931 件 ・ 関連度順
概要と使いどころ
Master entry point for all Stitch design workflows. Routes from user request → design spec → prompt assembly → screen generation → iteration (edit, variants, design systems) → design system extraction → framework conversion (Next.js, Svelte, HTML, React Native, or SwiftUI) → optional quality pass.
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Extract actionable insights from completed coding sessions. Use when a session completes to capture learnings for future sessions.
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。
Master memory forensics techniques including memory acquisition, process analysis, and artifact extraction using Volatility and related tools. Use when analyzing memory dumps, investigating incidents, or performing malware analysis from RAM captures.
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Hunt vulnerabilities in LLM-powered applications — direct/indirect prompt injection, system prompt extraction, ASCII smuggling, RCE via code tools, agentic AI exploits (ASI01-ASI10), data exfiltration via response channels, IDOR in chat history, jailbreak chains, RAG poisoning. Use when target has a chatbot, AI assistant, copilot, or agentic AI features.
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Search the web with Exa AI — neural search, content extraction, similar page discovery, quick research with citations, and async pro research with structured output. Covers all Exa endpoints (/search, /contents, /findSimilar, /answer, /research/v0/tasks). Triggers on web search, find pages, similar pages, research with citations, Exa, neural search.
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Scrape web pages to clean markdown using Firecrawl v2 — handles JS-heavy pages, site crawls, URL mapping, document parsing (PDF/DOCX/XLSX), LLM-powered extraction, autonomous agent scraping, and post-scrape browser interaction (Interact API). Free keyless mode available for search/scrape/parse/interact (no API key needed). Prefer over WebFetch for quality and completeness. Triggers on scrape URL, fetch page, crawl site, extract content, parse document, web to markdown, DeepWiki, Firecrawl.
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Scrape pages locally with anti-bot bypass, TLS impersonation, and adaptive element tracking — no API keys, no cloud. Handles Cloudflare protection, CSS/XPath element extraction, and survives site redesigns. Complements firecrawl (cloud) with 100% local execution. Triggers on Cloudflare bypass, anti-bot scraping, stealth fetch, local scraping, Scrapling.
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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Audits and optimizes the five global Claude layers (User Preferences, Styles, Global Memory, Skills, MCP Connectors) using the Global Layer Scorecard (six dimensions, anchored 1-5 rubrics). Detects eight cross-layer failure modes and produces evolutionary recommendations (Promotion, Demotion, Codification, Skill Extraction). Use when user says "audit my global setup," "optimize my preferences," "review my Claude configuration," "check my cross-project setup," "are my preferences working," "clean up my global memory," or "what should be in my preferences vs my project." Also use when a user has 3+ Projects and wants to improve their shared foundation. Do NOT use for single-Project audits, Project Memory optimization, or full-stack audits (use rootnode-project-audit, rootnode-memory-optimization, or rootnode-full-stack-audit respectively, if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.
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。
Organize, extract, prune, and verify Claude Code persistent memory files. Covers MEMORY.md as a concise index, topic extraction to dedicated files, staleness detection, accuracy verification against project state, the dual 200-line / 25KB load cap, and reachability of topic files from the index. Use when MEMORY.md is approaching either load cap, after a session produces durable insights worth preserving, when a topic section has grown beyond 10-15 lines and should be extracted, when project state has changed and memory entries may be stale, or before and after rewriting the index so that no topic file loses its only pointer.
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Extract the conceptual essence of a repository as skills, agents, and teams — the project's roles, procedures, and coordination patterns expressed as agentskills.io-standard definitions. Reads an arbitrary codebase and produces generalized definitions that capture WHAT the project does and WHO operates it, without replicating HOW it does it. Use when onboarding to a new codebase and wanting to understand its conceptual architecture, when bootstrapping an agentic system from an existing project, when studying a project's organizational DNA for cross-pollination, or when creating a skill/agent/team library inspired by a reference implementation.
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Deep receptive attention to extract intent beyond literal words. Maps active listening from counseling psychology to AI reasoning: clearing assumptions, attending to full signal, parsing multiple layers (literal, procedural, emotional, contextual, constraint, meta), reflecting understanding, noticing what is unsaid, and integrating the whole picture. Use when a user's request feels ambiguous, when context suggests something different from the literal words, when previous responses have missed the mark, or before beginning a large task where misunderstanding intent would waste significant effort.
