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概要と使いどころ
Expert prompt optimization for LLMs and AI systems. Use when building AI features, improving agent performance, crafting system prompts, or optimizing LLM interactions. Masters prompt patterns and techniques.
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。
Go performance optimization techniques including profiling with pprof, memory optimization, concurrency patterns, and escape analysis.
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。
SQL query optimization and database performance specialist. Use when optimizing slow queries, fixing N+1 problems, designing indexes, implementing caching, or improving database performance. Works with PostgreSQL, MySQL, and other databases.
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。
Comprehensive performance specialist covering analysis, optimization, load testing, and framework-specific performance. Use when identifying bottlenecks, optimizing code, conducting load tests, analyzing Core Web Vitals, fixing memory leaks, or improving application performance across all layers (application, database, frontend). Includes React-specific optimization 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。
App Store and Google Play screenshot creation with exact platform specs. Covers iOS/Android dimensions, gallery ordering, device mockups, and preview videos. Use for: app store optimization, ASO, app screenshots, app preview, play store listing. Triggers: app store screenshots, aso, app store optimization, play store screenshots, app preview, app listing, ios screenshots, android screenshots, app store images, app mockup, device mockup, app gallery, store listing
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 charts, graphs, and visualizations from data. Use when the user needs to visualize data, create charts, or generate reports with graphics.
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 intelligence for code analysis. Detects SQL injection, XSS, CSRF, authentication issues, crypto failures, and more. Actions: scan, analyze, fix, audit, check, review, secure, validate, sanitize, protect. Languages: JavaScript, TypeScript, Python, PHP, Java, Go, Ruby. Frameworks: Express, Django, Flask, Laravel, Spring, Rails. Vulnerabilities: SQL injection, XSS, CSRF, authentication bypass, authorization issues, command injection, path traversal, insecure deserialization, weak crypto, sensitive data exposure. Topics: input validation, output encoding, parameterized queries, password hashing, session management, CORS, CSP, security headers, rate limiting, dependency scanning.
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。
Perform black-box / grey-box web application penetration testing on an authorized target — auth bypass, IDOR, session handling, business-logic flaws, parameter tampering, Burp Suite / OWASP ZAP workflows. Use when the user mentions 'web pentest,' 'web application penetration test,' 'pentesting,' 'bug bounty,' 'Burp Suite,' 'ZAP,' 'OWASP testing,' 'authentication testing,' 'session testing,' 'authorization testing,' 'business logic testing,' 'web vulnerability testing,' or has explicit authorization to test a live web application.
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 audit privacy controls in product and infrastructure — GDPR, CCPA / CPRA, LGPD, PIPEDA. Covers data minimization, lawful basis, consent management, data subject access requests (DSARs — access, deletion, portability), data processing agreements, DPIA / TIA, breach notification timing, data classification, and the technical implementation of 'right to be forgotten' across backups, caches, analytics, and third parties. Use when the user mentions 'GDPR,' 'CCPA,' 'CPRA,' 'data privacy,' 'privacy engineering,' 'data subject access request,' 'DSAR,' 'right to deletion,' 'right to be forgotten,' 'data portability,' 'consent management,' 'cookie consent,' 'data minimization,' 'DPIA,' 'data protection impact assessment,' 'breach notification,' 'BAA,' 'DPA,' 'data processing agreement,' 'sub-processor,' 'cross-border data transfer,' 'SCCs,' or needs to implement or review privacy controls.
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。
Reinforcement learning for trade execution optimization including order splitting, adaptive timing, and impact minimization
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。
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
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。
Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market strategies. Use for feature prioritization, user research synthesis, requirement documentation, and product strategy development.
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。
Expert guidance for GA4 privacy and compliance including GDPR, CCPA, Consent Mode v2, data deletion, and privacy settings. Use when implementing Consent Mode, ensuring GDPR compliance, handling data deletion requests, configuring consent banners, or implementing privacy-first tracking. Covers consent parameters (ad_user_data, ad_personalization), data retention, IP anonymization, and compliance workflows.
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。
Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).
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。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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 authentication and authorization with Better Auth - a framework-agnostic TypeScript authentication framework. Features include email/password authentication with verification, OAuth providers (Google, GitHub, Discord, etc.), two-factor authentication (TOTP, SMS), passkeys/WebAuthn support, session management, role-based access control (RBAC), rate limiting, and database adapters. Use when adding authentication to applications, implementing OAuth flows, setting up 2FA/MFA, managing user sessions, configuring authorization rules, or building secure authentication systems for web applications.
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 modernization and hooks system enhancement for claude-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation.
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。
Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration
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。
Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.
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。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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。
Interactive visualization with Plotly. 40+ chart types (scatter, line, heatmap, 3D, geographic) with hover, zoom, pan. Two APIs: Plotly Express (DataFrame) and Graph Objects (fine control). For static publication figures use matplotlib; for statistical grammar use seaborn.
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。
Statistical visualization on matplotlib + pandas. Distributions (histplot, kdeplot, violin, box), relational (scatter, line), categorical, regression, correlation heatmaps. Auto aggregation/CIs. Use plotly for interactive; matplotlib for low-level.
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。
Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.
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。