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概要と使いどころ
MANDATORY skill for ALL code generation. Enforces top-down composition, consistent abstraction layers, and proper module hierarchy. Apply every time you write or modify code — in any language — no exceptions.
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Write the production code that makes a pre-decided set of failing tests pass, within an explicit write_set, following an explicit behavior contract. Used by `/tdd` Step 3 and by `/swarm-dispatch` workers. The caller has already decided architecture, naming, file layout, and abstraction boundaries — this skill executes that recipe with code-structure discipline and a 5-iteration RALPH loop. No scope expansion, no design decisions, no test edits.
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 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.
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。
Any live-web task via the Firecrawl CLI — including ordinary web research: searching the web, reading or extracting pages, gathering sources, discovering site URLs, bulk extraction, downloading a site, change alerts, or pages needing clicks/login — web only; local files route to firecrawl-parse. For US legal or regulatory questions use gov; for papers use firecrawl-research-index; for library, API, error, or bug questions use firecrawl-developer-index.
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 image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.
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Provide comprehensive techniques for penetration testing AWS cloud environments. Covers IAM enumeration, privilege escalation, SSRF to metadata endpoint, S3 bucket exploitation, Lambda code extraction, and persistence techniques for red team 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。
Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.
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。
AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.
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。
Automate web scraping and data extraction with Apify -- run Actors, manage datasets, create reusable tasks, and retrieve crawl results through the Composio Apify integration.
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 natural-language crypto/DeFi agents and EVM MCP plugins (Claude Code, Cursor, Codex, Gemini). Aomi turns prompts into wallet-signed txs on Ethereum, Base, Arbitrum, Optimism, Polygon, Linea — non-custodial, fork-simulated. 40+ apps: Uniswap, Aave, Lido, Morpho, GMX, Hyperliquid, Polymarket.
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。
Review proposed abstractions, dependencies, and configuration for concrete consumers and maintenance cost when simplifying a design or checking overengineering.
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。
Extract text and tables from PDFs, fill PDF form fields programmatically, and merge multiple PDF files into one. Use when working with PDF documents, form filling, document extraction, splitting, or any PDF manipulation task.
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 GenAI and agentic apps with genai-tk (tclatos/genai-tk), a YAML-driven IoC toolkit over LangChain, DeepAgents, DeerFlow, and Prefect. Use this skill whenever the user mentions genai-tk, genai_tk, `cli init`, `cli agents run`, `LangchainAgent`, `create_harness`, `get_llm`, `global_config()`, `model_id@provider`, `agents:`/`agent_defaults:` profiles, `RetrieverFactory`, `cli workflow`, or BAML extraction. Also trigger for app_conf.yaml, GENAITK_PROFILE, CodeAct, OpenSandbox, decision models, or "the toolkit".
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 AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.
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。
Code simplification suggestions — clarity, unnecessary complexity, redundant abstractions. Advisory only.
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。
Code simplification suggestions — clarity, unnecessary complexity, redundant abstractions. Advisory only.
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 shaping a capital-raise pitch — why-now / why-us / why-this framing, market-size reasoning, traction-story construction. Triggers on 'tighten the pitch', 'why-now is weak'.
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When running discovery interviews — question-bank build, bias audit, insight extraction. Triggers on 'audit my guide', 'extract insights from transcript', 'is my hypothesis falsifiable'.
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Extraia dados estruturados de respostas de LLM com validação Pydantic, tente novamente automaticamente extrações que falharem, analise JSON complexo com segurança de tipo e transmita resultados parciais com Instructor - biblioteca de saída estruturada testada em batalha
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Use this Canvas-owned clean-room skill for PDF work where layout, extraction, generation, forms, page operations, or visual verification matter. Trigger for reading PDFs beyond plain text, creating PDFs, merging, splitting, rotating, watermarking, extracting tables or images, filling forms, OCR handoff, and final PDF QA.
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。
[Architecture] Use when grading project architecture and scalability quality for greenfield init or brownfield audit: build/CI scalability, distributed-monolith risk, module isolation, dependency discipline, loose coupling, horizontal scaling, DRY, abstraction, clean architecture, observability, and delivery.
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。
Turns an ICP or research goal into a plan for finding and scraping suitable directories. Use when the user has a target audience but is unsure where to find them, and wants source categories, queries, and an extraction workflow.
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Build spend analytics and dashboards from Qdrant vector data. Covers bulk scroll extraction, aggregation patterns, vendor grouping, and Chart.js visualization. Use when building analytics features, aggregating procurement data, or creating dashboards.
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Produce comprehensive synthesis with explicit citations from multiple ordinary public-web sources via the Tavily CLI. Use for detailed reports, comparisons, market or literature research, and questions whose answer requires multi-source web evidence rather than a quick lookup. Do not use for simple discovery (tavily-search), one known URL (tavily-extract), one-site URL discovery or bulk extraction (tavily-map/crawl), or research centered on named social, community, career, code, media, authenticated, or cross-platform sources (Agent Reach). Takes 30-120 seconds.
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。