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style-learner风格学习者

Agent Skill

style-learner 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 Codex、Claude、Cursor、Gemini CLI 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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。

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周安装

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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:style-learner(风格学习者)
来源仓库:https://github.com/athola/claude-night-market
仓库路径:skills/style-learner
安装命令:
npx skills add https://github.com/athola/claude-night-market --skill style-learner
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/athola/claude-night-market --skill style-learner

简介

style-learner 用于记录任务执行中的错误、用户纠正、经验和能力缺口。

  • 适合让 Agent 持续沉淀问题、修正和最佳实践,提升后续任务表现。
  • 通过 npx skills add 命令从指定仓库安装,具体用法需结合 README 进一步确认。
  • 安装前建议确认权限范围、维护状态及是否涉及联网、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。

SKILL.md

Style Learning Skill

Extract and codify writing style from exemplar text for consistent application.

Approach: Feature Extraction + Exemplar Reference

This skill combines two complementary methods:

  1. Feature Extraction: Quantifiable style metrics (sentence length, vocabulary complexity, structural patterns)
  2. Exemplar Reference: Specific passages that demonstrate desired style

Together, these create a comprehensive style profile that can guide content generation and editing.

Required TodoWrite Items

  1. style-learner:exemplar-collected - Source texts gathered
  2. style-learner:features-extracted - Quantitative metrics computed
  3. style-learner:exemplars-selected - Representative passages identified
  4. style-learner:profile-generated - Style guide created
  5. style-learner:validation-complete - Profile tested against new content

Step 1: Collect Exemplar Text

Gather representative samples of the target style.

Minimum requirements:

  • At least 1000 words of exemplar text
  • Multiple samples preferred (shows consistency)
  • Same genre/context as target output
## Exemplar Sources

| Source | Word Count | Type |
|--------|------------|------|
| README.md | 850 | Technical |
| blog-post-1.md | 1200 | Narrative |
| api-guide.md | 2100 | Reference |

Step 2: Feature Extraction

Load: @modules/feature-extraction.md

Vocabulary Metrics

MetricHow to MeasureWhat It Indicates
Average word lengthchars/wordComplexity level
Unique word ratiounique/totalVocabulary breadth
Jargon densitytechnical terms/100 wordsAudience level
Contraction ratecontractions/sentencesFormality

Sentence Metrics

MetricHow to MeasureWhat It Indicates
Average lengthwords/sentenceComplexity
Length variancestd dev of lengthsNatural variation
Question frequencyquestions/100 sentencesEngagement style
Fragment usagefragments/100 sentencesStylistic punch

Structural Metrics

MetricHow to MeasureWhat It Indicates
Paragraph lengthsentences/paragraphDensity
List ratiobullet lines/total linesFormat preference
Header depthmax header levelOrganization style
Code block frequencycode blocks/1000 wordsTechnical density

Punctuation Profile

MetricNormal RangeStyle Indicator
Em dash rate0-3/1000 wordsParenthetical style
Semicolon rate0-2/1000 wordsFormal complexity
Exclamation rate0-1/1000 wordsEnthusiasm level
Ellipsis rate0-1/1000 wordsTrailing thought style

Step 3: Exemplar Selection

Load: @modules/exemplar-reference.md

Select 3-5 passages (50-150 words each) that best represent the target style.

Selection criteria:

  • Demonstrates characteristic sentence rhythm
  • Shows typical vocabulary choices
  • Represents the desired tone
  • Avoids atypical or exceptional passages

Exemplar Template

### Exemplar 1: [Label]
**Source**: [filename, lines X-Y]
**Demonstrates**: [what aspect of style]

> [Quoted passage]

**Key characteristics**:
- [Observation 1]
- [Observation 2]

Step 4: Generate Style Profile

Combine extracted features and exemplars into a usable style guide.

Profile Format

# Style Profile: [Name]
# Generated: [Date]
# Exemplar sources: [List]

voice:
  tone: [professional/casual/academic/conversational]
  perspective: [first-person/third-person/second-person]
  formality: [formal/neutral/informal]

vocabulary:
  average_word_length: X.X
  jargon_level: [none/light/moderate/heavy]
  contractions: [avoid/occasional/frequent]
  preferred_terms:
    - "use" over "utilize"
    - "help" over "facilitate"
  avoided_terms:
    - delve
    - leverage
    - comprehensive

sentences:
  average_length: XX words
  length_variance: [low/medium/high]
  fragments_allowed: [yes/no/sparingly]
  questions_used: [yes/no/sparingly]

structure:
  paragraphs: [short/medium/long] (X-Y sentences)
  lists: [prefer prose/balanced/prefer lists]
  headers: [descriptive/terse/question-style]

punctuation:
  em_dashes: [avoid/sparingly/freely]
  semicolons: [avoid/sparingly/freely]
  oxford_comma: [yes/no]

exemplars:
  - label: "[Exemplar 1 label]"
    text: |
      [Quoted passage]
  - label: "[Exemplar 2 label]"
    text: |
      [Quoted passage]

anti_patterns:
  - [Pattern to avoid 1]
  - [Pattern to avoid 2]

Step 5: Validation

Test the profile against new content:

  1. Generate sample content using the profile
  2. Compare metrics to extracted features
  3. Have user evaluate voice/tone match
  4. Refine profile based on feedback

Validation Checklist

  • Metrics within 20% of exemplar averages
  • No anti-pattern violations
  • Tone matches user expectation
  • Vocabulary aligns with exemplars
  • Structure follows profile guidelines

Usage in Generation

When generating new content, reference the profile:

Generate [content type] following the style profile:
- Voice: [from profile]
- Sentence length: target ~[X] words, vary between [Y-Z]
- Use exemplar passage as tone reference:
  > [exemplar quote]
- Avoid: [anti-patterns from profile]

Module Reference

  • See modules/style-application.md for applying learned styles to new content

Integration with slop-detector

After generating content, run slop-detector to verify:

  1. No AI markers introduced
  2. Style metrics match profile
  3. Anti-patterns avoided

Exit Criteria

  • Style profile document created
  • At least 3 exemplar passages included
  • Quantitative metrics extracted
  • Anti-patterns from slop-detector integrated
  • Validation test passed

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

Claude Code

27.48%
按下载量换算97

OpenCode

21.89%
按下载量换算77

Cursor

16.88%
按下载量换算60

Codex

12.07%
按下载量换算43

Antigravity

8.27%
按下载量换算29

Gemini CLI

3.74%
按下载量换算13

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权限和风险

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该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

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