Borrowing it
Nothing to install: this file belongs to ThreeFish-AI/negentropy. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ThreeFish-AI/negentropy/master/.agent/skills/doc-review/SKILL.mdgit clone --depth 1 https://github.com/ThreeFish-AI/negentropyWrote 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/threefish-ai/negentropy/doc-review)<a href="https://agentmods.dev/skills/threefish-ai/negentropy/doc-review"><img src="https://agentmods.dev/badge/skills/threefish-ai/negentropy/doc-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/threefish-ai/negentropy/doc-review"><img src="https://agentmods.dev/badge/skills/threefish-ai/negentropy/doc-review.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 | $0.00041 | $0.01393 |
| Opus 5 | $0.00020 | $0.00696 |
| Sonnet 5 | $0.00008 | $0.00279 |
| Haiku 4.5 | $0.00004 | $0.00139 |
Grade A, and why
doc-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 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.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document Review Skill
本 Skill 旨在为文档提供标准化、全维度的审查与精调服务。不仅要发现问题，更要解决问题。通过系统性思维与架构视角审查文档，并在确认优化方案后，主动将精调后的版本直接落实到目标文档中，确保文档质量的实质性提升。
核心审查点
依据 AGENTS.md 的工程行为准则，从以下五个正交维度对文档进行深度审计：
1. 系统性 (Systemic Integrity)
- 全局视角：文档是否建立了与项目全景（Pulse, Hippocampus, Perception, Realm of Mind）的链接？
- 涟漪效应：是否评估了文档变更对上下游（如架构图、API 定义、测试用例）的潜在影响？
- 上下文锚定：内容是否基于 CDD (Context-Driven Development) 构建，而非孤立的描述？
2. 正交性 (Orthogonality)
- 关注点分离：是否清晰界定了“为什么 (Why)”、“是什么 (What)”与“怎么做 (How)”？
- 概念独立：各章节是否作为一个独立的概念主体存在？修改一处是否需要联动修改多处（High Coupling）？
- 去冗余：遵循 DRY (Don't Repeat Yourself) 原则，引用单一事实来源 (Single Source of Truth) 而非重复定义。
3. 顺序自洽 (Sequential Consistency)
- 逻辑流：阅读顺序是否符合认知规律（如：背景 -> 目标 -> 方案 -> 验证）？
- 因果链：推论是否由前置事实严谨推导得出？是否存在突兀的跳跃？
- 术语一致性：核心名词与动词在全文及跨文档间是否保持严格一致？
4. 完整性 (Completeness)
- 闭环验证：是否涵盖了“设计-实现-验证”的全链路？是否有明确的测试标准或 SOP？
- 边界覆盖：是否讨论了限制条件、边缘情况 (Edge Cases) 与故障模式？
- 循证工程：关键决策是否提供了背景引用或 IEEE 格式的参考文献？
5. 直观性 (Intuitiveness)
- 图文并茂：复杂逻辑是否通过 Mermaid 图表（时序图、流程图、类图）进行了可视化降维？
- 视觉层级：标题、列表、引用块的使用是否构建了清晰的信息层级？
- 代码规范：代码示例是否完整、可运行，并符合 Vibe Coding Pipeline 标准？
审查流程
Step 1: 全景扫描 (Panorama Scan)
- 范围定义：明确文档的受众、核心目标与边界。
- 体量评估：检查字数、章节深度，评估是否需要拆分（如果层级 > 4 或字数 > 1万字）。
- 预检清单：执行元数据检查、语言规范检查与 Checksums 验证。
Step 2: 骨架评估 (Skeleton Assessment)
- TOC 分析：提取目录树，验证是否符合 MECE 原则（完全穷尽，相互独立）。
- 叙事流验证：检查顶级章节的排序逻辑（如：Input -> Process -> Output 或 Context -> Strategy -> Tactics）。
- 认知负载检查：识别过于臃肿的章节（Fat Sections），建议拆分或重组。
Step 3: 内容一致性与正交性 (Consistency & Orthogonality)
- 概念解耦：检查章节间是否存在强耦合（改A需改B），建议正交化重构。
- 事实核对 (CDD)：交叉验证文档内容与代码库 (
src/) 及架构标准 (AGENTS.md) 的一致性。 - 术语标准化：扫描全文，确保专有名词（如 "Hippocampus", "Perception"）定义的唯一性与一致性。
Step 4: 熵减与精炼 (Entropy Reduction)
- 噪声过滤：删除冗余的修饰语、过时的描述和重复的定义。
- 信噪比优化：将大段文本转换为列表、表格或 Mermaid 图表。
- 代码规范：验证代码块是否完整、可运行，并移除无关的 Log 或注释。
Step 5: 导航与交互体验 (Navigation & UX)
- 视觉层级：检查标题、引用块、Alerts 的层级关系是否清晰。
- 路标系统：验证锚点链接（Inter-links）与外部引用（References）的有效性。
- 图表审查：确保所有 Mermaid 图表在 Dark Mode 下清晰可见，且具备完整的图例。
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 · 88 lines · 41 tokens per session scan A 36757ccd3168
doc-review is a skill published in the GitHub repository ThreeFish-AI/negentropy (10 stars, last pushed 2d ago), licensed Apache-2.0. It adds 41 tokens to every session and 1,393 once invoked, about $0.0002 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 2026-08-31.
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