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reflectionreflection 分析

Agent Skill

reflection 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。

总安装

1,529

周安装

65

GitHub Stars

1,493

下载量

536
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:reflection(reflection 分析)
来源仓库:https://github.com/feiskyer/claude-code-settings
仓库路径:skills/reflection
安装命令:
npx skills add https://github.com/feiskyer/claude-code-settings --skill reflection
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/feiskyer/claude-code-settings --skill reflection

简介

用于查找、检索和筛选相关信息。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。

  • 适合在需要根据关键词或任务场景快速定位候选结果时使用。
  • 可结合来源仓库和原始 README 核验具体用法。
  • 安装前建议确认权限范围和维护状态。
  • 当前分类为研究检索。reflection 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Reflection

Analyze the current session and improve Claude Code instructions. Operates in two modes based on user input.

Mode Selection

  • Quick mode (default): Triggered by /reflection or /reflection quick. Focuses on analyzing chat history to identify and implement improvements to CLAUDE.md instructions.
  • Deep mode: Triggered by /reflection deep or /reflection comprehensive. Performs a full session analysis covering problems solved, patterns established, user preferences, system understanding, and knowledge gaps, then updates CLAUDE.md accordingly.

Determine the mode from the user's input. If no mode is specified, use Quick mode. If the user says "deep", "comprehensive", or "harder", use Deep mode.


Quick Mode: CLAUDE.md Improvement

1. Analysis Phase

Review the chat history in the context window, then read the current CLAUDE.md file in the repository root.

Analyze both to identify areas for improvement:

  • Inconsistencies in Claude's responses
  • Misunderstandings of user requests
  • Areas where Claude could provide more detailed or accurate information
  • Opportunities to enhance handling of specific query types or tasks
  • Missing instructions that would have prevented mistakes made during the session

2. Analysis Documentation

Use TodoWrite to track each identified improvement area and create a structured approach for changes.

3. Interaction Phase

Present findings and improvement ideas to the user. For each suggestion:

  1. Explain the current issue identified
  2. Propose a specific change or addition to the instructions
  3. Describe how this change would improve Claude's performance

Wait for feedback on each suggestion before proceeding. If the user approves a change, move to implementation. If not, refine the suggestion or move on.

4. Implementation Phase

For each approved change:

  1. Use the Edit tool to modify the CLAUDE.md file
  2. State the section being modified
  3. Present the new or modified text
  4. Explain how this change addresses the identified issue

5. Output Format

Present the final output in this structure:

<analysis>
[Issues identified and potential improvements]
</analysis>

<improvements>
[For each approved improvement:
1. Section being modified
2. New or modified instruction text
3. Explanation of how this addresses the identified issue]
</improvements>

<final_instructions>
[Complete updated set of instructions incorporating all approved changes]
</final_instructions>

Commit changes using git after successful implementation.


Deep Mode: Comprehensive Session Analysis

1. Session Analysis Phase

Review the entire conversation history and identify:

Problems and Solutions

  • What problems were encountered?
  • Initial symptoms reported by the user
  • Root causes discovered
  • Solutions implemented
  • Key insights learned

Code Patterns and Architecture

  • Design decisions made
  • Architecture choices
  • Code relationships discovered
  • Integration points identified

User Preferences and Workflow

  • Communication style
  • Decision-making patterns
  • Quality standards
  • Workflow preferences
  • Direct quotes that reveal preferences

System Understanding

  • Component interactions
  • Critical paths and dependencies
  • Failure modes and recovery
  • Performance considerations

Knowledge Gaps and Improvements

  • Misunderstandings that occurred
  • Information that was missing
  • Better approaches discovered
  • Future considerations

2. Reflection Output Phase

Present a structured summary covering:

  • Session overview: High-level summary of what was accomplished
  • Problems solved: Each problem with root cause and solution
  • Patterns established: Design and code patterns worth remembering
  • User preferences: Workflow and communication preferences observed
  • System relationships: Component interactions and dependencies learned
  • Knowledge updates: New understanding gained about the codebase or domain
  • Commands and tools: Any tools or commands that were particularly useful or problematic
  • Future improvements: Suggestions for next steps or optimizations
  • Collaboration insights: What worked well and what could improve in the AI-human interaction

3. Action Items

After presenting the analysis, propose concrete actions:

  1. Update CLAUDE.md with specific sections reflecting learnings
  2. Add comments to specific files where understanding was gained
  3. Create documentation for specific topics if needed
  4. Test anything that needs verification

Use TodoWrite to track these action items. Wait for user approval before implementing changes.

4. Implementation

For each approved action, implement the change and commit using git.


Best Practices

  • Always read the current CLAUDE.md file before proposing changes
  • Use TodoWrite to track analysis progress and implementation tasks
  • Test proposed changes by considering edge cases and common scenarios
  • Ensure all modifications maintain consistency with existing patterns
  • Be thorough in analysis, clear in explanations, and precise in implementations
  • Commit changes using git after successful implementation

Your Task

Reflect on the current session. Determine the mode from the user's input — default to Quick mode if no mode is specified.

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

36.29%
按下载量换算195

Claude

31.57%
按下载量换算169

Cursor

20.79%
按下载量换算111

Gemini CLI

8.89%
按下载量换算48

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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。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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