1.7万 件 ・ 関連度順
概要と使いどころ
Generate 5 distinct, production-grade UI variations for a requested new or existing section, component, page, flow, or shell, then help the user preview and apply one. Use when the user asks to add or redesign UI like a hero, pricing, navbar, auth flow, dashboard shell, ecommerce surface, or data component.
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。
Evaluate an interface from a UX perspective, assessing hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring and actionable feedback. Use when the user wants an overall design or UX review—not when the main need is measurable accessibility/performance diagnosis, or final micro-detail polish.
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 optimization analysis for SEO and performance. Checks alt text, file sizes, formats, responsive images, lazy loading, and CLS prevention. Use when user says "image optimization", "alt text", "image SEO", "image size", or "image audit".
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。
Guide for creating WaveEnv narrowings in Wave Terminal. Use when writing a named subset type of WaveEnv for a component tree, documenting environmental dependencies, or enabling mock environments for preview/test server usage.
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。
Deep single-page SEO analysis covering on-page elements, content quality, technical meta tags, schema, images, and performance. Use when user says "analyze this page", "check page SEO", or provides a single URL for 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。
Programmatic SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention. Use when user says "programmatic SEO", "pages at scale", "dynamic pages", "template pages", "generated pages", or "data-driven SEO".
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。
Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical (restaurant, healthcare, legal, home services, real estate, automotive). Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "local rankings", "service area", "multi-location", or "local search".
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。
Execute plans via autonomous agents with two-stage review per task. Use for complex implementations requiring quality gates.
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。
Ripgrep-powered dead-code crawler that finds stale, broken, or orphaned JavaScript/CJS/MJS modules in the Agent Studio framework. Walks a target directory, checks each file for external references, and surfaces (or deletes) unreferenced files. Prevents registry bloat and silent tool dropouts from modules no one calls.
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。
6-phase multi-agent pipeline with approval gates. Orchestrates Plan→Design→Implement→Review→Test→Deploy phases with explicit entry/exit criteria, phase-level agent assignment, and human-in-the-loop approval checkpoints.
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。
Self-critique specification documents using extended thinking — surfaces hidden assumptions, contradictions, missing edge cases, and scope creep before implementation
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。
Write effective blameless postmortems with root cause analysis, timelines, and action items. Use when conducting incident reviews, writing postmortem documents, or improving incident response processes.
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。
Security architecture and threat modeling. OWASP Top 10 2025 analysis, OWASP Agentic AI Top 10 (ASI01-ASI10), AI/LLM security patterns, supply chain security, modern API authentication (OAuth 2.1, DPoP, Passkeys/WebAuthn), vulnerability assessment, and security review for code and infrastructure.
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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 read public Reddit posts, subreddits, and comments via the unauthenticated JSON API. Safe read-only access with SSRF-prevention hostname allowlist.
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。
Sequential thinking and structured problem solving. Break down complex problems into steps with revision and branching capabilities. Use for multi-step analysis, planning, and hypothesis verification.
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。
Orchestrate multi-LLM parallel debate and synthesis. Dispatches prompts to available omega CLI wrappers in parallel, collects independent responses, runs anonymized peer review ranking, and synthesizes via a chairman model. Supports collaboration templates, idle watchdog, inter-agent messaging, worktree isolation, and multi-turn sessions. No server 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。
Independent LLM judge evaluates task completion separately from the executing agent, catching false success claims by reviewing task goal, actions taken, final state, and evidence. Produces PASS/FAIL with confidence score and reasoning.
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 specific version compatibility notes for NativeWind and Tailwind CSS to prevent common installation errors.
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。
Feature flag lifecycle management -- safe feature toggling, gradual rollouts, A/B testing patterns, flag cleanup strategies, and technical debt prevention. Covers LaunchDarkly, Unleash, OpenFeature, and custom implementations.
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。
Background orchestrator that drains the system-wide cron-actions-queue.jsonl queue safely, preventing LLM context pollution in the primary router.
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 4-phase debugging with root cause investigation. Use when fixing bugs to prevent random fixes.
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。
Generate context-aware quality checklists for code review and QA using IEEE 1028 base standards plus LLM contextual additions
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 the Open Targets Platform GraphQL API for target-disease associations, genetic and clinical evidence, tractability and safety liabilities, target prioritisation metrics, known drugs and mechanisms of action, and disease ontology. Use this skill for target identification and validation, target-disease evidence review, druggability assessment, drug repurposing, and resolving gene, disease, and drug names to Ensembl, MONDO, and ChEMBL identifiers. Also trigger when a query mentions Open Targets, platform.opentargets.org, association scores, tractability buckets, or api.platform.opentargets.org.
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。