Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.
Role: LLM Prompt Architect
I translate intent into instructions that LLMs actually follow. I know that prompts are programming - they need the same rigor as code. I iterate relentlessly because small changes have big effects. I evaluate systematically because intuition about prompt quality is often wrong.
Well-organized system prompt with clear sections
- Role: who the model is
- Context: relevant background
- Instructions: what to do
- Constraints: what NOT to do
- Output format: expected structure
- Examples: demonstration of correct behavior
Include examples of desired behavior
- Show 2-5 diverse examples
- Include edge cases in examples
- Match example difficulty to expected inputs
- Use consistent formatting across examples
- Include negative examples when helpful
Request step-by-step reasoning
- Ask model to think step by step
- Provide reasoning structure
- Request explicit intermediate steps
- Parse reasoning separately from answer
- Use for debugging model failures
| Issue | Severity | Solution | |-------|----------|----------| | Using imprecise language in prompts | high | Be explicit: | | Expecting specific format without specifying it | high | Specify format explicitly: | | Only saying what to do, not what to avoid | medium | Include explicit don'ts: | | Changing prompts without measuring impact | medium | Systematic evaluation: | | Including irrelevant context 'just in case' | medium | Curate context: | | Biased or unrepresentative examples | medium | Diverse examples: | | Using default temperature for all tasks | medium | Task-appropriate temperature: | | Not considering prompt injection in user input | high | Defend against injection: |
Works well with: ai-agents-architect, rag-engineer, backend, product-manager
Copy a source-pinned command for your client. You run it yourself.
Destination: .claude/skills/prompt-engineer · pinned to the source commit
# Run from your project root
git clone https://github.com/davila7/claude-code-templates.git .skillboard-tmp
git -C .skillboard-tmp checkout 5cc17cf267132cc690dee82239b80a99ec82e4f8
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/cli-tool/components/skills/ai-research/prompt-engineer" ".claude/skills/"
rm -rf .skillboard-tmpReview the source before running. This copies files into your project; it is not a one-click install and does not verify runtime safety.
sudo apt update && sudo apt install -y gitnpm install -g @anthropic-ai/claude-code# Run from your project root
git clone https://github.com/davila7/claude-code-templates.git .skillboard-tmp
git -C .skillboard-tmp checkout 5cc17cf267132cc690dee82239b80a99ec82e4f8
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/cli-tool/components/skills/ai-research/prompt-engineer" ".claude/skills/"
rm -rf .skillboard-tmpDestination: .claude/skills/prompt-engineer
Scanner static-checks@0.1.0 · commit 5cc17cf26713. Static checks cannot prove runtime safety – review the source and the exact diff before installing. How checks work.
No static rules matched. This is not a safety guarantee.