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
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/open-source-teardown)<a href="https://agentmods.dev/skills/zts212653/clowder-ai/open-source-teardown"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/open-source-teardown/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/open-source-teardown"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/open-source-teardown.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00198 | $0.03495 |
| Opus 5 | $0.00099 | $0.01747 |
| Sonnet 5 | $0.00040 | $0.00699 |
| Haiku 4.5 | $0.00020 | $0.00349 |
Grade A, and why
open-source-teardown 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 9d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open Source Teardown
明星项目拆解不是“读 README 做竞品分析”，而是宣传 claim → 源码证据 → 能力边界 → 我们的 tradeoff 的审计流程。
When to Use
触发：operator看到 PPT、博客、README、社区讨论后问“这个项目是不是很强？”；需要拆解 agent runtime、skill 系统、memory/RAG、MCP/gateway、RL/eval、插件架构等工程系统；或需要把一次竞品分析沉淀为 lesson / ADR / skill。
排除：只需要查公开资料或论文综述用 deep-research；只需要处理外部 issue/PR/intake 用 opensource-ops；还没有明确目标项目、只是在讨论方向用 collaborative-thinking。
灰例：如果用户同时要求“查社区 issue 情报 + 读源码”，先用本 skill 建代码证据骨架，再按需补 deep-research 或 opensource-ops。
Required Output
默认落盘：feature-discussions/YYYY-MM-DD-{project}-deep-dive/，包含 README、architecture-map、明星特性深挖、comparison、lessons/next steps。
最小合格产物必须包含：
- source repo URL、local path、commit SHA、更新时间。
- 宣传 claims ledger：claim / evidence files / Source verdict / Decision fit / unknowns。
- 输入谱系与复现矩阵：paper / appendix / limitations / code / config / data / checkpoint / 本次环境，
写明版本、可得性和不一致处；没有实验 claim 时记
not applicable，不虚构 artifact。 - 原始输出与失败尾部审计：raw transcript/output/log、成功与失败样本、per-run/per-seed 稳定性；
没有运行 claim 时记
not applicable，不可得的 artifact 显式记unknown。 - 架构图或模块地图：entrypoints、state stores、extension points、empty dirs；用 ASCII tree 或 Mermaid，参考 (internal reference removed)。
- 明星特性深挖：每个特性都写到代码路径和运行链路。
- 算法剥皮表：真算法 / LLM judge / 启发式 / 规则 / 外部服务。
- Clowder AI 对比：能学、不能学、我们因为 tradeoff 不 follow 的理由。
报告模板见 refs/report-template.md；十二个审计镜头 + 命令见 refs/teardown-method.md；用户视角第一性原理见 refs/user-mind-evaluation.md。
进度纪律
- 分次推进：每只猫每次只做 1-2 份产物，commit 后传球，不一气呵成。
- 双视角交叉：架构/明星特性/合流/skill draft 至少跨两只猫完成。
- 对口 review：最终报告或 skill draft 必须由非作者猫 review，跨族优先。
Step 0 — 定边界和真相源
- 记录用户原始问题和最关心的 claims。
search_evidence查我们是否已有同项目/同类系统讨论、lesson、feature anchor；有矛盾就 flag。- clone 或 update 到
/home/user/projects/ref/{project}。 - 记录
git rev-parse HEAD、最新 tag/release、git status --short。 - 把 README/PPT/官网中的明星特性拆成 claims ledger。
- 若项目伴随论文或实验 claim，先读论文正文、附录、limitations、data/model card 和发布 artifact，再读 thread / 搬运文 / 总结；二手材料用于找争议和反例，不替代一手证据。
What ships with it
3 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.
- 9d ago First seen · 213 lines · 198 tokens per session scan A a34af582f8e6
open-source-teardown is a skill published in the GitHub repository zts212653/clowder-ai (2,940 stars, last pushed yesterday), licensed MIT. It adds 198 tokens to every session and 3,495 once invoked, about $0.0010 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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