prompt-optimizer

name

prompt-optimizer

description

Improves existing prompts for better AI output quality: clearer instructions, better output format specification, role/persona addition, few-shot examples, and chain-of-thought encouragement. Use this skill whenever a user says "improve this prompt", "why is this prompt not working well?", "make this prompt better", "optimize this prompt for Claude/GPT", "my prompt gives bad results", "rewrite this prompt", "add examples to this prompt", or "help me get better outputs from this prompt". Also activate when a user shares a prompt and seems frustrated with the AI's outputs. Do NOT use for building full prompt libraries (use prompt-library-curator) or creating skills (use skill-creator).

Prompt Optimizer

Diagnose why a prompt produces poor outputs and rewrite it for clarity, specificity, and consistent high-quality results.

When to Use

• An existing prompt gives inconsistent, vague, or off-target outputs
• A prompt works sometimes but not reliably
• You want to improve a prompt before scaling its use
• You want to adapt a prompt for a different model (GPT → Claude, etc.)

When NOT to Use

• Organizing a prompt library (use prompt-library-curator)
• Building a new skill from scratch (use skill-creator)
• Running or evaluating prompts (use eval-runner)

---

Workflow

Step 1 — Receive the Prompt and Context

Ask the user for:

1. The current prompt (copy-paste)
2. Example of a bad output (what did the AI produce that was wrong?)
3. What a good output looks like (describe or show an example)
4. Target model: Claude / GPT-4 / Other
5. Constraints: Max tokens, format requirements, domain restrictions

Step 2 — Diagnose the Problem

Evaluate the prompt against common failure patterns:

Problem	Signals	Fix
Too vague	Outputs are generic or off-topic	Add specificity, context, and constraints
No output format	Format varies wildly	Specify exact format (JSON, bullet list, table)
No role/persona	Wrong expertise level	Add "You are an expert [domain]..." opener
No examples	Misunderstands task	Add 1–2 few-shot examples
Prompt too long	AI ignores later instructions	Move critical instructions to the beginning
Passive/hedging	AI adds disclaimers everywhere	Add "Be direct. Do not add disclaimers."
Wrong scope	Too broad or too narrow	Define explicit scope with "Include:" and "Exclude:"
No negative examples	AI does the thing you don't want	Add "Do NOT..." instructions

Step 3 — Apply Improvement Patterns

Pattern 1: Role + Task + Format

[Before]
"Summarize this article."

[After]
"You are a research analyst. Summarize the following article in exactly 3 bullet points,
each under 15 words. Focus on: the main argument, key evidence, and conclusion.
Do not include your own opinions."

Pattern 2: Add Output Schema

[Before]
"Extract the key information from this email."

[After]
"Extract the key information from the following email and return it as a JSON object
with these exact keys: sender, subject, action_required (true/false), deadline (ISO date or null),
priority (low/medium/high). Return ONLY the JSON, no explanation."

Pattern 3: Few-Shot Examples

[Before]
"Classify the sentiment of this review."

[After]
"Classify the sentiment of customer reviews. Return only: positive, negative, or neutral.

Examples:
Review: 'The product broke after a week.' → negative
Review: 'Great quality, fast shipping!' → positive
Review: 'It works as described.' → neutral

Now classify this review:
{review}"

Pattern 4: Chain-of-Thought (for reasoning tasks)

[Before]
"Is this email a phishing attempt?"

[After]
"Analyze whether the following email is a phishing attempt. Think step by step:
1. Check the sender domain
2. Look for urgency or fear language
3. Check links for domain mismatches
4. Look for requests for credentials or payment
Then give your verdict: phishing / likely phishing / legitimate. Explain your reasoning in 2 sentences."

Step 4 — Present Before/After

Show:

1. The original prompt
2. The improved prompt with annotations explaining each change
3. Why each change should improve output quality

Step 5 — Suggest Variants

Offer 2 alternatives:

• One with minimal changes (conservative)
• One with more aggressive restructuring

---

Output Format

## Prompt Optimization Report

### Original Prompt

[original]

### Diagnosis
- [Issue 1]: [Explanation]
- [Issue 2]: [Explanation]

### Improved Prompt

[improved]

### Changes Made
1. Added role: "[role]" — to anchor expertise and tone
2. Specified output format: "[format]" — to eliminate variability
3. Added constraint: "[constraint]" — to prevent [specific bad output]

### Alternative Variant (minimal changes)

[alternative]

---

Safety & Confirmation

• Never change the fundamental intent of a prompt without confirming with the user.
• Flag if an improved prompt is longer than the original — longer isn't always better.
• For prompts used in automated pipelines, note that changes should be A/B tested before full rollout.