ai-llm-patterns
Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
536 skills found
Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.
PostgreSQL schema and migration expert for Diddit. Manages idempotent SQL files, tables, indexes, and constraints following strict camelCase conventions and transactional safety.
Manages the OpenClaw release lifecycle: prepares branches, updates versioning across multiple platforms, generates changelogs, and orchestrates npm and binary artifact publishing.
A meta-skill for building robust AI agent skills using a TDD approach: define failure (RED), implement the skill (GREEN), and plug rationalization loopholes (REFACTOR).
Evidence-first literature collector for automated research pipelines. Scales paper pools to 1200+ with metadata normalization, provenance tracking, and multi-source ingestion.
Comprehensive ABAP development support for SAP systems, covering classic ABAP, ABAP Cloud, CDS views, RAP, EML, and modern syntax patterns.
Architectural expert for the SpecKit template, managing Spec-Driven Development, design patterns, and microservices lifecycle automation.
Perform automated visual regression testing by comparing UI screenshots against established baselines to identify layout shifts, color changes, and rendering regressions.
Performs a structured five-stage code review covering requirements, correctness, code quality, testing, and security. Provides actionable, categorized feedback (Blocker/Major/Minor/Nit) to improve PR quality.
Write INVEST-compliant user stories with testable Given-When-Then acceptance criteria to bridge the gap between requirements and development.
Safely execute, test, and verify commands discovered in documentation with real output capture, performance tracking, and git-aware safety protocols.
A terminal-based Chrome DevTools Protocol client designed for AI agents. Provides direct, session-persistent control over browser navigation, DOM manipulation, scraping, and network inspection.