Design effective prompts for large language models - chain-of-thought reasoning, few-shot examples, role assignment, XML structure, output format control, temperature tuning, and prompt chaining for multi-step tasks. Covers both general LLM prompting and model-specific best practices for Claude, GPT-4o, and Gemini. Use when writing system prompts, designing AI agent instructions, improving LLM output quality, reducing hallucinations, or engineering prompts for production AI applications.
How to install llm-prompt-engineering?
jignesh-ponamwar/skills-mcp/llm-prompt-engineering
$npx -y skills add jignesh-ponamwar/skills-mcp --skill llm-prompt-engineeringInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/jignesh-ponamwar/skills-mcp" --skill "jignesh-ponamwar/skills-mcp/llm-prompt-engineering"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/jignesh-ponamwar/skills-mcp" that are relevant to the current task. Run `npx skills add "https://github.com/jignesh-ponamwar/skills-mcp"` and select the relevant skills, then follow their instructions.
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