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Agent Skill · 研究检索

prose-polish(散文润色)

prose-polish 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

prose-polish 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 51,536。

总安装 582周安装 24GitHub Stars 公开资料未说明下载 190核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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:prose-polish(散文润色)
来源仓库:https://github.com/leegonzales/aiskills
仓库路径:skills/prose-polish
安装命令:
npx skills add leegonzales/aiskills --skill "prose-polish"
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。

AgentSkills.tonpx skills
npx skills add leegonzales/aiskills --skill "prose-polish"

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

prose-polish 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。它支持按任务场景或来源线索筛选内容,适用于文本润色、语言风格调整或表达优化。

通过关键词输入和条件过滤,Agent 可输出结构化候选列表,便于后续人工验证或深度分析。安装前建议确认权限范围和维护状态,注意是否涉及联网、命令执行或文件读写操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/prose-polish

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add leegonzales/aiskills --skill "prose-polish";命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

prose-polish 怎么安装?

在终端执行 npx skills add leegonzales/aiskills --skill "prose-polish"。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

prose-polish 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

prose-polish 需要什么权限?

权限级别是只读。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。

prose-polish 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

prose-polish 用的人多吗?

公开数据是累计安装 582 次,近一周安装 24 次,下载 190 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

prose-polish 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 要求有安全扫描结论才肯安装的团队。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
prose-polish

散文润色 · 本页

582—只读MIT未收录

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
prose-polish
description
Evaluate and elevate writing effectiveness through multi-dimensional quality assessment. Analyzes craft, coherence, authority, purpose, and voice with genre-calibrated thresholds. Use for refining drafts, diagnosing quality issues, generating quality content, or teaching writing principles.

Prose Polish v2

Evaluate and elevate writing effectiveness through multi-dimensional quality assessment. Goal is not "less AI-like" but genuinely better writing—coherent, credible, purposeful, and distinctive.

Philosophy

Writing Effectiveness = f(Text, Author, Audience, Context, Genre)

We optimize for quality, not undetectability. These often correlate, but the distinction matters:

  • Bad goal: "Make this not sound like AI"
  • Good goal: "Make this effective writing"

Quick Start

Analysis: Detect genre → Load detection-patterns.md → Apply 6-dimension evaluation → Generate quality profile

Elevation: Analyze → Load remediation-strategies.md → Phase 1 (Structure) → Phase 2 (Style) → Explain changes

Prevention: Load prevention-prompts.md → Build genre-calibrated constraints → Generate → Self-verify

Core Capabilities

1. Detection & Analysis

When: User asks to "analyze," "evaluate," "check," or "score" text

Process:

  1. Detect Genre (before scoring)

- Technical | Business | Academic | Creative | Personal | Journalistic - Apply genre-appropriate thresholds

  1. Load references/detection-patterns.md
  1. Perform 6-Dimension Analysis:

- Craft (0-100): Lexical patterns, structural variance, rhetorical execution - Coherence (0-100): Logical flow, functional specificity, earned transitions - Authority (0-100): Earned vs delegated vs false expertise signals - Purpose (0-100): Clear intent, stakes, audience calibration - Voice (0-100): Distinctiveness, embodiment, appropriate register - Effectiveness (0-100): Genre-weighted synthesis

  1. Generate Quality Profile

Report Format:

WRITING EFFECTIVENESS ANALYSIS

Genre: [Detected] | Calibration: [Applied]

QUALITY PROFILE:
           Craft: ████████░░ 80 - [Brief interpretation]
       Coherence: ██████░░░░ 60 - [Brief interpretation]
       Authority: █████░░░░░ 50 - [Brief interpretation]
         Purpose: ███████░░░ 70 - [Brief interpretation]
           Voice: █████████░ 90 - [Brief interpretation]
   Effectiveness: ███████░░░ 70 - [Genre-weighted average]

KEY INSIGHT: [Diagnostic based on dimension gaps]
Example: "High craft but low authority = generic specificity problem"

