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.
156 skills found
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production applications.
Transforms vague or poorly structured prompts into optimized, high-performance instructions using proven prompt engineering principles for better AI model execution.
Intelligent unit and integration test generation powered by Minion framework, featuring business logic validation, boundary testing, and Vitest integration.
Advanced prompt rewriting and optimization service. Analyzes prompts for clarity, specificity, and structure, providing actionable improvements, variations for testing, and prompt engineering best practices.
Orchestrate Codex CLI for efficient parallel coding, task automation, and session-managed workflows to optimize token usage and development speed.
Create, refine, and optimize high-quality YAML prompts for AI assistants using structure guidelines, template patterns, and quality standards.
Search, discover, and refine AI prompts using the prompts.chat library. Access thousands of community-curated prompts for ChatGPT, Claude, and other AI models.
Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.
Migrate your codebase, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to the advanced Opus 4.5 model with automated configuration adjustments.
A RAG-based AI solver for high school Chinese GSAT exams, featuring structured knowledge retrieval, reasoning templates, and explainable AI outputs.
Enriches vague prompts by performing codebase research and asking targeted questions to clarify user intent before execution.
Diagnose, isolate, and mitigate LLM context failures like lost-in-middle, poisoning, distraction, and context clash to improve agent reliability.