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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

日本語の概要は準備中です。原文の説明を表示しています。

Runs a local OpenClaw agent non-interactively and returns its structured response. Use when the user asks to delegate a bounded task to the OpenClaw main agent.

日本語の概要は準備中です。原文の説明を表示しています。

Runs MiniMax Code headlessly for repository analysis and coding tasks. Use when the user asks to delegate work to MiniMax Code or obtain a MiniMax-based coding-agent result.

日本語の概要は準備中です。原文の説明を表示しています。

Runs the Code Mate Qoder CN CLI non-interactively with structured output. Use when the user asks to delegate a bounded coding task to Qoder.

日本語の概要は準備中です。原文の説明を表示しています。

将成功解决的用户问题收录到 FAQ 知识库。问题解决后自动判断是否收录。也可以在用户说"收录到 FAQ"、"记录这个问题"、"add to FAQ"时手动触发。

日本語の概要は準備中です。原文の説明を表示しています。

Runs Gemini CLI headlessly for repository analysis and coding tasks. Use when the user asks to delegate work to Gemini CLI or compare its result with another coding agent.

日本語の概要は準備中です。原文の説明を表示しています。

Runs Codex CLI non-interactively for code analysis and implementation tasks. Use when the user asks to delegate repository work to Codex or obtain a second coding-agent result.

日本語の概要は準備中です。原文の説明を表示しています。

Runs Pi non-interactively and parses its JSON event stream for repository tasks. Use when the user asks to delegate a bounded analysis or implementation task to Pi.

日本語の概要は準備中です。原文の説明を表示しています。

Run Cherry Studio critical-path system regression tasks through the repository-owned Playwright E2E workflow. Use for full regression, release acceptance, development-branch system validation, or a named cherry-regression-test task on GitHub-hosted macOS and Windows runners.

日本語の概要は準備中です。原文の説明を表示しています。

Runs Claude Code non-interactively for code analysis and implementation tasks. Use when the user asks to delegate repository work to Claude Code or compare its result with another coding agent.

日本語の概要は準備中です。原文の説明を表示しています。

Runs DeepSeek Harness headlessly for bounded repository tasks. Use when the user asks to delegate analysis or implementation to DeepSeek Harness.

日本語の概要は準備中です。原文の説明を表示しています。

Use when implementing or debugging ANY network request, API call, or data fetching. Covers fetch API, React Query, SWR, error handling, caching, offline support, and Expo Router data loaders (`useLoaderData`).

日本語の概要は準備中です。原文の説明を表示しています。

Set up Tailwind CSS v4 in Expo with react-native-css and NativeWind v5 for universal styling

日本語の概要は準備中です。原文の説明を表示しています。

Implement and review React, React Native, and TypeScript UI for maximum practical render performance through leaf-local updates, stable identities, honest Hook semantics, and minimal effects. Use for components, hooks, callbacks, context, external stores, memoization, list rows, large dependency arrays, callback or state fanout, re-render reduction, and React Compiler-aware performance work.

日本語の概要は準備中です。原文の説明を表示しています。

Inspect and profile React Native component trees from agent-device. Use for React Native performance, profiling, props, state, hooks, render causes, slow components, excessive rerenders, or questions like why a component rerendered.

日本語の概要は準備中です。原文の説明を表示しています。

React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.

日本語の概要は準備中です。原文の説明を表示しています。

Build, audit, and fix @legendapp/list and LegendList usage in React, React Native, and web apps. Use for virtualization, scroll blanking, mount cost, data identity, recycling, row invalidation, measurement, chat anchoring, drawDistance, visibility, or Legend List performance regressions.

日本語の概要は準備中です。原文の説明を表示しています。

Provides React Native performance optimization guidelines for FPS, TTI, bundle size, memory leaks, re-renders, and animations. Applies to tasks involving Hermes optimization, JS thread blocking, bridge overhead, FlashList, native modules, or debugging jank and frame drops.

日本語の概要は準備中です。原文の説明を表示しています。

Helps understand and write EAS workflow YAML files for Expo projects. Use this skill when the user asks about CI/CD or workflows in an Expo or EAS context, mentions .eas/workflows/, or wants help with EAS build pipelines or deployment automation.

日本語の概要は準備中です。原文の説明を表示しています。

Uniwind — Tailwind CSS v4 styling for React Native. Use when adding, building, or styling components in a React Native project that uses Tailwind with className.

日本語の概要は準備中です。原文の説明を表示しています。

Automates Apple-platform apps (iOS, tvOS, macOS) and Android devices. Use when navigating apps, taking snapshots/screenshots, tapping, typing, scrolling, extracting UI info, collecting logs/network/perf evidence, or planning agent-device CLI commands.

日本語の概要は準備中です。原文の説明を表示しています。

Complete guide for building beautiful apps with Expo Router. Covers fundamentals, styling, components, navigation, animations, patterns, and native tabs.

日本語の概要は準備中です。原文の説明を表示しています。

Iterative diagnose-and-fix workflow that repeatedly uses the diagnose skill to find a proven problem, plan the smallest credible improvement, implement it, re-diagnose, and continue until no useful fix remains or user intervention is required. Requires the diagnose skill; selected installs must also install diagnose. Use when asked to keep debugging, fix and verify, iterate until satisfied, improve after diagnosis, or run a diagnosis/fix/verification loop.

日本語の概要は準備中です。原文の説明を表示しています。

Opt-in evidence-first causal diagnosis for bugs, browser or app/device failures, flaky behavior, and performance regressions. Activate only when the user explicitly invokes `$diagnose` or explicitly asks to use the named diagnose skill. Do not activate merely because the user mentions a bug, asks why something failed, requests debugging or a fix, or describes unexpected behavior. Once explicitly invoked, locate likely causes, instrument relevant boundaries with extensive structured logging, reproduce the issue, analyze the collected logs, rank causes with confidence scores, and pursue 100% operational confidence while probes can increase confidence.

日本語の概要は準備中です。原文の説明を表示しています。