DETAILED ANALYSIS:

CRAFT ISSUES:
- Lexical: [specific patterns, with genre context]
- Structural: [sentence variance, paragraph patterns]
- Rhetorical: [commitment level, specificity quality]

COHERENCE ISSUES:
- Logical flow: [do ideas connect across paragraphs?]
- Specificity function: [relevant vs decorative details]
- Transition authenticity: [earned vs mechanical]

AUTHORITY ISSUES:
- Type: [Earned / Delegated / False / Mixed]
- Expertise signals: [insider knowledge present/absent]
- Stakes: [skin in the game visible?]

PURPOSE ISSUES:
- Intent clarity: [what is this FOR?]
- Audience calibration: [appropriate for reader?]
- Stakes: [why should reader care?]

VOICE ISSUES:
- Distinctiveness: [recognizable author?]
- Embodiment: [feels like a person?]
- Register: [appropriate for genre?]

TOP 5 PRIORITY IMPROVEMENTS:
1. [Most impactful, actionable fix]
2. [...]
3. [...]
4. [...]
5. [...]

Scoring Philosophy:

  • Be ruthless in scoring. Avoid grade inflation.
  • Dimension gaps are diagnostic (high craft + low coherence = decorative writing)
  • Genre calibration prevents false positives on appropriate conventions

2. Elevation & Remediation

When: User asks to "improve," "fix," "elevate," or "rewrite" text

Process:

  1. Perform quick 6-dimension analysis
  2. Load references/remediation-strategies.md
  3. Apply Two-Phase Remediation:

Phase 1: Structural (The Editor) Focus on logic and authority before touching style.

  • Coherence Pass:

- Check: Does logic flow across paragraphs? - Check: Is every detail doing work? - Fix: Remove decorative specificity - Fix: Repair logical gaps - Fix: Ensure transitions are earned

  • Authority Pass:

- Check: Is authority earned or delegated? - Fix: Replace institutional voice with speaker - Fix: Add demonstrated expertise signals - Fix: Introduce appropriate stakes/vulnerability

Phase 2: Stylistic (The Writer) Now refine rhythm, commitment, and voice.

  • Rhythm Pass:

- Sentence variance per genre threshold - Structural breaks appropriate to genre - Information density variance (avoid uniform medium-density)

  • Commitment Pass:

- Remove cowardly hedges (opinion avoidance) - Preserve protective hedges (epistemic honesty) - Add functional specificity - Make claims with stakes

  • Voice Pass:

- Add embodiment markers - Inject appropriate personality (avoid "LinkedIn Influencer" overcorrection) - Risk-taking calibrated to genre

Output:

ELEVATED VERSION:
[Rewritten text]

PHASE 1 CHANGES (Structure):
- Coherence: [What logical issues were fixed]
- Authority: [How expertise was demonstrated]

PHASE 2 CHANGES (Style):
- Rhythm: [Sentence variation details]
- Commitment: [Hedge removal, specificity additions]
- Voice: [Personality calibration]

BEFORE/AFTER EXAMPLES:
[3-5 transformations with principles explained]

Depth Control (Aggressiveness Levels):

Users can control the extent of remediation:

LevelWhat It DoesWhen to Use
ConservativePhase 1 only (Coherence + Authority)Preserve voice, fix logic only
ModerateBoth phases, light Phase 2Balance improvement with original tone
AggressiveBoth phases, full transformationComplete rewrite for maximum quality

How to request:

  • "Fix the logic but keep my voice" → Conservative
  • "Improve this while keeping the general tone" → Moderate
  • "Rewrite this for maximum effectiveness" → Aggressive

Default: Moderate (both phases, respects original intent)

3. Prevention & Generation

When: User asks to "write" or "generate" with quality emphasis

Process:

