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概要と使いどころ
Home Assistant custom integration development in Python. Covers custom_components, DataUpdateCoordinator, config_flow, OAuth2, conversation agent, HACS publishing, device registry, entity platforms, services, repair issues, diagnostics, Bluetooth integrations, and multi-coordinator 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。
Audit and improve the discoverability of the timepicker-ui documentation site (docs-app/) across the full modern discoverability stack — SEO (ranking), AEO (answer engines / featured snippets), GEO (citations inside ChatGPT/Gemini/Perplexity/Claude), and AIO (Google AI Overviews). Use when the user wants to optimize docs SEO, improve Google ranking/positioning, win featured snippets or AI-answer boxes, get the docs cited by AI engines, add or fix sitemap/robots/manifest, fill in page metadata, add Open Graph/Twitter cards, answer-first copy, or JSON-LD (FAQPage/SoftwareApplication/TechArticle/BreadcrumbList). Drives the seo-optimizer subagent with an audit → approve → apply flow.
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。
Read a known webpage or execute a discovered workflow or data-provider capability. Use for page content or structured results once the URL or tool is selected.
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 and runs reproducible evaluations for AI agents, prompts, tools, skills, and model-backed workflows using realistic datasets, isolated baselines, objective assertions, rubric grading, trajectory analysis, cost/latency tracking, and regression comparison. Use when measuring agent quality, optimizing skill triggering, comparing prompts or models, or gating an AI feature release. Not for ordinary deterministic unit tests.
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。
Threat-models and hardens an application, API, data flow, dependency, or deployment against authentication and authorization flaws, injection, secrets exposure, unsafe deserialization, SSRF, abuse, privacy loss, and supply-chain risk. Use for security review, threat modeling, hardening, or sensitive changes. Not for generic code style review or an unexplained bug without a security hypothesis.
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 Metabase cards, dashboards, Remote Sync, embedding, tenants, and the Agent API/MCP server for AI workflows. Use when scripting, promoting, or embedding Metabase content.
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 route HitKeep contributions through the repository-owned hk developer platform. Use for any HitKeep code, UI, API, database, analytics-feature, seed, screenshot, documentation, integration, or release-preparation change, as well as setup, local development, builds, smoke tests, and contributor workflow discovery.
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。
Reinforces an AI agent by turning Monte Carlo's reinforcement loop diagnosis into code fixes. Reads the daily reinforcement loop report for an agent's workflows, ranks the diagnosed issues, proposes what to fix, and — with the user's approval at each step — opens a pull request. Activates on "fix my agent", "improve my agent's health", "reinforce my agent", "what should I fix in my agent". Not for investigating a specific agent alert or trace (monte-carlo-troubleshoot-agent-traces), creating agent monitors (monte-carlo-monitoring-advisor), or instrumenting a new agent (monte-carlo-instrument-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。
Comprehending and investigating codebases: structure mapping, feature discovery, data flow tracing for 'does X exist?' or 'how does Y work?'. Includes a conversational ask mode. Does not write code.
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 TikTok ecommerce creator rankings by market and date range. Use this skill when the user asks for this exact ecommerce workflow, analysis, data lookup, or deliverable.
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 visual or keyword-derived competitors from a product image or URL across supported marketplaces. Use this skill when the user asks for this exact ecommerce workflow, analysis, data lookup, or deliverable.
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 Nexscope patents with a required Analytics query expression and return publication details and the full match count. Use this skill when the user asks for this exact ecommerce workflow, analysis, data lookup, or deliverable.
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。
Assess product imagery for TRO, trademark, patent, and copyright infringement risk. Use this skill when the user asks for this exact ecommerce workflow, analysis, data lookup, or deliverable.
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。
Route Amazon product-selection work across market, keyword, competitor, trend, review, and profitability capabilities. Use this skill when the user asks for this exact ecommerce workflow, analysis, data lookup, or deliverable.
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。
Reviews Remix v2 loaders and actions for mutations-in-loader, missing validation, leaked server fields, wrong return helpers, v1 useTransition holdovers, and revalidation traps. Use when reviewing loader/action code in a Remix v2 codebase.
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。
RIPDPI's own Compose conventions: ViewModel pattern, Route/Screen split, DataStore->StateFlow, RipDpiThemeTokens. Use for how this app does Compose, not generic Compose API questions (see compose).
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 PDF financial plan generator. Scans current working directory for FINANCE-*.md files, extracts scores and financial data, then runs the bundled Python ReportLab script to produce a polished, client-ready PDF with cover page, Financial Health Score gauge, score breakdown, cash flow, debt, retirement, portfolio, and prioritized action plan. Output: FINANCE-PLAN.pdf.
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。
Create high-quality, model-aware system prompts for any LLM (Claude, GPT, Gemini, open-source, etc.). Use this skill whenever the user wants to create, write, build, design, draft, or improve a system prompt, system instructions, or custom instructions for any AI model. Also trigger when the user asks about prompt engineering, prompt design, prompt optimization, or wants to define behavior for an AI assistant, chatbot, agent, or any LLM-powered application — even if they don't explicitly say "system prompt". Covers all use cases including chatbots, agentic systems, tool-use workflows, content generation, data extraction, code assistants, and multi-turn conversations.
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 domain knowledge and guidance for the Flare Data Connector (FDC)—attestation types, request flow, Merkle proofs, verifier/DA Layer, and smart contract integration. Use when working with FDC, cross-chain attestations, EVMTransaction, Web2Json, Payment, AddressValidity, proof-of-reserves, weather insurance, or Flare Developer Hub FDC guides and starter repos.
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 architecture reference for the A3 insurance platform — file locations, data flow, conventions, microservice map, and fullstack 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。
Design and implement an AI feature integration — model selection, architecture pattern, system prompt, data flow, error handling, cost estimate. Use when asked to "add AI to this", "LLM integration", "add Claude/GPT", or "AI-powered feature".
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。
Core programming concepts from variables through recursion. Covers data types (integers, floats, strings, booleans, arrays, objects), variables and scope (lexical, dynamic, block, function, global), control flow (conditionals, loops, pattern matching), functions (parameters, return values, closures, higher-order functions), recursion (base cases, call stack, tail recursion, mutual recursion), type systems (static vs dynamic, strong vs weak, type inference, generics), and error handling (exceptions, Result types, defensive programming). Use when teaching, reviewing, or diagnosing issues with fundamental programming constructs.
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。
Category-theoretic pipes with GF(3) flow balance checking. Validates data transformation pipelines using triadic trit algebra.
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。
Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.
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。