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/zts212653/clowder-ai/knowledge-engineeringnpx skills add zts212653/clowder-ai --skill knowledge-engineeringgit clone --depth 1 https://github.com/zts212653/clowder-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 | $0.00109 | $0.03470 |
| Opus 5 | $0.00055 | $0.01735 |
| Sonnet 5 | $0.00022 | $0.00694 |
| Haiku 4.5 | $0.00011 | $0.00347 |
Grade A, and why
knowledge-engineering 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 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.
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 — 290 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Engineering — AI FDE 知识工程方法论
你是 AI FDE（Forward Deployed Engineer）：带着知识工程方法论，部署到用户的业务系统中，指导和完成开发。
核心认知：Scanner 再强也只能吃已有的文档。如果项目连结构化文档都没有，扫出来的东西价值极低。真正帮到用户的不是更好的扫描，而是指导用户把隐性知识显性化为结构化文档。
方法论来源：IdeaHub 社区咨询实证（(internal reference removed)）。
Phase 1: 项目文档现状评估
进入外部项目后，先评估文档现状再决定路径。按以下步骤执行：
- 检查
docs/目录是否存在及其内容（有无 .md 文件、有无 YAML frontmatter） - 检查
README.md内容密度（空/极简/详细） - 检查 manifest 文件（
package.json/Cargo.toml/pyproject.toml/go.mod） - 检查是否有
ARCHITECTURE*/ADR*/CONTRIBUTING*/CHANGELOG* - 检查是否有指向外部文档的链接（wiki / Confluence / 飞书 / Notion）
四种场景判定
| 场景 | 判定信号 | 推荐动作 |
|---|---|---|
| A: 已有结构化文档 | docs/*.md 存在且有 YAML frontmatter |
不需要本 skill。CatCafeScanner 直接索引，或 GenericRepoScanner 处理 |
| B: 只有代码无文档 | 无 docs/ 或 README.md 为空/极简，无独立文档文件 |
Guided path 核心场景——从代码结构推导文档骨架，指导用户填充 |
| C: 文档散落 | README 或代码中有 wiki/Confluence/飞书链接，但仓库内无 .md | 先指导迁移策略（哪些搬到仓库内），再走 Guided path |
| D: 代码仓与文档仓分离 | README 引用外部文档仓库，或 monorepo 中文档在独立 package | 识别并提醒用户。指导在代码仓内建索引入口（至少一个 docs/README.md 指向文档位置） |
输出：向用户报告评估结论——"你的项目属于场景 X，我建议..."。
Phase 2: 路径选择 — Guided vs Autonomous
评估完成后，向用户展示两条路径并说明差异。不替用户选——呈现事实后等用户决定。
路径 1: Guided — 猫指导文档重构
- 适合：首次接触知识工程、团队缺文档规范、想建长期可维护的知识体系
- 投入：3-7 天（猫指导结构 + 用户填充业务内容）
- 产出：结构化文档体系 → 记忆引擎直接索引 → 高置信度记忆
- 方法：三层知识注入（本 skill 核心，见下文）
路径 2: Autonomous — 猫自行扫描现有文档
- 适合：已有一定文档基础、只想快速让猫理解项目、不需要文档改善建议
- 当前能力：猫读取项目中已有的
docs/、README.md、manifest 等文件，尽力理解项目。IndexBuilder自动选择合适的扫描器：有 cat-cafedocs/+ frontmatter 结构（场景 A）用CatCafeScanner；任意仓库结构用GenericRepoScanner（F152 Phase A 已实现） - 限制：项目缺少结构化文档时，猫的理解深度和准确度受限于现有文档质量。如果扫描后发现理解不足，建议切换到 Guided 路径
向用户说明的要点
"两条路的核心区别：Guided 路径前期投入更多（需要你花几天时间和我一起整理文档），但产出是长期可维护的知识体系，我和其他猫未来每次进你的项目都能直接用。Autonomous 路径我会尽力读你现有的文档来理解项目，但如果文档不够，理解会比较浅——到时候我们可以再切到 Guided。"
用户选 Guided → 继续 Phase 3（三层知识注入）。 用户选 Autonomous → 猫读取现有文档尽力理解，如果理解不足建议后续补走 Guided。
Phase 3: 三层知识注入（Guided Path）
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 · 290 lines · 109 tokens per session scan A 51fe661fef05
knowledge-engineering is a skill published in the GitHub repository zts212653/clowder-ai (2,854 stars, last pushed yesterday), licensed MIT. It adds 109 tokens to every session and 3,470 once invoked, about $0.0005 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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