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概要と使いどころ
Classify and advance issues or external pull requests through the configured triage workflow. Use explicitly to inspect queues, evaluate an item, recommend a state, or apply an authorized transition.
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。
Recommend the smallest Matt Pocock skill or short skill sequence that fits the user's current goal.
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 one bounded deep dive on a single risky component of a system design and returns options, failure modes, and a justified recommendation. Use during a design session when a component needs expert depth beyond the main thread's budget.
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 the user asks which /phx-* command fits a task (review, plan, debug, test) or how two commands differ. Checks plans and git state, then recommends from the routing table. Not for bare /help, a tour, or ambiguous requests (intent-detection).
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。
Recommend the right $elixir-phoenix:phx-* workflow. Use when choosing a plugin skill, not for Codex /help.
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。
Recommend safe Bash permissions for Elixir mix commands in; Use when permission prompts slow workflow, "fix…
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 the user asks which phx-* command fits a task (review, plan, debug, test) or how two commands differ. Checks plans and git state, then recommends from the routing table. Not for bare /help, a tour, or ambiguous requests (intent-detection).
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。
Recommend safe Bash permissions for Elixir mix commands in settings.json. Use when permission prompts slow workflow, "fix permissions", "reduce prompts", "auto-allow mix".
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。
Recommend safe Bash permissions for Elixir mix commands in settings.json. Use when permission prompts slow workflow, "fix permissions", "reduce prompts", "auto-allow mix".
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。
Analyze skill effectiveness across sessions. Computes per-skill metrics (action rate, friction, outcomes), identifies degrading skills, and generates improvement recommendations. Requires session-scan data in metrics.jsonl.
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 the user asks which /phx: command fits a task (review, plan, debug, test) or how two commands differ. Checks plans and git state, then recommends from the routing table. Not for bare /help, a tour, or ambiguous requests (intent-detection).
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。
Analyzes album state and recommends the optimal next action. Use when the user asks "what should I do next?" or "what's left to do?"
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。
A complete marketing department in one skill. Website and landing-page audits with weighted 0-100 scores, copywriting with panel scoring and AI-slop removal, an 18-tactic ad hook engine, GEO/AEO for getting cited by ChatGPT/Perplexity/AI Overviews, paid-ads creative diagnosis and production briefs, email sequences, LinkedIn/X writing, launch playbooks, positioning and offer design, competitor teardowns, app store optimization, honest analytics and test design. Use for ANY marketing task — audit, write, rewrite, diagnose, score, plan, launch, position, price, analyze — whenever the user mentions marketing, growth, conversion, copy, ads, hooks, CPM, ROAS, SEO, GEO, email, social, landing pages, funnels, launches, competitors, brand, positioning, pricing, or app stores, or says "my landing page sucks", "nobody's converting", "why are my CPMs up", "AI doesn't recommend us", "write me 20 hooks". Route via the table inside; fan out subagents for multi-dimensional work. Not for pure engineering, legal, or finance.
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 `hf` CLI for Hub authentication, downloads, uploads, repositories, cache, jobs, buckets, webhooks, and endpoint administration. Use for CLI operations, not model recommendations, paper analysis, training design, or building a Space.
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。
Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.
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。
Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.
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。
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
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。
Builds tables and data grids for displaying tabular information, from simple HTML tables to complex enterprise data grids. Use when creating tables, implementing sorting/filtering/pagination, handling large datasets (10-1M+ rows), building spreadsheet-like interfaces, or designing data-heavy components. Provides performance optimization strategies, accessibility patterns (WCAG/ARIA), responsive designs, and library recommendations (TanStack Table, AG Grid).
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。
Image prompting skill for Nano Banana (NBP/NB2) and GPT Image 2.5 (Flare/Sunburst). Writes ready-to-use prompts with model/quality/size recommendations. Use when: "нарисуй", "сгенерируй картинку", "image prompt", "промпт для картинки", blog covers, slides, posters, product shots, UI mockups, storyboards, character sheets, edit/colorize, style transfer, vision analysis, image-to-prompt, nb, NBP, NB2, gpt-image-2.5, multi-panel grids, ecommerce product photography, fashion editorial, food/beverage ads, cinematic portraits. Do NOT use for: video (use video skill), 3D models, audio, non-image 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。
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).
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。
Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring clarity of recommendations.
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。
Estimate and communicate business impact of insights. Use when sizing opportunities discovered in analysis, calculating ROI of recommended actions, or prioritizing initiatives by potential impact.
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。
Analyze Google Business Profile visibility, geographic grid rankings, local search keywords, and nearby competitors through the standalone Google Maps CLI. Use for local SEO or location-strategy decisions, not ordinary place recommendations.
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。
This skill should be used when performing web research beyond a simple single search -- looking into topics, comparing options, investigating questions, finding recommendations, or any task where effective use of Exa, Firecrawl, and Reddit tools matters. Triggers on "research", "look into", "investigate", "compare", "find out about", "search for", "find information", "what do people think about", "what are the best", "look up", or multi-source search tasks. Also invocable explicitly by deep-research team members via the Skill tool.
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。