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 organize-threadsgit 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/organize-threads)<a href="https://agentmods.dev/skills/zts212653/clowder-ai/organize-threads"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/organize-threads.svg" alt="Measured on agentmods" 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 | $0.00065 | $0.00734 |
| Opus 5 | $0.00032 | $0.00367 |
| Sonnet 5 | $0.00013 | $0.00147 |
| Haiku 4.5 | $0.00006 | $0.00073 |
Grade A, and why
organize-threads 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 8d 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.
What it actually says
Organize Threads
用户请求整理未分类 thread 时加载此 skill。分析 thread 标题和元数据,对照可用标签建议分类。
流程
1. 获取数据
- 用 cat_cafe_list_labels 获取可用标签列表(id + name + color)
- 用 cat_cafe_list_threads 获取 thread 列表
- 如果用户触发消息中已附带标签和 thread 数据,优先使用(减少工具调用)
- 筛选出未分类 thread(labels 为空或不存在的)
2. 分析 thread
- 逐个分析 thread 标题
- 语义匹配:标题含义和标签含义的对应(不是简单 substring)
- 一个 thread 可匹配 0-N 个标签
- 无法判断的 thread 不强行分类
3. 输出建议
- 按 thread 逐条列出建议的标签
- 简要说明匹配理由(一句话)
- 附带机器可读 JSON 块(供前端 modal 预填充)
- 不自动应用——等用户在 modal 中确认
输出格式
有标签时(标准格式)
当触发消息列出了可用标签,使用标签 ID:
## 分类建议
| Thread | 建议标签 | 理由 |
|--------|----------|------|
| {title} | {label names} | {一句话说明} |
<!-- SUGGESTIONS_JSON:{"threadId1":["labelId1","labelId2"],"threadId2":["labelId3"]} -->
key = threadId,value = labelId 数组。必须使用 id 而非 name。
无标签时(扩展格式)
当触发消息说明"当前没有任何标签",先建议标签体系再分类:
## 建议标签体系
| 标签名 | 颜色 | 说明 |
|--------|------|------|
| {name} | {color} | {一句话说明用途} |
## 分类建议
| Thread | 建议标签 | 理由 |
|--------|----------|------|
| {title} | {label names} | {一句话说明} |
<!-- SUGGESTIONS_JSON:{"newLabels":[{"name":"标签名","color":"#hex"}],"assignments":{"threadId1":["标签名"]}} -->
newLabels = 建议创建的标签(名称+颜色),assignments = 每个 thread 建议的标签名数组(用名称不用 ID,因为标签尚未创建)。前端会在用户确认后自动创建标签并应用。
注意事项
- 有标签时只用已有标签,不发明新标签
- 无标签时建议 3-8 个标签,颜色用十六进制,名称简短
- 标题信息不足时,跳过该 thread(宁缺勿滥)
- 最多处理 50 个 thread(避免消息过长)
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.
- 8d ago First seen · 87 lines · 65 tokens per session scan A 0a59a51770bd
organize-threads is a skill published in the GitHub repository zts212653/clowder-ai (2,924 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 734 once invoked, about $0.0003 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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