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 instructions/carguo/gsygithubapp/agents-mdgit clone --depth 1 https://github.com/CarGuo/GSYGithubAPPWhat 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 | $0.01542 | $0.01542 |
| Opus 5 | $0.00771 | $0.00771 |
| Sonnet 5 | $0.00308 | $0.00308 |
| Haiku 4.5 | $0.00154 | $0.00154 |
Grade A, and why
GSYGithubAPP AGENTS.md 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — GSYGithubApp AI 协作守则
该文件是面向 AI 协作（Trae / Cursor / Copilot 等）和团队成员的统一入口。 所有 AI 在改动本仓库前 必须 先阅读本文件以及 harness/README.md。
1. 项目速览
- 应用：GSYGithubApp，跨平台 GitHub 客户端（React Native）。
- 当前栈：React Native
0.80.2、React19.1.0、Redux + redux-thunk、React Navigation v7、Realm 20、Hermes、新架构（Fabric + TurboModules）已开启。 - 入口：index.js → App.js → AppNavigator.js。
- 原生壳：Android 见 android/app/build.gradle，iOS 见 ios/Podfile。
- 历史路由：app/router.js 仅作历史参考，实际路由是 React Navigation。
2. AI 工程化目录
所有架构 / 需求 / 决策 / 测试 / 回归 / 升级手册都沉淀在 harness/：
| 域 | 路径 | 说明 |
|---|---|---|
| 总入口 | harness/README.md | 工程化总览 |
| 架构 | harness/architecture/ | 系统架构、模块、数据流、原生桥 |
| 需求 | harness/requirements/ | 按域拆分的功能需求 |
| 决策 | harness/decisions/ | ADR（Architecture Decision Records） |
| 迭代 | harness/iteration/ | AI 改动日志、版本节奏 |
| 测试 | harness/testing/ | 测试矩阵、Jest/RNTL/E2E/手工 |
| 回归 | harness/regression/ | release-gate checklist |
| 手册 | harness/playbooks/ | RN 升级、依赖升级等 SOP |
3. AI 改动 SOP（强制）
- 先读再改：开工前阅读相关 harness/architecture、harness/requirements 与历史 harness/iteration/CHANGELOG-AI.md。
- 写计划：复杂任务先用 TodoWrite 列计划，并参照 harness/playbooks 的现成 SOP。
- 小步快跑：单次改动尽量聚焦一个模块；跨层改动需写 ADR 放入 harness/decisions。
- 沉淀测试：
- 纯逻辑（utils/dao/store）→ Jest 单测，路径
__tests__/unit/<module>.test.js。 - UI 组件 → React Native Testing Library，路径
__tests__/components/<Page>.test.js。 - 关键链路 → E2E 用例，沉淀到 harness/testing/e2e/ 与
e2e/。 - 无法自动化的操作 → 写入 harness/testing/manual/ 的 Markdown 用例。
- 纯逻辑（utils/dao/store）→ Jest 单测，路径
- 写日志：完成后追加一条记录到 harness/iteration/CHANGELOG-AI.md。
- 过 gate：发布或合并前跑完 harness/regression/checklist.md。
- 打 tag 前必跑 release 包验证（v5.0.0 硬规矩）：任何
git tag+git push origin <tag>触发 CI 发版前，必须先在本地完整跑过 harness/regression/checklist.md §8（patch 体检 +assembleRelease装机闭环 + 混淆/R8 场景）。禁止"先打 tag 让 CI 兜底"——CI 默认走 bundleRelease 会静默吞 patch / 资源 / autolinking / R8 错误，导致 tag 已挂出却无可发资产。混淆开启时尤其关键：R8 删类/改名/反射剥离的崩溃只在 release 运行时暴露。
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 · 65 lines · 1,542 tokens per session scan A 84b363b76d6f
GSYGithubAPP AGENTS.md is an instructions file published in the GitHub repository CarGuo/GSYGithubAPP (2,491 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 1,542 tokens to every session, about $0.0077 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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