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 eval-designgit 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/eval-design)<a href="https://agentmods.dev/skills/zts212653/clowder-ai/eval-design"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/eval-design/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/eval-design"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/eval-design.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 | $0.00093 | $0.05810 |
| Opus 5.5 | $0.00037 | $0.02324 |
| Sonnet 5.5 | $0.00019 | $0.01162 |
| Haiku 4.5 | $0.00009 | $0.00581 |
Grade A, and why
eval-design 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 today.
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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Design — E0 资格门 · 出生证契约 · 运行拓扑 · 设计自检 · 五病体检 · 干预证
版本定位(2026-08-23 v2.2 修正轮):v0.3 = v0.2 + E5 自评回灌禁区 + runway 记账成本 + 纵向 Eval 运行拓扑 + 裁决角色分权。上位宪法:
docs/architecture/eval-philosophy.md(七公理 E0–E6;v2.2 已 ratified)。 机制选择与落地边界以 ADR-031 v3.4 为准:按问题选机制, 不按层补齐。
Why This Is a Skill(价值门禁)
模型的训练先验里有"怎么算 accuracy",没有猫咖的 eval 宪法。本 skill 是 2026-07 Eval 宪法(七公理 E0–E6)+ Alden 三日思辨的操作化——未来任何猫给任何 harness 域 配 eval 时,用同一套流程,防止家里的 eval 资产继续长成划水/污染/归因停滞/ 干预失证/摸鱼五病混合物。 来源:docs/architecture/eval-philosophy.md(宪法 v2.2,七公理)+ feature-discussions/2026-07-17-eval-charter-draft.md(v1 历史草案,已演进归档)+ 2026-07-16-alden-dialogue-distillation.md(思辨蒸馏)。
与能力进化项目的往返
capability-evolution 负责一句话入口、对象/owner 绑定与已有 Program 的持续推进;本 skill 负责测量、校准、归因诊断与干预设计。需要跨对象承接时用它的方法导航,不复制旧案例的目标、阈值或授权。
- 从项目进入:带上当前 Program/对象/claim、已有版本与来源、权限和真实缺口,按问题选择本 skill 的相关部分;不强制每个项目从头填写全部表格。
- 设计完成后返回:交付本次相关的测量/角色/校准/触发或干预约定,明确哪些仍是草稿、哪些已由 owner 发布,以及证据仍允许或不允许什么结论。回到原 Program 的当前阶段,不重复 start。
- 正式写入分界:F311 只持 owner refs。出生证、proof、角色与 outcome 不能从本 skill 的回答中直接合成;Approval 和采用仍走原 owner/正式动作。证据不足不阻塞既有授权内的公开准备。
- 没有 Program 上下文:沿原指标设计/体检流程继续;使用
eval-design本身不要求创建 Evolution Program。
业务方只说“专业、省心、效果更好”也可能需要本 skill;先判断当前是否在设计可靠量尺,别把普通用法问答或单次交付都升级成 eval。信息很少时,仍交付本次能确定的 claim/观察单位/判断与取证草案,逐项说明 GT 域、来源与采集方式、裁判及付薪方,并把校准者、阈值、成本或数据缺口保持为未知。下方 E0 与出生证约束正式资格;草案不能自称已过 E0,也不能拿几条业务形容词代替实际应用方法。
核心定义:一个 eval 指标到底是什么
一个 eval 指标 = 一个赌注:"这个数字的变动方向,与我们真实在乎的东西的变动 方向一致。" 它不是测量值本身,是"测量值 → 真实效用"的映射假设。假设会失效 (分布漂移)、会被破坏(优化压力)、需要验证(校准)——所以指标有生命周期。
〇、E0 资格门(建前先过——三问任一答不出即不发牌照,宪法 E0)
- claim 落在哪个 GT 域——同一产物的不同 claim 落在不同域(测试判得了 "符合规约",判不了"是人要的产品"),禁止拿产物整体贴单一域标签;
- 判定所需的新鲜 bit 在哪——已在系统可观察边界内(verifier 射程:边际 判分成本≈0、可自动重复)/ 在冻结先验里(judge 射程:便宜但有额度)/ 只在 价值主人脑中与真实后果里(必须外采)。裁判类型由 claim 的域决定,不由 预算决定;
- 裁判的工资谁在付——verifier=测试维护投入;judge=冻结先验额度 + calibration runway 的记账/人锚抽样成本(见出生证);价值主人=真实关系与打扰 预算。答不出付薪方=环转不久。若连 runway 都付不起,结论是缩小或不建 judge 环, 不是省掉测量后继续宣称它可持续。
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.
- yesterday Changed 54c547c4075c
- 14d ago Changed · +6 lines d5331d94a3bf
- 1mo ago Changed · +23 lines · +3 tokens per session 0557fc73fb48
- 1mo ago First seen · 211 lines · 90 tokens per session scan A 00a8dbaf3441
eval-design is a skill published in the GitHub repository zts212653/clowder-ai (3,471 stars, last pushed today), licensed MIT. It adds 93 tokens to every session and 5,810 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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