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proposal-review提案审查

Agent Skill

proposal-review 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,011

周安装

43

GitHub Stars

4

下载量

354
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

3

许可证

MIT

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:proposal-review(提案审查)
来源仓库:https://github.com/petekp/agent-skills
仓库路径:skills/proposal-review
安装命令:
npx skills add https://github.com/petekp/agent-skills --skill proposal-review
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/petekp/agent-skills --skill proposal-review

简介

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。
  • 通过 npx skills add 命令从指定仓库安装并使用。
  • 需确认权限范围、维护状态,注意是否会触发联网、命令执行或文件读写。
  • 建议结合原始 README 核验具体用法和功能边界。

SKILL.md

Proposal Review

Methodically review proposals by chunking content, predicting feedback, and producing actionable output for the proposer.

Workflow

┌─────────────────────────────────────────────────────────────┐
│  1. INTAKE: Read entire proposal, identify source format    │
├─────────────────────────────────────────────────────────────┤
│  2. CHUNK: Split into reviewable sections (smart hybrid)    │
├─────────────────────────────────────────────────────────────┤
│  3. REVIEW LOOP: For each chunk:                            │
│     • Present chunk content                                 │
│     • Predict 3-4 likely reactions                          │
│     • Use AskUserQuestion for feedback                      │
│     • Record response                                       │
├─────────────────────────────────────────────────────────────┤
│  4. SYNTHESIZE: Compile feedback, infer overall sentiment   │
├─────────────────────────────────────────────────────────────┤
│  5. OUTPUT: Generate feedback document matching source      │
└─────────────────────────────────────────────────────────────┘

Phase 1: Intake

Read the entire proposal. Identify:

  • Source format: Local file, GitHub PR/issue/gist, Google Doc export, etc.
  • Structure: Headers, sections, numbered lists, or flowing prose
  • Length: Estimate chunk count (aim for 3-8 chunks for typical proposals)

Do not summarize or share initial impressions. Proceed directly to chunking.

Phase 2: Chunking Strategy

Use smart hybrid chunking:

Proposal StructureChunking Approach
Clear headers/sectionsOne chunk per major section
Large section (>500 words)Split at natural paragraph breaks
Small adjacent sections (<100 words each)Merge into single chunk
Numbered lists of itemsGroup 3-5 related items per chunk
Flowing prose without structureSplit at topic transitions (~300-400 words)

Chunk ordering: Present in document order unless there's a clear dependency (e.g., "Alternatives" before "Proposed Solution" if alternatives inform the solution review).

Phase 3: Review Loop

For each chunk:

3.1 Present the Chunk

Quote or summarize the chunk content. For longer chunks, quote key sentences and summarize the rest. Use a clear header like:

### Chunk 2 of 5: Technical Architecture

3.2 Predict Reactions

Generate 3-4 predicted reactions spanning these categories:

CategoryExample Predictions
Clarification"This is unclear—what does X mean?", "How does this interact with Y?"
Concern"This scope seems too large", "Have you considered Z risk?"
Approval"This approach makes sense", "Good tradeoff analysis"
Suggestion"Consider alternative A", "This needs more detail on B"

Select predictions that feel most relevant to this specific chunk. Not every chunk needs all categories.

3.3 Collect Feedback

Use AskUserQuestion with:

  • Predicted reactions as options (2-4 most likely)
  • User can select one OR provide custom feedback via "Other"
  • Keep option labels concise (under 10 words), use description for detail

Example:

question: "What's your reaction to this technical architecture section?"
header: "Architecture"
options:
  - label: "Looks good"
    description: "The proposed architecture is sound and well-reasoned"
  - label: "Scope concern"
    description: "This feels too ambitious for the timeline"
  - label: "Need clarification"
    description: "Some technical details are unclear or missing"
  - label: "Consider alternative"
    description: "There may be a simpler or better approach"

3.4 Record Response

Store each response with:

  • Chunk identifier (number + title)
  • Selected option or custom text
  • Any quoted content the feedback references

Phase 4: Synthesis

After all chunks reviewed:

  1. Group feedback by theme: Consolidate similar concerns across chunks
  2. Infer overall sentiment: Based on feedback distribution:

- Mostly approvals → Positive with minor suggestions - Mixed → Conditional support, needs revisions - Mostly concerns → Significant issues to address

  1. Identify patterns: Note if same concern appears multiple times

Then ask:

question: "Would you like me to include suggested next steps for the proposer?"
header: "Next Steps"
options:
  - label: "Yes, include action items"
    description: "Generate concrete next steps based on feedback"
  - label: "No, just the feedback"
    description: "Keep output to observations and reactions only"

Phase 5: Output Generation

Adapt output format to source:

SourceOutput Format
GitHub PRPR review comment with quoted lines and threaded feedback
GitHub IssueComment with sections matching issue structure
Markdown fileCompanion *-feedback.md with inline references
Google DocStructured comment list with section references
Generic/unknownStructured markdown with clear sections

Output Structure

## Feedback Summary

**Overall**: [Inferred sentiment - 1 sentence]

## Section-by-Section Feedback

### [Section Name]
[Feedback with quotes where relevant]

### [Section Name]
...

## Key Themes
- [Theme 1]: [Consolidated feedback]
- [Theme 2]: ...

## Next Steps (if requested)
- [ ] [Action item 1]
- [ ] [Action item 2]

Tone Guidelines

  • Direct but constructive
  • Quote specific text when critiquing
  • Frame concerns as questions when possible ("Have you considered..." vs "This won't work")
  • Acknowledge what works, not just what doesn't

Edge Cases

Very short proposals (<300 words): Skip chunking, review as single unit with 4-5 predicted reactions.

Very long proposals (>3000 words): Cap at 8-10 chunks. Merge aggressively or offer to focus on specific sections.

Unclear structure: Ask user which sections matter most before chunking.

Multiple proposals: Review one at a time. Ask user for order preference if not obvious.

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenCode

26.9%
按下载量换算95

Gemini CLI

21.33%
按下载量换算76

Antigravity

15.66%
按下载量换算55

Claude Code

12.86%
按下载量换算46

github-copilot

7.91%
按下载量换算28

windsurf

3.37%
按下载量换算12

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。

来源信息

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