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conversation-analyzer对话分析器

Agent Skill

conversation-analyzer 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

2,468

周安装

106

GitHub Stars

563

下载量

865
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:conversation-analyzer(对话分析器)
来源仓库:https://github.com/mhattingpete/claude-skills-marketplace
仓库路径:skills/conversation-analyzer
安装命令:
npx skills add https://github.com/mhattingpete/claude-skills-marketplace --skill conversation-analyzer
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/mhattingpete/claude-skills-marketplace --skill conversation-analyzer

简介

conversation-analyzer 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。

  • 适用于对话分析、协作事项处理和代码变更跟踪等开发类任务。
  • 支持仓库状态查询、代码变更分析和协作事项整理。
  • 安装命令:npx skills add https://github.com/mhattingpete/claude-skills-marketplace --skill conversation-analyzer
  • 建议确认权限范围和维护状态,注意是否会触发命令执行或文件读写操作。

SKILL.md

Conversation Analyzer

Analyzes your Claude Code conversation history to identify patterns, common mistakes, and workflow improvement opportunities.

When to Use

  • "analyze my conversations"
  • "review my Claude Code history"
  • "what patterns do you see in my usage"
  • "how can I improve my workflow"
  • "am I using Claude Code effectively"

What It Analyzes

  1. Request type distribution (bug fixes, features, refactoring, queries, testing)
  2. Most active projects
  3. Common error keywords
  4. Time-of-day patterns
  5. Repetitive tasks (automation opportunities)
  6. Vague requests causing back-and-forth
  7. Complex tasks attempted without planning
  8. Recurring bugs/errors

Analysis Scope

Default: Last 200 conversations for recency and relevance.

Methodology

1. Request Type Distribution

Categorizes by: bug fixes, feature additions, refactoring, information queries, testing, other.

2. Project Activity

Tracks which projects consume most time, identifies project-specific patterns.

3. Time Patterns

Hour-of-day usage distribution, identifies peak productivity times.

4. Common Mistakes

  • Vague requests: Initial requests lacking context vs. acceptable follow-ups
  • Repeated fixes: Same issues occurring multiple times
  • Complex tasks: Multi-step requests without planning
  • Repetitive commands: Manual tasks that could be automated

5. Error Analysis

Frequency of error-related requests, common error keywords, recurring problems.

6. Automation Opportunities

Identifies repeated exact requests, suggests skills, slash commands, or scripts.

Output

Structured report with:

  • Statistics: Request types, active projects, timing patterns
  • Patterns: Common tasks, repetitive commands, complexity indicators
  • Issues: Specific problems with examples
  • Recommendations: Prioritized, actionable improvements

Tools Used

  • Read: Load history file (~/.claude/history.jsonl)
  • Write: Create analysis reports if requested
  • Bash: Execute Python analysis script
  • Direct analysis: Parse JSON programmatically

Analysis Script

Uses scripts/analyze_history.py for comprehensive analysis:

Capabilities:

  • Loads and parses ~/.claude/history.jsonl
  • Analyzes patterns across multiple dimensions
  • Identifies common mistakes and inefficiencies
  • Generates actionable recommendations
  • Outputs detailed reports

Usage within skill: Runs automatically when user requests analysis.

Standalone usage:

cd ~/.claude/plugins/*/productivity-skills/conversation-analyzer/scripts
python3 analyze_history.py

Outputs:

  • conversation_analysis.txt - Detailed pattern analysis
  • recommendations.txt - Specific improvement suggestions

Example Output

Analyzed last 200 conversations:
- 60% general tasks, 15% bug fixes, 13% feature additions
- Project "ultramerge" dominates 58% of activity
- Same test-fixing request made 8 times
- 19 multi-step requests without planning
- Peak productivity: 13:00-15:00

Recommendations:
- Use test-fixing skill for recurring test failures
- Create project-specific utilities for ultramerge
- Use feature-planning skill for complex requests
- Add tests to prevent recurring bugs
- Schedule complex work during peak hours

Success Criteria

  • User understands usage patterns
  • Concrete, actionable recommendations
  • Specific examples from history
  • Prioritized by impact (quick wins vs long-term)
  • User can immediately apply improvements

Integration

  • feature-planning: Implement recommended improvements
  • test-fixing: Address recurring test failures
  • git-pushing: Commit workflow improvements

Privacy Note

All analysis happens locally. Conversation history never leaves user's machine.

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

27.33%
按下载量换算236

Gemini CLI

26.74%
按下载量换算231

Antigravity

19.03%
按下载量换算165

OpenCode

11.6%
按下载量换算100

trae

8.57%
按下载量换算74

Codex

3.86%
按下载量换算33

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

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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