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/liyecom/liye-ai/github-digestnpx skills add liyecom/liye-ai --skill github-digestgit clone --depth 1 https://github.com/liyecom/liye-aiWhat 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 | $0.00238 | $0.02360 |
| Opus 5 | $0.00119 | $0.01180 |
| Sonnet 5 | $0.00048 | $0.00472 |
| Haiku 4.5 | $0.00024 | $0.00236 |
Grade C, and why
github-digest scanned grade C with 1 finding 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 3d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- `忽略 (ignore)` — 确认没用 → **当下即删**：`rm -rf ~/.liye-os/github-digest/clones/<repo-slug>`。 How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Digestion — 进化情报侦察
给定一个已知的 GitHub repo，把它读懂，判断对 LiYe Systems 进化有没有可借鉴之处，
产出一份定型结构报告并归档。它回答的是"这东西值不值得我们上心"——站在
github-scout（发现未知 repo）和 source-intake（受控 intake 已选 repo）的上游。
这个 skill 决定不了复用。 它只出 advisory 报告 + verdict。任何真实复用都要走 harvest-ADR / Reference Declaration 仪式（SYSTEMS.md Fork 纪律）。
为什么用 subagent 而不是主线直接读
一个真实 repo 读下来是 50–80k tokens（README + 目录 + 关键源码）。默认 dispatch 一个 只读研究 subagent 去啃，让它只回传定型报告——主线上下文不被源码淹没，且每份报告长同一个样、 可横向比较。只有当 repo 极小（几个文件）时才值得主线内联。
工作流
- 接收输入：repo URL（必需）+ 可选的关注问题。从 URL 解析
<owner>/<repo>与<repo-slug>。 - Dispatch 研究 subagent：用
references/subagent-prompt.md作模板，填入目标 URL、 clone 目标路径、关注问题。subagent 负责只读获取 + clean-room 读 + 按模板出报告。 - 落盘归档：把 subagent 回传的报告写成
docs/methodology/01_Research_Intelligence/recon-log/<YYYY-MM-DD>-<repo-slug>.md（日期用当前实际日期），并在recon-log/README.md的索引表顶部加一行 （日期 / repo / verdict emoji / LiYe 层 / 一句话）。 - 应用清理策略（见下）：按 verdict 处置克隆。
- 回主线汇报：verdict-first 摘要（结论 + 最相关的 2–3 点 + 许可风险标记 + 下一步）。
报告结构与 frontmatter 见 references/recon-report-template.md——严格按它产出，
否则历史条目无法横向比较。
克隆位置与生命周期（清理策略）
- 克隆到 repo 外、持久目录：
~/.liye-os/github-digest/clones/<repo-slug>/（git clone --depth 1）。绝不克隆进 liye_os / 任何工作仓，绝不 fork / vendor。 （持久目录而非 session scratchpad，因为 watch 的克隆要留到价值兑现。） - 跑完按 verdict 处置克隆：
忽略 (ignore)— 确认没用 → 当下即删：rm -rf ~/.liye-os/github-digest/clones/<repo-slug>。值得观察 (watch)/harvest-adr 候选— 保留：在报告 frontmatter 记clone_path+clone_status: retained；留到该 repo 的价值兑现（整合进 LiYe systems / 走完 harvest-ADR）或被判定不再有用，那时才删。
- 删克隆是 clean-room 收尾，不是 governance 操作；但删 watch 克隆前要确认其价值已了结。
Governance 硬边界（永远不替用户越过）
- 只读 / clean-room：可以理解概念，绝不逐字誊抄源码进报告或任何 LiYe 仓。
- 强 copyleft 是硬毒药：GPL/AGPL 等即使 verdict=watch，也只能概念参照、clean-room
重实现，绝不阅读其源码后照抄；frontmatter 必标
license_caution。 - 不 fork / 不 clone 进工作仓 / 不 vendor / 不建运行时依赖——surface，别 act。
- 任何复用走 harvest-ADR / Reference Declaration（reimplement + ≥3 scenarios）。
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 141 lines · 238 tokens per session scan C 03dff4b034bf
github-digest is a skill published in the GitHub repository liyecom/liye-ai (33 stars, last pushed 23d ago), licensed Apache-2.0. It adds 238 tokens to every session and 2,360 once invoked, about $0.0012 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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