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review-doc审查文档

Agent Skill

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

总安装

360

周安装

15

GitHub Stars

公开资料未说明

下载量

120
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:review-doc(审查文档)
来源仓库:https://github.com/vamdawn/ai-forge
仓库路径:skills/review-doc
安装命令:
npx skills add https://github.com/vamdawn/ai-forge --skill review-doc
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/vamdawn/ai-forge --skill review-doc

简介

review-doc 辅助文档、Markdown 和内容稿件的整理与改写。

  • 适合提炼结构、补齐章节、统一术语或检查链接有效性。
  • 使用时需保留项目已有事实和路径,不写成确定结论。review-doc 属于待分类类 Skill,可作为该场景下的辅助能力补充。
  • 涉及对外文案时需控制语气,避免过度营销或夸大能力。
  • 建议结合原始 README 核验具体用法和功能细节。

SKILL.md

Document Review

Workflow

  1. Read target document — Read the ENTIRE file at $ARGUMENTS in one pass. Note total line count.
  2. Read referenced documents — Scan for mentions of PRDs, design docs, specs, or style guides. Read each one for context. If none found, proceed without.
  3. Choose review mode — Based on document size:

- ≤300 lines → Single-agent mode (Step 4a) - >300 lines → Parallel multi-agent mode (Step 4b)

4a. Single-Agent Review

Spawn ONE review sub-agent via Task tool (subagent_type: "general-purpose"). Pass full document content and all referenced context. Instruct the sub-agent to:

  • Check correctness, structure, completeness, clarity, and grammar
  • Categorize each issue as Critical / Important / Minor
  • Return a markdown table: Severity | Line | Category | Issue | Fix
  • Sort by severity (Critical first), then line number

4b. Parallel Multi-Agent Review

For documents >300 lines, split and review concurrently:

  1. Partition the document — Divide by top-level headings (h1/h2) into logical sections. If no headings exist, split into chunks of ~300 lines with 20-line overlap to catch cross-boundary issues. Target 2–5 chunks; never exceed 5.
  2. Spawn parallel sub-agents — Launch ALL sub-agents in a single message using multiple Task tool calls (subagent_type: "general-purpose"). Each sub-agent receives:

- Its assigned section/chunk (with line number range clearly stated) - The full list of referenced documents for context - A brief summary of the rest of the document (surrounding section titles + first sentence) so it can assess cross-references - Instructions to check: correctness, structure, completeness, clarity, grammar - Instructions to return a markdown table: Severity | Line | Category | Issue | Fix

  1. Spawn a structure sub-agent in parallel — In the same message as Step 2, launch one additional sub-agent that reviews the document holistically for:

- Overall structure and logical flow - Missing sections or content gaps - Inconsistencies between sections (terminology, style, tone) - Duplicate or contradictory content - Table of contents accuracy (if present)

  1. Merge results — Collect all sub-agent tables. Deduplicate issues that appear in overlapping regions (keep the more specific description). Combine into one unified table sorted by severity then line number.

5. Apply fixes

Apply ALL fixes in one pass using Edit tool.

6. Verify

Re-read the document. If new issues remain, do ONE more fix pass maximum.

7. Report

Output summary to the user using the format below.

Output Format

## Document Review: [filename]

### Review Mode
[Single-agent | Parallel (N section agents + 1 structure agent)]

### Issues Found
| Severity | Line | Category | Source | Issue | Fix Applied |
|----------|------|----------|--------|-------|-------------|
| Critical | 42   | Correctness | Section 2 | Wrong endpoint URL | ✅ |
| Important | 128  | Structure | Holistic | Missing error handling section | ✅ |

### Summary
- Critical: N found, N fixed
- Important: N found, N fixed
- Minor: N found, N fixed
- Passes: 1 | Status: ✅ Complete

Edge Cases

  • >1000 lines: Read in 500-line chunks, complete full read before review.
  • No clear section boundaries: Fall back to fixed-size ~300-line chunks with 20-line overlap.
  • No issues found: Report clean result — do not invent issues.
  • User specifies focus area: Prioritize that area but still do a full review pass.
  • File not found or unreadable: Report error to user and terminate — do not proceed with review.
  • Sub-agent failure: If one parallel sub-agent fails, merge results from successful ones and log a warning. If all fail, fall back to reviewing directly in main session.
  • Referenced document unreadable: Log a warning in the report and continue review without that reference.
  • Overlapping issues from parallel agents: During merge, deduplicate by line number proximity (±3 lines) and category match — keep the entry with the more actionable fix description.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.55%
按下载量换算41

Claude

32.55%
按下载量换算39

Cursor

18.1%
按下载量换算22

Gemini CLI

8.98%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/vamdawn/ai-forge --skill review-doc 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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