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 receive-reviewgit 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/receive-review)<a href="https://agentmods.dev/skills/zts212653/clowder-ai/receive-review"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/receive-review/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/receive-review"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/receive-review.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 | $0.00058 | $0.04383 |
| Opus 5 | $0.00029 | $0.02191 |
| Sonnet 5 | $0.00012 | $0.00877 |
| Haiku 4.5 | $0.00006 | $0.00438 |
Grade A, and why
receive-review 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 5d 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 — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SOP 位置: 本 skill 是
sop-definitions/development.yamlstagereview的反馈处理执行细节。 上一步:request-review| 下一步:merge-gate
Receive Review
处理 reviewer 反馈的完整流程。核心原则：技术正确性 > 社交舒适，验证后再实现，禁止表演性同意。
触发入口
| 来源 | 说明 |
|---|---|
| operator/猫猫转述 | 手动告知 review 结果 |
github-review-feedback connector 通知 |
F140 自动投递：review decisions（approved/changes_requested）+ inline/conversation comments |
| 云端 Codex review | 通过 ReviewRouter 投递的 email review 结果 |
收到 github-review-feedback 通知时，按下面的核心知识处理——不区分来源，只区分反馈类型。
自动触发处理（F140 Phase B）
当 github-review-feedback connector 唤醒你时：
- 读取通知内容，识别 review decision 类型
CHANGES_REQUESTED→ 直接进入下方 Red→Green 流程APPROVED→ 不需要 receive-review，检查是否可以走 merge-gateCOMMENTED→ 判断是否需要代码修改，需要则进入 Red→Green 流程- 处理完成后通知operator结果（KD-13: 事后通知）
详见 ../.cat-cafe-shared-refs/pr-signals.md Phase B 自动响应行为。
核心知识
两类反馈，处理方式不同
| 类型 | 特征 | 处理 |
|---|---|---|
| 代码级 | bug / edge case / 性能 / 命名 | Red→Green 修复流程 |
| 愿景级 | "这不是operator要的" / "缺了多项目管理" / "UI 不可用" | STOP → 回读原始需求 → 升级operator |
愿景级反馈不能用代码 patch 修补设计问题。 先对照operator experience验证 reviewer 说得对吗；如确实偏离，升级operator确认偏差范围，再重新设计。
Reviewer Delta Annotation（F253 AC-B2）
当 review request 附有 Fresh-Context Findings 节时，reviewer 在自己的 findings 中标注 delta tag，量化 cross-model review 增值：
| Tag | 含义 | 用途 |
|---|---|---|
[FC:covered] |
该 finding 已被 fresh-context 发现 | 量化 fresh-context 覆盖率 |
[FC:new] |
该 finding 是 fresh-context 未发现的新发现 | 量化正式 reviewer 增值（reviewer delta metric） |
[FC:N/A] |
该 finding 不适用 delta 标注（如愿景级/架构级） | 排除非代码 finding |
Annotation 格式：在 finding 行末加 tag
P2-1: 边界条件未处理 — src/foo.ts:42 [FC:covered]
P1-1: Race condition in concurrent writes — src/bar.ts:18 [FC:new]
P3-1: 建议重新考虑整体架构方向 [FC:N/A]
注意：
- 标注是 lightweight annotation，不增加 review 流程摩擦
- Review request 无 Fresh-Context Findings 节时（未触发 fresh-context），不标注
- Delta 数据自然累积在 review 记录中，Phase C
eval:qc聚合分析 - 标注不影响 finding 的 severity 判定或处理流程
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.
- 5d ago Changed · -3 lines a0a7178a9fb0
- 10d ago First seen · 303 lines · 58 tokens per session scan A 7d22756095d9
receive-review is a skill published in the GitHub repository zts212653/clowder-ai (2,956 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 4,383 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.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.