prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production applications.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
528 skills found
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production applications.
Execute implementation plans in separate sessions with review checkpoints, ensuring task-by-task verification and robust code quality.
Meta-skill for structured, multi-depth codebase exploration including architectural analysis, fast structural overviews, and deep-dive documentation workflows.
React Native performance optimization guide covering FPS, TTI, bundle size, memory leaks, and profiling patterns based on Callstack's industry-standard expertise.
Official MoonBit language reference, syntax guide, and coding conventions for building high-performance software projects.
Token-efficient codebase analysis skill for call graphs, semantic search, impact analysis, and data flow. Saves ~95% tokens vs. raw reads.
Resume a paused experimental loop by restoring branch context, loading configuration, reading history, and identifying optimization patterns for continued iteration.
A comprehensive configuration toolkit for Claude Code featuring battle-tested agents, skills, hooks, and automation workflows for software development.
Expert guidance for Google Ads Script development including AdsApp API, campaign management, keyword bidding, automated rules, performance reporting, and spend optimization.
Generate optimized SQL queries from natural language. Supports BigQuery, PostgreSQL, MySQL, and Snowflake. Analyze database schemas, interpret business requirements, and output ready-to-run queries with explanations.
Analyze GA4 and GSC performance data with automated benchmarks, status indicators, and actionable content optimization insights.
Get deep, critical, NeurIPS/ICML-style peer reviews of your research, paper drafts, and experimental setups using external LLMs via Codex MCP.