Awesome-omni-skill prompt-engineer

Transform rough prompts/ideas into production-ready LLM prompts. Use when crafting, refining, or optimizing prompts for any AI model (Claude, GPT, Llama, etc.) with advanced techniques like CoT, constitutional AI, RAG optimization.

install
source · Clone the upstream repo
git clone https://github.com/diegosouzapw/awesome-omni-skill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data-ai/prompt-engineer" ~/.claude/skills/diegosouzapw-awesome-omni-skill-prompt-engineer-8a66fd && rm -rf "$T"
manifest: skills/data-ai/prompt-engineer/SKILL.md
source content

Prompt Engineer

Expert prompt engineering skill that transforms rough ideas into well-structured, production-ready prompts optimized for LLMs.

When to Activate

  • User provides a rough prompt/idea and wants it refined
  • User asks to create/design/optimize a prompt for any LLM
  • User needs prompt architecture for agents, RAG, or multi-step workflows
  • User asks about prompting techniques or best practices

Workflow

1. Analyze Input

Identify from user's request:

  • Target model (Claude, GPT, Llama, etc.) — default: Claude
  • Use case (agent system prompt, task prompt, RAG, chat, etc.)
  • Domain (technical, creative, business, etc.)
  • Constraints (token limits, output format, safety requirements)

2. Apply Techniques

Select appropriate techniques from

references/techniques.md
based on use case:

  • Complex reasoning → Chain-of-Thought, Tree-of-Thoughts
  • Safety-critical → Constitutional AI patterns
  • Data extraction → Structured output, JSON mode
  • Multi-step tasks → Prompt chaining, agent patterns
  • Knowledge-heavy → RAG optimization

3. Craft the Prompt

Follow model-specific guidelines from

references/model-optimization.md
:

  • Structure with clear sections (role, context, instructions, output format)
  • Include examples where beneficial (few-shot)
  • Add constraints and guardrails
  • Optimize for token efficiency

4. Deliver Output

MANDATORY format — always include ALL sections:

The Prompt

Display complete prompt in a single copyable code block.

Implementation Notes

  • Techniques used and rationale
  • Model-specific optimizations
  • Parameter recommendations (temperature, max_tokens)
  • Expected behavior and output format

Testing & Evaluation

  • 3-5 test cases to validate
  • Edge cases and failure modes
  • Optimization suggestions

Usage Guidelines

  • When/how to use effectively
  • Customization options
  • Integration considerations

Key Principles

  • Always show the complete prompt — never just describe it
  • Token efficiency — concise but comprehensive
  • Production-ready — reliable, safe, optimized
  • Model-aware — tailor to target model's strengths
  • Refer to
    references/techniques.md
    for advanced technique details
  • Refer to
    references/model-specific-optimization-guide.md
    for model-specific guidance
  • Refer to
    references/production-patterns-and-enterprise-templates.md
    for enterprise patterns