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prompt-engineer

prompt-engineer

github.com/davila7Agent workflows33.0k1.5k installsUpdated today
Repository

About prompt-engineer

Prompt Engineer is a skill for designing and refining instructions for LLMs. It covers prompt structure, examples, context and output constraints, along with systematic testing to assess prompt changes.

Key features

  1. 01Design and optimize prompts
  2. 02Structure system prompts
  3. 03Manage context windows
  4. 04Specify output formats
  5. 05Test prompts and evaluate results
  6. 06Create few-shot examples

Use cases

  1. 01Turn an ambiguous request into explicit instructions
  2. 02Define a consistent structured response format
  3. 03Compare prompt revisions through evaluation
  4. 04Add examples and edge cases to guide model output

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/davila7/claude-code-templates --skill prompt-engineer

After installing, type/prompt-engineerin Claude Code to confirm it loaded.

Details

From the project README

Prompt Engineer

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

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