Prompt Engineering

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name: prompt-engineering description: Comprehensive prompt engineering framework for designing, optimizing, and iterating LLM prompts. This skill should be used when users request prompt creation, optimization, or improvement for any LLM task, or when users need help translating vague requirements into effective prompts through collaborative dialogue and iterative refinement.

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Prompt Engineering

Overview

This skill transforms vague user requests into precise, effective prompts through collaborative dialogue, systematic analysis, and iterative refinement. It combines proven prompt engineering techniques with a structured development process to create prompts that reliably achieve user objectives.

Workflow Decision Tree

When a user requests prompt assistance, follow this decision flow:

User Request
├─ "Create a prompt" / "Make a prompt" / Vague request
│  └─ → Start with EXPLORATION PHASE
├─ "Optimize this prompt" / Has existing prompt
│  └─ → Start with SIMPLE OPTIMIZATION
└─ "Fix this issue with my prompt" / Specific problem
   └─ → Start with ANALYSIS PHASE (focused on problem)

Core Process

Phase 1: Exploration - Uncovering True Needs

Before creating any prompt, deeply understand the user's actual needs through strategic questioning. Start broad, then narrow down systematically.

Initial Context Gathering:

What task will this prompt accomplish?

Who will use it and in what environment?

How frequently will it be used?

What does success look like?

Deepening Understanding:

Request concrete examples of desired outputs

Ask about past failures or attempts

Identify critical success factors

Uncover unstated assumptions and constraints

Technical Requirements:

Model and platform constraints

Token limits and cost considerations

Response time requirements

Integration with other systems

Continue exploration until the core requirements are crystal clear. Never assume—always verify.

Phase 2: Analysis - Choosing the Right Strategy

Analyze the task to determine the optimal prompting approach.

Task Classification:

Classify the task along key dimensions:

Complexity: Simple directive vs multi-step reasoning

Output Type: Creative vs analytical vs structured

Error Tolerance: High-stakes vs experimental

Frequency: One-time vs repeated use

Strategy Selection:

Based on classification, choose primary techniques:

Simple Tasks: Direct instructions with clear constraints

Complex Reasoning: Chain-of-thought with step-by-step breakdown

Creative Tasks: Role setting with flexible boundaries

Structured Output: Explicit format specifications with examples

High-Stakes: Self-consistency checks and validation steps

Trade-off Analysis:

Present multiple approaches with clear trade-offs:

Approach A: Detailed but token-heavy

Approach B: Concise but requires interpretation

Approach C: Balanced with moderate complexity

Always explain WHY each approach fits the specific context.

Phase 3: Implementation - Building Iteratively

Create the prompt through progressive refinement, starting simple and adding complexity as needed.

Version 1 - Minimal Viable Prompt:

Core instructions only

Test basic functionality

Identify gaps and ambiguities

Version 2 - Enhanced Clarity:

Add specific examples if needed

Clarify ambiguous points

Include essential constraints

Version 3+ - Optimization:

Refine wording for precision

Remove redundancy

Balance detail with conciseness

Document each version's changes and rationale. Store prompts in markdown files with:

Version history

Design decisions

Known limitations

Usage examples

Phase 4: Validation - Critical Evaluation

Rigorously evaluate the prompt against quality criteria.

Essential Checks:

Clarity: Can the instructions be misunderstood?

Completeness: Are all necessary elements present?

Consistency: Do instructions contradict each other?

Efficiency: Can anything be removed without loss?

Robustness: How does it handle edge cases?

Testing Approach:

Run through typical use cases

Test boundary conditions

Imagine failure modes

Check for unwanted behaviors

Be ruthlessly honest about weaknesses. If something isn't working, acknowledge it and iterate.

Simple Optimization

When optimizing an existing prompt, focus on minimal, targeted improvements:

1. Identify Specific Issues: What exactly isn't working? 2. Diagnose Root Causes: Why is the current prompt failing? 3. Apply Minimal Edits: Change only what's necessary 4. Preserve Working Elements: Keep what already works well 5. Test Improvements: Verify fixes don't break other aspects

Common optimization targets:

Ambiguous language → Specific instructions

Missing constraints → Added boundaries

Inconsistent outputs → Format specifications

Verbose responses → Length constraints

Off-topic responses → Clearer scope definition

Prompt Creation from Scratch

When creating new prompts, structure them as instructions for an eager but inexperienced assistant who needs clear guidance.

Essential Components:

1. Role/Context (if beneficial): - Set perspective or expertise level - Establish tone and approach 2. Clear Objective: - State the primary goal explicitly - Define success criteria

3. Specific Instructions: - Break complex tasks into steps - Provide decision criteria - Specify constraints and boundaries

4. Output Format (when relevant): - Define structure explicitly - Provide format examples - Specify length or detail level

5. Examples (when clarifying): - Show desired patterns - Illustrate edge cases - Demonstrate style/tone

Key Techniques Reference

Foundation Techniques

Role Setting: Establish perspective when expertise or tone matters

Effective for: Specialized knowledge, consistent voice

Example: "As an experienced code reviewer, analyze..."

Progressive Disclosure: Start general, add detail as needed

Effective for: Complex multi-part tasks

Example: "First outline the approach, then implement each section..."

Explicit Constraints: Define boundaries clearly

Effective for: Preventing unwanted outputs

Example: "Limit response to 3 paragraphs, focus only on technical aspects"

Advanced Techniques

Chain-of-Thought: Request reasoning before conclusions

Use when: Logic and transparency matter

Trigger: "Think step-by-step" or "Explain your reasoning"

Few-Shot Learning: Provide input-output examples

Use when: Pattern is easier shown than explained

Caution: 2-3 examples usually sufficient

Self-Consistency: Have model verify its own outputs

Use when: Accuracy is critical

Implementation: "Review your answer for errors and inconsistencies"

For detailed technique explanations and examples, consult:

references/techniques.md - Comprehensive technique catalog

references/patterns.md - Common prompt patterns

references/antipatterns.md - What to avoid

Collaboration Principles

Be a Thought Partner, Not Just an Executor

Bad: "Here's your prompt" (without understanding needs)

Good: "Let me understand what you're trying to achieve first..."

Question Assumptions Constructively

Surface hidden requirements through dialogue

Challenge unclear objectives respectfully

Propose alternatives when original approach seems suboptimal

Iterate Based on Feedback

Start with minimum viable prompt

Test and refine based on actual outputs

Document what works and what doesn't

Teach While Doing

Explain why certain techniques work

Share the reasoning behind design choices

Help users understand prompt engineering principles

References

This skill includes detailed reference documentation:

references/

techniques.md - Complete catalog of prompting techniques with examples

patterns.md - Reusable prompt patterns for common scenarios

antipatterns.md - Common mistakes and how to avoid them

evaluation.md - Comprehensive quality evaluation framework

examples.md - Library of before/after prompt improvements

Consult these references for in-depth technical details and extensive examples not included in this overview.