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概要と使いどころ
Matches natural language task descriptions to appropriate skills using semantic similarity. Handles fuzzy matching, intent extraction, and capability alignment. Activate on 'find skill', 'match task', 'semantic search', 'skill lookup', 'what skill for'. NOT for ranking matches (use dag-capability-ranker) or skill catalog (use dag-skill-registry).
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Analyze design trends and recommend appropriate styles, color palettes, and typography systems for your project based on current design movements. NOT for accessibility auditing or full design system creation.
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。
Professional CV and resume builder transforming career narratives into ATS-optimized, multi-format resumes. Integrates with career-biographer for data and competitive-cartographer for positioning. Generates PDF, DOCX, LaTeX, JSON Resume, HTML, and Markdown. Activate on 'resume', 'CV', 'ATS optimization', 'job application'. NOT for cover letters, portfolio websites (use web-design-expert), LinkedIn optimization, or interview preparation.
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。
Expert in ALL computational collage composition: photo mosaics, grid layouts, scrapbook/journal styles, magazine editorial, vision boards, mood boards, social media collages, memory walls, abstract/generative arrangements, and art-historical techniques (Hockney joiners, Dadaist photomontage, Surrealist assemblage, Rauschenberg combines). Masters edge-based assembly, Poisson blending, optimal transport color harmonization, and aesthetic optimization. Activate on 'collage', 'photo mosaic', 'grid layout', 'scrapbook', 'vision board', 'mood board', 'photo wall', 'magazine layout', 'Hockney', 'joiner', 'photomontage'. NOT for simple image editing (use native-app-designer), generating new images (use Stability AI), single photo enhancement (use photo-composition-critic), or basic image similarity search (use clip-aware-embeddings).
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。
Strategic analyst that maps competitive landscapes, identifies white space opportunities, and provides positioning recommendations. Use when users need competitive analysis, market positioning strategy, differentiation tactics, or "how do I stand out?" guidance across any domain (portfolios, products, services). NOT for market size estimation or financial forecasting.
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。
Systematic methodology for eliciting, representing, and operationalizing expert cognitive skills—pattern recognition, situation assessment, mental simulation—from practitioners without requiring research training. Bridges academic rigor and practical usability.
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 production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
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。
Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.
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。
Designs digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.
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。
Automated discovery and matching of agent skills for dynamic task routing and capability assessment
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。
Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system
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。
Capture, gate, and query long-term project knowledge via the gated wiki subsystem: propose candidates, recall, or search; agents propose, humans approve. NOT for short-lived memory (mk:memory).
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。
Fetch external sources (web/arXiv/GitHub) into scanner-gated wiki CANDIDATES, never canonical pages: guarded, capped, scanned. NOT for local knowledge (mk:wiki) or markdown fetch (mk:web-to-markdown).
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。
Fast parallel codebase scouting — searches many directories at once, returning a file map + architecture fingerprint. Use before planning or cross-dir work. NOT for deep single-file reads.
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Bootstraps a new project end to end: research, design, plan, scaffold, implement, docs. Explicit invocation only. NOT for autonomous builds (mk:autobuild); NOT for existing-repo work (mk:cook).
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。
Creates structured multi-file implementation plans before build. Scope-aware: trivial tasks exit early, simple tasks get fast plans, complex tasks get full research + phase files + validation. Enforces Gate 1. Activated by /mk:plan or /mk:cook. NOT for ticket complexity analysis against an existing codebase (see mk:planning-engine); NOT for CEO-level scope review of existing plans (see mk:plan-ceo-review).
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。
Retrieve up-to-date library, framework, and project documentation using scripts + MCP tools (Context7, Context Hub) with intelligent fallback. Use this skill whenever the user or agent needs documentation for any library, framework, API, SDK, or internal project spec. Triggers on "docs for [X]", "how does [library] work", "find documentation", "API reference for", "look up [feature] in [library]", "latest docs", "what's the API for", "find our [internal spec]", or any request that requires current, accurate documentation rather than relying on training data. Always prefer this skill over raw WebSearch for documentation retrieval — it returns structured, context-efficient results.
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 creating a new project from scratch — orchestrates the full journey from idea to running code. Research → design → plan → scaffold → implement → docs. Explicit invocation only — never auto-activates. CLI = project infrastructure (.claude/), bootstrap = application code + full pipeline. NOT for autonomous multi-hour builds of specified products (see mk:autobuild); NOT for single-task feature work on existing projects (see mk:cook).
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。
Fetch daily arXiv announcements (cs.RO), filter by research relevance, generate a structured markdown digest, and sync matched papers to a Zotero collection with arXiv PDFs attached.
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。
Semble (MinishLab) MCP wrapper — semantic code search via embeddings (98% token reduction vs grep+read, NDCG@10=0.854)
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 the project wiki as RAG knowledge source — search wiki pages to answer codebase questions before exploring raw files
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。
Search and recall memories from claude-mem
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。
Structured SE task evaluation using 15 benchmark definitions from claude-code-harness research
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。
Store and semantically search agent memory with ONNX vector embeddings on a ruOS desktop, using the Ruflo CLI. Use to persist patterns/decisions across sessions and recall them by meaning rather than exact key.
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。