ai-engineering
SolidReviews and guides LLM/AI application engineering: prompt design, prompt caching, multimodal inputs, RAG, agent loops and tool design, resilience (rate limits, retries, fallbacks), memory, model migration, evals, testing, prompt-injection defence, and observability. Synthesises practices from Anthropic, OpenAI, Google, OWASP LLM Top 10, and practitioners (Hamel Husain, Eugene Yan, Chip Huyen). Triggers on "review my prompt", "design a system prompt", "optimise tokens", "set up RAG", "build an agent", "handle rate limits", "migrate to a new model", "write evals", "test my prompt", "audit AI code", "/ai-engineering".
Install
Quality Score: 84/100
Skill Content
Details
- Author
- mthines
- Repository
- mthines/agent-skills
- Created
- 3 months ago
- Last Updated
- 2 days ago
- Language
- TypeScript
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
ai-prompt-engineering
Prompt engineering for production LLMs — structured outputs, evals, RAG, tool workflows, multimodal prompting, and safety. Use when designing, debugging, or shipping prompts.
prompt-engineering
Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic systems that need structured prompts.
prompt-engineering
Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.