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概要と使いどころ
Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. This skill covers the Interactions API, the recommended way to use Gemini models and agents in Python and TypeScript.
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, generate, or scaffold GitHub Actions workflows, action.yml, or .github/workflows CI/CD pipelines.
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 scraper with SPA/JavaScript rendering, page interaction, and JS execution. Two-tier engine (HTTP → Playwright browser). Smart discovery, batch fetch, interactive content extraction, OpenAPI parsing. Use when read_url_content fails, SPA rendering needed, or page interaction required.
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。
CLI tool for AI-powered web scraping, data extraction, search, and crawling via ScrapeGraph AI. Use when the user needs to scrape websites, extract structured data from URLs, convert pages to markdown, crawl multi-page sites, search the web for information, automate browser interactions (login, click, fill forms), get raw HTML, discover sitemaps, or generate JSON schemas. Triggers on tasks involving: (1) extracting data from websites, (2) web scraping or crawling, (3) converting webpages to markdown, (4) AI-powered web search with extraction, (5) browser automation, (6) generating output schemas for scraping. The CLI is just-scrape (npm package just-scrape).
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。
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.
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。
3D interactions: production-level raycasting, pointer focus, keyboard parity. Use when implementing, reviewing or testing 3d interactions for React, Vite, Next.js or Perfect_AI projects.
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。
Microinteraction system: production-level hover, focus, error, success, loading. Use when implementing, reviewing or testing microinteraction system for React, Vite, Next.js or Perfect_AI projects.
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 drug-drug interactions — CYP metabolic interactions (substrate/inhibitor/inducer), transporter (P-gp, BCRP, OATP) effects, pharmacodynamic synergy/antagonism, clinical significance scoring, and management recommendations. Use for polypharmacy review, prescribing decision support, and safety analysis when adding or switching drugs.
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 reusable interactions, verbs, in-hand use, actions, and BUI requests with clear validation and mutation.
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 data extraction pipelines. TRIGGERS - Use when user needs help with ai-extraction-pipeline 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。
Create and configure GitHub Actions. Use when building custom actions, setting up runners, implementing security practices, or publishing to the marketplace.
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 proactively for authorization with ActionPolicy. Creates policies, scopes, and integrates with GraphQL/ActionCable. Preferred over Pundit for composable, cacheable authorization.
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。
Real-time WebSocket features with Action Cable in Rails. Use when: (1) Building real-time chat, (2) Live notifications/presence, (3) Broadcasting model updates, (4) WebSocket authorization. Trigger keywords: Action Cable, WebSocket, real-time, channels, broadcasting, stream, subscriptions, presence, cable
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 you need to write or review programmatic JDBC with Spring — including JdbcClient (Spring Framework 7+) as the default API, JdbcTemplate only where batch/streaming APIs require JdbcOperations, NamedParameterJdbcTemplate for legacy named-param code, parameterized SQL, RowMapper mapping to records, batch operations, transactions, safe handling of generated keys, DataAccessException handling, read-only transactions, streaming large result sets, and @JdbcTest slice testing. This should trigger for requests such as Review Java code for Spring JDBC (JdbcTemplate, JdbcClient, NamedParameterJdbcTemplate); Apply best practices for Spring JDBC data access in Java code; Detect and fix SQL injection risks in JDBC code; Improve transaction boundaries or exception handling for JDBC operations. Part of cursor-rules-java project
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。
Query protein-protein and gene interaction databases (STRING, BioGRID, IntAct, SIGNOR, Reactome, HuRI, HuMAP, OmniPath, ConsensusPathDB, DIP). Use when building PPI networks, choosing between physical vs functional vs genetic interactions, signed/directed vs undirected, high-throughput vs curated, picking confidence thresholds, aggregating across resources, or navigating license constraints. Encodes the database decision matrix, STRING v12 channel semantics, OmniPath as meta-database, SIGNOR for signed signaling, and per-resource rate limits.
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 distributed transaction patterns using the Saga Pattern for Spring Boot microservices. Use when implementing distributed transactions across services, handling compensating transactions, ensuring eventual consistency, or building choreography or orchestration-based sagas with Kafka, RabbitMQ, or Axon Framework.
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 working with @Transactional, multi-step database operations, distributed transactions, or any code that needs atomicity guarantees. Covers propagation rules, isolation levels, read-only optimization, and common pitfalls.
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。
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.
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。
GitHub Actions CI/CD for GNOME apps — build+test with GTK4/libadwaita on runners, Flatpak builds via flatpak-github-actions, attaching .flatpak bundles to releases, optional OCI publishing to GHCR, and headless GUI smoke tests with Xvfb/AT-SPI. Use when setting up CI, release automation, or GUI testing for a GTK app.
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 GNOME's 4-tier destructive-action pattern: undo toasts instead of confirmation dialogs, AdwAlertDialog for irreversible actions. Use when adding delete, remove, clear, or other destructive actions.
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 React Server Actions in Next.js 14+ for secure, progressively enhanced form handling and server mutations without API routes. Use when handling form submissions, performing server-side mutations, implementing progressive enhancement, creating actions that work without JavaScript, validating data on the server, revalidating cached data after mutations, handling file uploads server-side, implementing optimistic UI updates, or building forms that gracefully degrade without client-side JavaScript.
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。
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.
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。
Query protein-protein and gene interaction databases (STRING, BioGRID, IntAct, SIGNOR, Reactome, HuRI, HuMAP, OmniPath, ConsensusPathDB, DIP). Use when building PPI networks, choosing between physical vs functional vs genetic interactions, signed/directed vs undirected, high-throughput vs curated, picking confidence thresholds, aggregating across resources, or navigating license constraints. Encodes the database decision matrix, STRING v12 channel semantics, OmniPath as meta-database, SIGNOR for signed signaling, and per-resource rate limits.
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 explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps. Invoke when user mentions abstraction levels, making concepts concrete, or explaining at different depths.
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。