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 memory-search-best-practicesgit 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/memory-search-best-practices)<a href="https://agentmods.dev/skills/zts212653/clowder-ai/memory-search-best-practices"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/memory-search-best-practices/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/memory-search-best-practices"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/memory-search-best-practices.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 | $0.00296 | $0.03315 |
| Opus 5 | $0.00148 | $0.01657 |
| Sonnet 5 | $0.00059 | $0.00663 |
| Haiku 4.5 | $0.00030 | $0.00331 |
Grade A, and why
memory-search-best-practices 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 6d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Search Best Practices（多刀检索 + 全集召回）
单刀 top-k 不是全集；recall 任务需要 multi-query union + Read 原文 + 知道何时停。
核心命题
Query expansion 由 agent 做，不是系统做（KD-8 同源：dumb system + smart agent）：
- 系统不知道 "AUDHD" 在我们家关联 sensory gating / 2e / RSD / PDA — 那是领域知识
- Agent（LLM）有领域知识，但经常觉得"够了"就停（Ragdoll家族尤甚——2026-05-17 dogfood 实证：三猫搜同题各拿 10 条，全集需三猫合）
- 解法：教 agent 题型对应 recipe + 何时停下来判据，不在系统层加黑盒 expansion
单一 owner 闭环（检索切片不是用户要追的 feed）
多刀是同一只猫内部扩大召回的证据步骤，不是把一个结果拆成多份 custody：
- 一个召回目标只保留单一 owner，由它持有 union、去重、Read 原文与停止判据，最后一次终局交付。
- query、scope、语言或工具切片不得升级成需要用户分别跟踪的独立 feed、任务或半份报告。
- 可以在同一 invocation 内并行调用只读工具以缩短墙钟时间，但调用完成后仍由当前 owner 统一收敛；中间切片不向用户索要推进决定。
- 只有用户明确要求独立多猫观点，或子任务本身有不同、可独立验收的交付物时，才建立多 custody；这不属于普通 coverage 搜索。
8 类题型 → recipe
| 题型 | 例 query | Recipe（≥几刀几路） | 关键 |
|---|---|---|---|
| 是什么 | "F200 是什么" | 1 刀：search_evidence(query, hybrid, scope=docs) + Read top doc |
单刀够用 |
| 周边关系 | "F200 关联什么" | 1 刀：graph_resolve(anchor, depth=1, relations=[feature_ref,related_to]) |
配 relations filter 防 hub 爆炸 |
| 决策考古 | "为什么当时选 X 而不是 Y" | graph_resolve(anchor) → Read ADR/spec → 抽 thread anchor → get_thread_context 看原话 |
必 Read ADR 原文 + thread 原话 |
| 冷启动 onboard | "新猫接手 F200 要知道什么" | docs(hybrid) + graph + trajectories + Read spec | 三入口全用 |
| coverage 全集 | "哪些地方提过 X" | ≥3 刀：docs/hybrid + threads/semantic + agent expand 同义/缩写/中英二轮 + graph_resolve 命中 anchor 后追 source threads + union dedup | 不是单 top-k；agent 自己 expand |
| source-map / provenance | "X 这个想法的源头是哪个 thread" | canonical doc 命中后从文档抽 source thread ids → get_thread_context Read 原文 |
canonical doc 自带 provenance link，跟着走 |
| absence check | "我们提过 Y 没有" | 正反两路：search(Y) + search(Y 相关概念/反义) 都 0 命中才算 absent |
单刀 0 命中不等于不存在 |
| delta | "上次到现在 X 变了什么" | list_recent(scope=threads, since=N天) + 对比 graph 邻居增减 + Read 关键 diff。压缩恢复子场景起手：先看 TodoWrite + session digest 拿到"上次已知状态" → 再 list_recent 补增量（46 review P3 补） |
时间窗口 + 增量视角 |
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.
- 6d ago Changed · +8 lines · +4 tokens per session 918174ef29dd
- 10d ago First seen · 150 lines · 292 tokens per session scan A e26bd4207092
memory-search-best-practices is a skill published in the GitHub repository zts212653/clowder-ai (2,956 stars, last pushed today), licensed MIT. It adds 296 tokens to every session and 3,315 once invoked, about $0.0015 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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