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Alluvial gold recovery using panning, sluicing, and classification. Covers site reading (geological indicators, stream dynamics, pay streak location), panning technique, sluice box operation, and responsible extraction practices. Use when prospecting in an area with known or suspected alluvial gold deposits, sampling a stream to test for gold presence, maximizing recovery during recreational panning, or assessing a site's gold potential before investing more effort.
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Perform controlled dismantling of rigid system structures while preserving essential capabilities (imaginal discs). Covers rigidity mapping, dissolution sequencing, knowledge extraction, interface archaeology, and safe decomposition of technical debt and organizational calcification. Use when assess-form classified the system as PREPARE or CRITICAL, when a system is so calcified that incremental change is impossible, when technical debt blocks all forward progress, or before adapt-architecture when the current form must be softened before it can be reshaped.
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Extract maximum value from existing code — performance optimization, API surface refinement, and dead weight elimination. The art of turning base code into gold through systematic identification and amplification of value-bearing patterns. Use when optimizing a working but sluggish codebase, refining an API surface that has accumulated cruft, reducing bundle size or memory footprint, or preparing code for open-source release — when code works correctly but doesn't shine and needs polish rather than a full rewrite.
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Fix silent failures when parsing Grafema semantic IDs that are in URI format instead of legacy arrow format. Use when: (1) code splits semantic IDs by "->" but gets the whole string back because IDs are grafema:// URIs, (2) file path extraction from semantic IDs returns empty or wrong values, (3) derived edges (DEPENDS_ON, etc.) produce 0 results despite source edges existing, (4) any code that processes semantic IDs after the analysis pipeline's to_uri_format() has run. The grafema:// URI format uses # fragments with percent-encoded characters instead of -> separators.
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Fires after fixing any non-trivial bug or regression. Asks: could the graph have caught this as a guarantee? Pairs with reflection-in-and-on-action — picks up after "earliest catchable signal" and asks the next question: was that signal expressible in graph? Triggers: (1) after any non-trivial bug fix is verified working, (2) after a regression report (something used to work, broke), (3) during step 6 (knowledge extraction) of the workflow, (4) when reflection-on-action surfaces a "would have been catchable" signal. Outcome is a triage decision (graph-reachable? rule expressible? rule sound?) and either a draft Linear issue + guarantee proposal, or a recorded "graph capability gap" note. Never auto-creates guarantees.
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End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.
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Reverse-engineer any person's writing voice from their content. Paste in posts, articles, tweets, or essays and this skill launches 10 parallel Sonnet subagents to analyze every dimension of the voice, then synthesizes into a voice file. Use when someone says 'match this voice,' 'analyze this writing,' 'extract their voice,' 'make me sound like this,' 'study this person's writing,' or pastes in content they want to learn from.
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。
Web scraping, search, crawling, and browser automation via the Firecrawl CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, scrape a webpage, grab content from a URL, extract data from a website, crawl documentation, download a site, or interact with pages that need clicks or logins. Also use when they say "fetch this page", "pull the content from", "get the page at https://", or reference scraping external websites. This provides real-time web search with full page content extraction and cloud browser automation — capabilities beyond what Claude can do natively with built-in tools. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.
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。
Call Exa Search (POST /search) for semantic web retrieval with ranked results, filters, freshness, highlights/text, structured output, or streaming. Use when the agent needs open-ended web search, competitor discovery, news, people, or research papers and does not already have URLs. Prefer Exa MCP web_search_exa / web_search_advanced_exa when available; otherwise raw HTTP with EXA_API_KEY. NOT for known-URL extraction (use exa-contents or firecrawl) or multi-step list-building (use company-research / lead-generation / Agent API).
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。
Call Exa Contents (POST /contents) for LLM-ready extraction from known URLs: text, highlights, summaries, links, image links, subpages, freshness-controlled crawl. Use when the agent already has URLs. Prefer Exa MCP web_fetch_exa when available; otherwise raw HTTP with EXA_API_KEY. NOT for open-ended discovery (use exa-search) or auth-walled X/Twitter (use mktg-x). For full-site crawl of a known domain, firecrawl may be better.
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当用户需要找出产品/服务的独特竞争优势来写定位文案或广告主轴时调用。 适用于：产品同质化严重不知怎么差异化、新产品定位、重新审视现有产品的竞争优势。 核心信号：用户在问"我的产品和别人有什么不同""怎么让客户选我而不是对手"。 不适用于：已有明确USP只需要执行、不需要竞争分析的产品（如垄断市场）。
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 entity extraction systems for structured data from text. TRIGGERS - Use when user needs help with ai-entity-extractor related tasks.
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。