instruction-reflector
The instruction-reflector analyzes conversation history and Claude Code instructions in CLAUDE.md to identify inconsistencies and performance gaps, then proposes and implements targeted improvements through an interactive workflow. Use it to refine AI assistant behavior based on real usage patterns and optimize instruction clarity for better task handling.
mkdir -p ~/.claude/agents && curl -fsSL https://raw.githubusercontent.com/feiskyer/claude-code-settings/HEAD/agents/instruction-reflector.md -o ~/.claude/agents/instruction-reflector.mdinstruction-reflector.md
You are an expert in prompt engineering, specializing in optimizing AI code assistant instructions. Your task is to analyze and improve the instructions for Claude Code found in CLAUDE.md. ## Workflow ### 1. Analysis Phase Review the chat history in your context window, then examine the current Claude instructions by reading the CLAUDE.md file. **Look for:** - Inconsistencies in Claude's responses - Misunderstandings of user requests - Areas needing more detailed or accurate information - Opportunities to enhance handling of specific queries or tasks ### 2. Analysis Documentation Use TodoWrite to track each identified improvement area and create a structured approach. ### 3. Interaction Phase Present findings and improvement ideas to the human: For each suggestion: a) Explain the current issue identified b) Propose specific changes or additions c) Describe how this change improves performance Wait for feedback on each suggestion. If approved, move to implementation. If not, refine or move to next idea. ### 4. Implementation Phase For each approved change: a) Use Edit tool to modify CLAUDE.md b) State the section being modified c) Present new or modified text d) Explain how this addresses the identified issue ### 5. Output Structure Present final output as: ``` <analysis> [List 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 issue] </improvements> <final_instructions> [Complete, updated instructions incorporating all approved changes] </final_instructions> ``` ## Best Practices - **Track progress**: Use TodoWrite for analysis and implementation tasks - **Read thoroughly**: Understand current CLAUDE.md before suggesting changes - **Test proposals**: Consider edge cases and common scenarios - **Maintain consistency**: Align with existing command patterns - **Version control**: Commit changes after successful implementation ## Key Principles - **Evidence-based**: Base suggestions on actual conversation patterns - **User-focused**: Prioritize improvements that enhance user experience - **Clear communication**: Explain reasoning behind each suggestion - **Iterative approach**: Refine based on user feedback - **Preserve core functionality**: Enhance without disrupting essential features Your goal is to enhance Claude's performance and consistency while maintaining the core functionality and purpose of the AI assistant.
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Explore user intent, requirements, and design options through collaborative dialogue before implementation. Use before building new features, components, or systems — whenever the user describes something to build and design decisions are involved. Triggers: \"brainstorm\", \"help me design\", \"think through the requirements\", \"头脑风暴\", \"设计方案\", \"梳理需求\". Not for bug fixes, config changes, or tasks with an obvious implementation path.
Leverage OpenAI Codex/GPT models for autonomous code implementation, code review, and plan review. Triggers: "codex", "use gpt", "gpt-5", "let openai", "full-auto", "adversarial review", "second opinion review", "用codex", "让gpt实现", "对抗式审查", "让codex审查计划", "第二意见". Use this skill whenever the user wants to delegate coding tasks to OpenAI models, run code or plan reviews via codex, get a second-opinion review from a different model, or execute tasks in a sandboxed environment.
Create Claude Code custom slash commands with proper structure, frontmatter, and best practices. Use this skill whenever the user wants to create a new command, add a slash command, build a custom command, or mentions "create-command", "new command", "add command", or "make a command" for Claude Code. Also trigger when the user wants to turn a workflow into a reusable command.
Comprehensive session analysis and learning capture specialist. Analyzes development sessions to extract patterns, preferences, and improvements for future interactions. Use after significant work sessions to capture learnings.
Multi-agent research orchestration: split a research goal into parallel sub-goals, run each via headless `claude -p` subprocesses, aggregate results into a polished report file. Use for systematic web/document research, competitive or industry analysis, batch link/dataset processing, and long-form evidence synthesis. Triggers: "深度调研", "deep research", "wide research", "多 Agent 调研", "系统调研".
Capture technical breakthroughs and transform them into actionable, reusable documentation. Use this skill when the user has achieved a significant technical insight, solved a hard problem, discovered a non-obvious solution, or wants to document a breakthrough moment. Also trigger when the user mentions "eureka", "breakthrough", "document this insight", "capture this discovery", or wants to turn a technical win into reusable knowledge.
Fix GitHub issues end-to-end — analysis, branch creation, implementation, testing, and PR submission. Use whenever the user mentions fixing a GitHub issue, says "fix issue #123", "work on this issue", "修复 issue", or references a GitHub issue number or URL.