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Found 4,067 Skills
Streamlines bug fixing by creating a GitHub issue first, then a feature branch for implementing and thoroughly testing the solution before merging.
Identify risky assumptions for a feature idea in an existing product across Value, Usability, Viability, and Feasibility. Uses multi-perspective devil's advocate thinking. Use when stress-testing a feature idea, doing risk assessment, or preparing for assumption mapping.
Complete CI/CD guide for Cloudflare Workers using GitHub Actions and GitLab CI. Use for automated testing, deployment pipelines, preview environments, secrets management, or encountering deployment failures, workflow errors, environment configuration issues.
Deep persona design for Agentforce agents with 50-point scoring. TRIGGER when: user designs agent personas, defines agent personality/identity, creates persona documents, encodes persona into Agent Builder fields, or asks about agent tone/voice/register. DO NOT TRIGGER when: building agent metadata (use sf-ai-agentforce), testing agents (use sf-ai-agentforce-testing), or Agent Script DSL (use sf-ai-agentscript).
Create a minimal working Vast.ai example. Use when starting a new Vast.ai integration, testing your setup, or learning basic Vast.ai API patterns. Trigger with phrases like "vastai hello world", "vastai example", "vastai quick start", "simple vastai code".
/em -stress-test — Business Assumption Stress Testing
Use this skill when implementing data validation, data quality monitoring, data lineage tracking, data contracts, or Great Expectations test suites. Triggers on schema validation, data profiling, freshness checks, row-count anomalies, column drift, expectation suites, contract testing between producers and consumers, lineage graphs, data observability, and any task requiring data integrity enforcement across pipelines.
Consumer-side guide for integrating @lodev09/react-native-true-sheet into a React Native app. Use this skill whenever the user wants to add, configure, control, or debug a bottom sheet using TrueSheet — including ref-based sheets, named global sheets, web support with TrueSheetProvider/useTrueSheet, React Navigation or Expo Router sheet flows, Reanimated-driven animations, scrolling content, stacking, headers/footers, detents, side sheets, keyboard handling, dimming, liquid glass, and Jest testing. Also use when the user is migrating from v2 to v3, troubleshooting layout or gesture issues, or asking about any TrueSheet prop, event, or method — even if they don't mention "TrueSheet" by name but describe a bottom sheet in a React Native context.
Comprehensive Rust coding guidelines covering ownership, error handling, async patterns, traits, testing, performance, clippy, and documentation. Use when writing new Rust code, reviewing or refactoring existing Rust, implementing async systems with Tokio, designing error hierarchies, choosing between borrowing and cloning, setting up tests or benchmarks, configuring linting, or optimizing performance. Do not use for non-Rust languages or general software architecture unrelated to Rust idioms.
A complete guide to developing MusicFree desktop theme packs from scratch. It is triggered when users request to write, create, design MusicFree desktop theme packs, or ask to generate themes based on reference images, color schemes, or style keywords. It covers the full process including CSS variable system, color design paradigms, static themes, dynamic iframe themes, resource optimization, packaging testing, and submission to the theme market. This Skill is designed for AI execution, guiding AI to collaborate with community contributors (who may have no front-end experience) to complete theme pack development.
Use when working on Hono projects or adding Hono into a codebase. Enforces Hono architecture rules for app composition, route modules, middleware, validation, error handling, testing, and typed RPC boundaries before any code change.
Build type-safe LLM applications with DSPy.rb — Ruby's programmatic prompt framework with signatures, modules, agents, and optimization. Use when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.