Prompt Engineer

SkillMedia

Gives your agent a prompt engineer skill so it can write clearer, more effective instructions for AI models.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Prompt Engineer skill

About this skill

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.

What this skill tells your AI

The instructions your AI receives, as published by davila7/claude-code-templates in cli-tool/components/skills/ai-research/prompt-engineer/SKILL.md and read by ahel’s review.

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.

Capabilities

  • Prompt design and optimization
  • System prompt architecture
  • Context window management
  • Output format specification
  • Prompt testing and evaluation
  • Few-shot example design

Requirements

  • LLM fundamentals
  • Understanding of tokenization
  • Basic programming

Patterns

Structured System Prompt

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

Few-Shot Examples

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

Chain-of-Thought

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

Anti-Patterns

❌ Vague Instructions

❌ Kitchen Sink Prompt

❌ No Negative Instructions

⚠️ Sharp Edges

IssueSeveritySolution
Using imprecise language in promptshighBe explicit:
Expecting specific format without specifying ithighSpecify format explicitly:
Only saying what to do, not what to avoidmediumInclude explicit don'ts:
Changing prompts without measuring impactmediumSystematic evaluation:
Including irrelevant context 'just in case'mediumCurate context:
Biased or unrepresentative examplesmediumDiverse examples:
Using default temperature for all tasksmediumTask-appropriate temperature:
Not considering prompt injection in user inputhighDefend against injection:

Related Skills

Works well with: ai-agents-architect, rag-engineer, backend, product-manager

Signals

GitHub stars
32k
Forks
4k
Last commit
Sep 2026
Advanced
Item type
skill
Key
prompt-engineer-davila7
Source
github.com/davila7/claude-code-templates