtime: "acp",
agentId: "claude",
mode: "session",
label: "react-project"
})
持久会话 + thread：绑定到 Telegram 话题
sessions_spawn({
task: "用 Next.js 14 App Router 重构整个电商前端",
runtime: "acp",
agentId: "claude",
thread: true, // 绑定到当前 Telegram thread
mode: "session",
label: "nextjs重构"
})
完成后 Claude Code 的输出会自动推送到 Telegram thread 里。
聊天指令（人类直接用）
在 Telegram 发这些指令即可：
/acp spawn claude 帮我写一个 Docker Compose 文件
/acp spawn codex 用 Rust 写一个 http server
/acp status 查看当前 ACP 会话状态
/acp cancel 取消当前任务
/acp close 关闭会话
小code 团队标准工作流
场景：Daniel 让你写一个 API
错误做法（慢、资源浪费）：
自己开模型 → 写代码 → 测试 → 修 bug → 循环
正确做法（用 ACP + Claude Code）：
1. 理解需求，确定技术栈和文件位置
2. sessions_spawn 调起 Claude Code 一次性任务
3. Claude Code 完成后你负责 review
4. 有问题再调一次 Claude Code 修
// 示例：Daniel 让小code 写一个用户认证 API
sessions_spawn({
task: `在 ~/clawd/projects/api/ 下创建用户认证模块：
- POST /auth/login（邮箱+密码，返回 JWT）
- POST /auth/register（邮箱+密码+昵称）
- GET /auth/me（返回当前用户信息，需带 Bearer token）
- 使用 Python FastAPI + SQLite
- 密码用 bcrypt 哈希
- JWT secret 从环境变量 AUTH_SECRET 读取
- 代码要可以运行，有完整的错误处理`,
runtime: "acp",
agentId: "claude",
label: "auth-api"
})
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.
- yesterday First seen · 156 lines · 29 tokens per session scan A 9a8393249808
codex-cc-guide is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed 3d ago), licensed MIT. It adds 29 tokens to every session and 1,220 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 2026-09-05.
Other skills, from other repositories
deslop
The optimization pass, defined - delete before you add, one smell class per pass, behaviour pinned by a test that ran BEFORE the edit. Lints a SKILL.md and prose by the same instinct. Use for the per-story optimization pass or when code has grown noisy without growing capable.
root-cause
Find the mechanism behind a failure instead of patching its symptom - reproduce first, one variable per experiment with the prediction written before the run, exit by naming the mechanism and pinning it with a failing test. Use for a bug, an unexplained red test, or a failure that will not reproduce.
code-tour
Maintain docs/code-tour.md — the annotated guided reading of Aigon's core logic. Use when you have changed code the tour quotes, added a subsystem a new reader would need, or the user says "update the code tour", "the tour is stale", "add X to the code tour", or asks to review/refresh the code examples doc.
aigon-next
Suggest the most likely next workflow action based on current context.
aigon-research-do
Do research - agent writes findings.
review-deep
Drive the deep-review phase of an automated PR review. Consumes the walkthrough, runs the deterministic deep-review workflow (parallel lenses → adversarial validation → code-enforced threshold/caps), drafts the surviving findings, and completes the review run.