Clowder AI is a self-hosted workspace where AI agents from different model families work together as a persistent team, retaining identities, shared evidence, and memory across tasks. It is for people who want to coordinate multiple AI agents without repeatedly rebuilding their context.
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 zts212653/clowder-ai --skill knowledge-engineeringgit clone --depth 1 https://github.com/zts212653/clowder-aiWrote 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/zts212653/clowder-ai/knowledge-engineering)<a href="https://agentmods.dev/skills/zts212653/clowder-ai/knowledge-engineering"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/knowledge-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zts212653/clowder-ai/knowledge-engineering"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/knowledge-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00109 | $0.03470 |
| Opus 5.5 | $0.00044 | $0.01388 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (3,471 stars, last pushed today), licensed MIT. It adds 109 tokens to every session and 3,470 once invoked, about $0.0004 per session on Opus 5.5. 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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