  1. Identify genre and audience
  2. Load references/prevention-prompts.md
  3. Construct genre-calibrated constraints
  4. Generate with quality dimensions in mind
  5. Self-verify against 6-dimension framework
  6. Refine if any dimension scores below threshold

4. Training & Teaching

When: User wants to learn quality evaluation

Process:

  1. Load appropriate reference files
  2. Explain the 6 dimensions and why they matter
  3. Show examples of dimension gaps (high X, low Y)
  4. Demonstrate genre calibration effects
  5. Practice exercises with real text

Genre Calibration

Detect genre before scoring. Apply appropriate thresholds:

GenreSentence VarianceHedge TolerancePassive VoiceTemplate OKVoice Expectation
Technical5+ StdDevHigher (precision)HigherExpectedNeutral authority
Business6+ StdDevStandardLowerStructure OKProfessional human
Academic6+ StdDevHigher (epistemic)ModerateIf fresh contentMeasured expertise
Creative8+ StdDevLowLow= FailureDistinctive required
Personal8+ StdDevLowLowMust be organicStrongly embodied
Journalistic7+ StdDevStandardLowLead structure OKClear but present

Genre-Specific Signals

Technical Documentation:

  • Allow: "certain," "particular," "specific" (precision, not hedging)
  • Allow: Consistent sentence length (clarity, not robotic)
  • Require: Explains WHY not just HOW
  • Authority: Demonstrated through insider terminology and tradeoff awareness

Business Writing:

  • Require: Friction acknowledgment (what challenges exist?)
  • Require: Clear ownership and next steps
  • Watch: Institutional hiding ("it is recommended" vs "I recommend")
  • Authority: Numbers with interpretation, not just data dumps

Academic Writing:

  • Require: Synthesis over summarization
  • Require: Clear contribution statement
  • Allow: "It appears that" as epistemic honesty
  • Authority: Citation genealogy, not just name-dropping

Creative/Narrative:

  • Require: Surprise, sensory embodiment
  • Require: Specificity that reveals character, not decorates
  • Watch: Generic emotional beats ("hollow ache" without texture)
  • Authority: Earned through embodied experience

Dimension Deep Dives

Coherence (NEW in v2)

What it catches: Decorative specificity, logic gaps, non-sequiturs

Red Flags:

  • Details that don't advance understanding
  • Causal claims that don't hold ("teaching calculus → cracked hands")
  • Transitions that connect syntactically but not semantically
  • Specificity that signals "human-ness" rather than builds meaning

Questions to Ask:

  1. If I remove transitions, do ideas still connect?
  2. Could I swap paragraphs without changing meaning? (Bad if yes)
  3. Is every specific detail doing work?
  4. Would a hostile reader find logical gaps?

Authority (NEW in v2)

What it catches: Performed expertise vs demonstrated expertise

Authority Types:

  • Earned: Insider details, vulnerability, consequences for being wrong
  • Delegated: Citations without synthesis, institutional voice, numbers without interpretation
  • False: Stereotypes as expertise, generic specificity, authority cosplay

Note: We measure *signaling*, not *truth*. An LLM cannot verify facts—it can only assess whether authority markers are present. Be honest about this limitation.

Hedge Classification (NEW in v2)

Not all hedges are bad. Classify before penalizing:

Cowardly Hedges (PENALIZE):

  • Avoiding opinion: "Some might say," "It could be argued"
  • Diluting claims: "somewhat," "fairly," "rather"
  • Escape hatches: "in a sense," "in many ways"

Protective Hedges (PRESERVE):

  • Epistemic honesty: "The evidence suggests," "Current research indicates"
  • Appropriate uncertainty: "appears to," "likely"
  • Precision: "certain," "particular," "specific"

Quality Standards

Every output should pass:

Coherence Test: Do ideas connect logically across the piece? Authority Test: Is expertise demonstrated, not just claimed? Purpose Test: Is it clear what this writing is FOR? Voice Test: Would you recognize this author again? Embodiment Test: Does it feel like a person with experiences? Surprise Test: Is there at least one unexpected element?

