aka. Agent Skills
Discover skills for AI coding agents. Works with Claude Code, OpenAI Codex, Gemini CLI, Cursor, and more.
PII detection and masking for LLM observability. Use when logging prompts/responses, tracing with Langfuse, or protecting sensitive data in production LLM pipelines.
Reference patterns for parsing skill metadata. Use when extracting phases, examples, or features from SKILL.md files for demo generation
Use when backing up, restoring, or validating golden datasets. Prevents data loss and ensures test data integrity for AI/ML evaluation systems.
Use when validating golden dataset quality. Runs schema checks, duplicate detection, and coverage analysis to ensure dataset integrity for AI evaluation.
LLM output evaluation and quality assessment. Use when implementing LLM-as-judge patterns, quality gates for AI outputs, or automated evaluation pipelines.
OKR framework, KPI trees, leading/lagging indicators, and success metrics patterns. Use when defining goals, measuring outcomes, or building measurement frameworks.
User stories, acceptance criteria, PRDs, and requirements documentation patterns. Use when translating product vision to engineering specs, writing user stories, or creating requirements documents.
Comprehensive API design patterns for REST, GraphQL, and gRPC. Use when designing APIs, creating endpoints, adding routes, implementing pagination, rate limiting, or authentication patterns.
RFC 9457 Problem Details for standardized HTTP API error responses. Use when implementing problem details format, structured API errors, error registries, or migrating from RFC 7807.
VCR.py HTTP recording for Python tests. Use when testing Python code making HTTP requests, recording API responses for replay, or creating deterministic tests for external services.
Enforce testing best practices - AAA pattern, naming conventions, isolation, coverage thresholds. Blocks non-compliant tests. Use when writing or reviewing tests.
Advanced pytest patterns including custom markers, plugins, hooks, parallel execution, and pytest-xdist. Use when implementing custom test infrastructure, optimizing test execution, or building reusable test utilities.
Property-based testing with Hypothesis for discovering edge cases automatically. Use when testing invariants, finding boundary conditions, implementing stateful testing, or validating data transformations.
Mock Service Worker (MSW) 2.x for API mocking. Use when testing frontend components with network mocking, simulating API errors, or creating deterministic API responses in tests.
Long-term semantic memory across sessions using Mem0. Use when you need to remember, recall, or forget information across sessions, or when referencing what we discussed last time or in a previous session.
TAM/SAM/SOM market sizing, Porter's Five Forces, competitive analysis, and SWOT frameworks. Use when sizing market opportunities, analyzing competition, or assessing industry dynamics.
LangGraph checkpointing and persistence. Use when implementing fault-tolerant workflows, resuming interrupted executions, debugging with state history, or avoiding re-running expensive operations.
Automatic GitHub issue progress updates from commits and sub-task completion. Use when tracking issue progress from commits or automating status updates.
Integration testing patterns for APIs and components. Use when testing component interactions, API endpoints with test databases, or service layer integration.
Image optimization with Next.js 16 Image, AVIF/WebP formats, blur placeholders, responsive sizes, and CDN loaders. Use when improving image performance, responsive sizing, or Next.js image pipelines.