Total 37,953 skills
Showing 12 of 37953 skills
AI agent operational rules including token discipline, navigation-first approach, and output contracts. Use when you need efficient and predictable agent behavior during development tasks.
Custom React hooks patterns including useDebounce, useLocalStorage, useMediaQuery, useClickOutside, and more. Use when creating reusable hook logic or implementing common UI patterns.
API integration patterns including REST, GraphQL, WebSocket, and tRPC. Use when designing APIs, implementing data fetching, or building real-time features.
Expert-level Rust performance optimization guidelines for build profiles, allocation, synchronization, async/await, and I/O. This skill should be used when writing, reviewing, or optimizing Rust code for performance. Triggers on tasks involving slow Rust code, large binary size, long compile times, LTO configuration, release profile tuning, allocation reduction, clone avoidance, lock contention, BufReader/BufWriter, flamegraph analysis, async runtime issues, Tokio performance, spawn_blocking, parking_lot vs std sync, or any Rust performance investigation.
Guidance for converting COBOL programs to modern languages (Python, Java, etc.) while preserving exact behavior and data format compatibility. This skill should be used when modernizing legacy COBOL applications, converting COBOL business logic to modern languages, or ensuring byte-for-byte output compatibility between COBOL and its replacement.
AI content platform for marketing teams.
Audit npm, pip, and Go dependencies that OpenClaw skills try to install. Checks for known vulnerabilities, typosquatting, and malicious packages.
Expert in Jest testing framework, advanced mocking strategies, snapshot testing, async patterns, TypeScript integration, and performance optimization
Home Assistant entity discovery, states, helpers, labels, groups, and services. Use when the user asks about entities or services, needs to rename/move/hide/label entities, or requires service calls via ha-mcp tools.
GPU-based particle systems using instanced rendering, buffer attributes, Points geometry, and custom shaders. Use when rendering thousands to millions of particles efficiently, creating particle effects like snow, rain, stars, or abstract visualizations.
Use when working with Codable protocol, JSON encoding/decoding, CodingKeys customization, enum serialization, date strategies, custom containers, or encountering "Type does not conform to Decodable/Encodable" errors - comprehensive Codable patterns and anti-patterns for Swift 6.x
Use when profiling async/await performance, diagnosing actor contention, or investigating thread pool exhaustion. Covers Swift Concurrency Instruments template, task visualization, actor contention analysis, thread pool debugging.