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Reflect on the conversation and propose improvements to CLAUDE.md configuration files.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Add terms, context, or instructions to Shama voice-to-text files for clipboard mode (Ctrl+Opt+Space) or shell-command mode (Ctrl+Opt+S), at project or global level.
Initialize Shama voice-to-text config for the project — create voice.json, voice-vocabulary.md, voice-context.md, and register the project in Shama's registry.toml.
Reflect on codebase navigation effectiveness at end of conversation. Surfaces dead ends, inefficiencies, missing context. Does not write files — pair with /kiro:steering-custom to persist.
Use when picking or vetting a keyboard shortcut on macOS. Triggers include "what hotkey should I use for X", "is `<combo>` available", "does this shortcut conflict", "recommend a keybinding for…", "check `<combo>` against my setup", "pick a hotkey for…", or any mention of choosing/binding/changing a shortcut in WezTerm, tmux, Zed, Chrome, Claude Code, or macOS. Determines whether a proposed combo collides with OS-reserved bindings, app defaults, or the user's customizations, and recommends ergonomic alternatives when needed.
Detect and remove dead code with knip. Use when the user asks to "run knip", "find unused files", "find unused exports", "find unused dependencies", "clean up dead code", "remove dead code", "set up knip", "configure knip", "knip.json", "knip false positive", "knip CI", or mentions a `knip` config, dependency bloat, bundle bloat from unused imports, or tree-shaking unused exports. Covers the configuration-first workflow, confidence-gated deletion, framework-specific gotchas (Next.js 15+, Tailwind, Storybook, Jest, Bun's test runner and `bun build --compile`), monorepos, CI integration, and performance tuning.
This skill should be used when the user asks to "set up react-scan", "install react-scan", "diagnose React re-renders", "find unnecessary renders", "find unstable props", "automate React render checks with Playwright", "react-scan + playwright", "measure component renders programmatically", "check why a React component is slow", or mentions React rendering issues, slow React interactions, render counts, or component-level perf attribution. Covers install across Next.js/Vite/Remix/script-tag/browser-extension, the lite headless API for CI, and the canonical render-attribution → fix → validate loop driven through Playwright.
| name | reflection |
| description | Reflect on the conversation and propose improvements to CLAUDE.md configuration files. |
| disable-model-invocation | true |
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/CLAUDE.local.md. Follow these steps carefully:
Then, examine the current Claude instructions:
User-level CLAUDE.md:
!read-file ~/.claude/CLAUDE.md "User-level Claude instructions"
Project-level CLAUDE.md:
!read-file ./CLAUDE.md "Project-level Claude instructions"
Project-level CLAUDE.local.md:
!read-file ./CLAUDE.local.md "Local project-level Claude instructions"
Analyze the chat history and instructions to identify areas that could be improved. Look for:
Wait for feedback from the human on each suggestion before proceeding. If the human approves a change, move it to the implementation phase. If not, refine your suggestion or move on to the next idea.
Implementation Phase: For each approved change: a) Clearly state the section of the instructions you're modifying b) Present the new or modified text for that section c) Explain how this change addresses the issue identified in the analysis phase
Output Format: Present your final output in the following structure:
<final_instructions> [Present the complete, updated set of instructions for Claude, incorporating all approved changes] </final_instructions>
Remember, your goal is to enhance Claude's performance and consistency while maintaining the core functionality and purpose of the AI assistant. Be thorough in your analysis, clear in your explanations, and precise in your implementations.
