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Execute this skill optimizes prompts for large language models (llms) to reduce token usage, lower costs, and improve performance. it analyzes the prompt, identifies areas for simplification and redundancy removal, and rewrites the prompt to be more conci... Use when optimizing performance. Trigger with phrases like 'optimize', 'performance', or 'speed up'.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ai-ml-engineering-pack:optimizing-promptsThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Optimize LLM prompts for reduced token usage, lower costs, and improved output quality by identifying redundancies, simplifying instructions, and restructuring for clarity.
Optimize LLM prompts for reduced token usage, lower costs, and improved output quality by identifying redundancies, simplifying instructions, and restructuring for clarity.
Refine prompts for optimal LLM performance. It streamlines prompts to minimize token count, thereby reducing costs and enhancing response speed, all while maintaining or improving output quality.
This skill activates when you need to:
User request: "Optimize this prompt for cost and quality: 'I would like you to create a detailed product description for a new ergonomic office chair, highlighting its features, benefits, and target audience, and also include information about its warranty and return policy.'"
The skill will:
User request: "Optimize this prompt for better summarization: 'Please read the following document and provide a comprehensive summary of all the key points, main arguments, supporting evidence, and overall conclusion, ensuring that the summary is accurate, concise, and easy to understand.'"
The skill will:
This skill integrates with the prompt-architect agent to leverage advanced prompt engineering techniques. It can also be used in conjunction with the llm-integration-expert to optimize prompts for specific LLM APIs.
The skill produces structured output relevant to the task.
Searches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.
npx claudepluginhub flight505/skill-forge --plugin ai-ml-engineering-pack