prompt-optimizer

Files (4)

• LICENSE.txt
• SKILL.md
• examples.md · references/
• prompt-best-practices.md · references/

SKILL.md (first lines)

# Prompt Optimizer

## Overview

This skill transforms user-provided prompts into high-quality, clear, and effective instructions optimized for AI models. Apply proven prompt engineering principles to enhance clarity, specificity, structure, and effectiveness. The skill uses a systematic workflow to analyze, identify improvement opportunities, and restructure prompts based on industry best practices.

## When to Use This Skill

Activate this skill when users:
- Explicitly request prompt optimization or improvement
- Provide vague or unclear instructions that need refinement
- Ask for help making their requests more effective
- Submit poorly structured prompts that would benefit from reorganization
- Request guidance on how to better communicate with AI models
- Present complex tasks that need to be broken down into clearer instructions

## Optimization Workflow

Follow this systematic process to optimize any prompt:

### Step 1: Analyze the Original Prompt

Examine the user's prompt and identify:

**Clarity issues:**
- Ambiguous terms or vague requirements
- Implicit assumptions that should be explicit
- Missing context or background information

**Specificity gaps:**
- Lack of concrete constraints or requirements
- Undefined success criteria
- Missing audience or purpose information
- Unclear scope or boundaries

**Structure problems:**
- Disorganized or stream-of-consciousness format
- Missing logical flow
- Lack of clear sections or hierarchy

**Format considerations:**
- No specified output format
- Unclear expectations about length, tone, or style
- Missing examples or templates

**Complexity assessment:**
- Determine if the task is too complex for a single prompt
- Identify if the request would benefit from prompt chaining
- Assess if step-by-step reasoning is needed

### Step 2: Identify the Core Intent

Determine the fundamental objective behind the user's request:

- What is the user ultimately trying to accomplish?
- What problem are they trying to solve?
- What would constitute a successful output?
- Who is the intended audience or consumer of the output?

Clarify these points with the user if they are not evident from the original prompt.

### Step 3: Apply Optimization Principles

Enhance the prompt using these core principles:

**Make it clear and direct:**
- State requirements explicitly without assuming inference
- Remove ambiguity and vague language
- Use concrete, specific terms

**Provide context and motivation:**
- Explain WHY certain requirements matter
- Include relevant background information
- Describe the use case or scenario

**Add specificity:**
- Define concrete constraints (length, format, scope)
- Specify target audience
- Include quality criteria
- State any limitations or boundaries

**Structure the request:**
- Organize information logically
- Use clear sections or numbered points
- Separate different types of information (context, requirements, format)

**Include examples when helpful:**
- Provide input-output examples for complex formats
- Show desired tone or style through examples
- Demonstrate edge case handling

**Allow for uncertainty:**
- Explicitly permit expressing "I don't know"
- Request acknowledgment of limitations
- Prevent hallucination by encouraging honesty

### Step 4: Consider Advanced Techniques

Evaluate if any advanced techniques would enhance the prompt:

**Chain of Thought:**
- Apply when the task requires reasoning or analysis
- Request step-by-step thinking for complex problems
- Use structured format to separate reasoning from answer

**Prefilling:**
- Use when a specific format is absolutely required (JSON, XML)
- Apply to eliminate unwanted preambles
- Utilize to establish immediate tone or style

**Prompt Chaining:**
- Break complex tasks into sequential steps
- Create a multi-stage workflow for intricate projects
- Design each prompt to build on previous outputs

**Structured Output:**
- Specify exact format requirements
- Provide schemas or templates
- Use tags or delimiters for different sections

From ckanner/agent-skills, Apache-2.0.

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