Effectiveness Calculation

Genre-weighted average:

GenreCraftCoherenceAuthorityPurposeVoice
Technical25%30%20%15%10%
Business20%25%25%20%10%
Academic20%30%20%15%15%
Creative15%20%10%15%40%
Personal15%20%20%15%30%

Reference Files

When to Load:

  • detection-patterns.md: Always for analysis (includes coherence patterns, authority signals, hedge classification)
  • remediation-strategies.md: Always for elevation (two-phase framework)
  • prevention-prompts.md: Always for generation (genre-calibrated constraints)
  • ai-vocabulary.md: When checking specific banned words

Critical Principles

  1. Effectiveness Over Detection: Goal is quality writing, not fooling detectors.
  1. Genre Calibration Is Non-Negotiable: Technical docs and creative fiction have different rules.
  1. Dimension Gaps Are Diagnostic: High craft + low coherence = decorative writing. High voice + low authority = personality without substance.
  1. Coherence Before Surface: Fix logic before fixing rhythm. A polished fallacy is still a fallacy.
  1. Authority Is Signaling: We measure expertise markers, not truth. Be honest about this.
  1. Hedges Need Classification: Epistemic honesty is not weakness. Only penalize opinion-avoidance.
  1. Avoid Overcorrection: "LinkedIn Influencer" mode is not the goal. Calibrate voice to genre.
  1. Radical Simplicity Has Value: Sometimes effectiveness means removing signals, not adding them.

Examples

Analysis Example

Input: Business quarterly report with strong metrics but institutional voice

Output:

WRITING EFFECTIVENESS ANALYSIS

Genre: Business | Calibration: Applied

QUALITY PROFILE:
           Craft: ████████░░ 78 - Clean structure, precise numbers
       Coherence: ████████░░ 85 - Logical flow, functional data
       Authority: █████████░ 88 - Genuine SaaS expertise visible
         Purpose: ███████░░░ 75 - Clear reporting, muted stakes
           Voice: ██████░░░░ 62 - Institutional, could be any company
   Effectiveness: ████████░░ 82 - Strong business communication

KEY INSIGHT: High authority through insider metrics (NRR, churn analysis)
compensates for institutional voice. Genre-appropriate execution.

DETAILED ANALYSIS:
...

Elevation Example (Two-Phase)

Phase 1 Output:

STRUCTURAL FIXES:
- Coherence: Moved security section before feature description (foundations first)
- Authority: Replaced "best practices recommend" with specific tradeoff analysis

Phase 2 Output:

STYLISTIC FIXES:
- Rhythm: Added 5-word punch after long explanation
- Commitment: Removed "somewhat" and "fairly" (cowardly hedges)
- Voice: Added one moment of personality without overdoing it

Success Metrics

Objective:

  • Coherence score improvement when logic is fixed
  • Authority score reflects genuine expertise presence
  • No false positives on genre-appropriate conventions
  • Dimension gaps correctly diagnose quality issues

Subjective:

  • Text reads as effective for its purpose
  • Domain experts recognize authentic expertise
  • Genre conventions respected, not penalized
  • User understands WHY changes improve quality

Notes

  • This skill evaluates effectiveness, not truth
  • Genre detection happens BEFORE scoring
  • Two-phase remediation: structure first, style second
  • Hedge classification: epistemic honesty is not weakness
  • Avoid overcorrection: "more voice" can become cringe
  • Radical simplicity sometimes wins over complexity

平台分布

Claude Code

27.11%
按下载量换算52

windsurf

24.04%
按下载量换算46

OpenCode

20%
按下载量换算38

Codex

13.69%
按下载量换算26

Antigravity

7.16%
按下载量换算14

Gemini CLI

3.7%
按下载量换算7

安全审计

暂无安全审计结果可展示。

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。目前只有一个公开来源,建议同时核对源仓库。

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