Prompt Optimizer

Purpose: Transform a rough, vague, or inconsistent AI prompt into a high-quality, optimized version. Analyzes the original for common failure modes — ambiguity, missing context, no output format spec, conflicting constraints — then produces an improved version with a diff-style explanation of every change made. Works for any LLM (Claude, GPT, Gemini, Llama).

Invocation

/optimize-prompt

Paste your prompt after the command or in the next message.

What Gets Analyzed

1. Clarity and Specificity

• Is the task underspecified? ("write some code" vs "write a TypeScript function that...")
• Are there ambiguous pronouns or references?
• Does it state what a "good" output looks like?

2. Context

• Is there missing background the model needs to answer well?
• Should a persona or role be set? ("You are a senior security engineer...")
• Is the input format specified?

3. Output Format

• Is the expected format defined (JSON, Markdown, bullet list, numbered steps)?
• Is length/verbosity specified?
• Are examples of the desired output provided?

4. Constraints

• Are there conflicting or redundant instructions?
• Are hard constraints clearly marked vs soft preferences?
• Is scope limited to prevent scope creep?

5. Chain-of-Thought

• Would the model benefit from "think step by step"?
• Should it show its reasoning or just the answer?

Output Format

## Original Prompt

[your original prompt, quoted]

---

## Issues Found

| # | Issue | Severity | Category |
|---|-------|----------|----------|
| 1 | [description] | High/Med/Low | Clarity/Context/Format/Constraints |

---

## Optimized Prompt

[the improved prompt, ready to copy-paste]

---

## What Changed

- **[Change 1]:** [explanation of why this improves the prompt]
- **[Change 2]:** [explanation]

---

## Tips for This Type of Prompt

[1-3 specific tips for prompts in this domain]

Rules

• Never change the intent — only improve how it's expressed
• Show every change — the explanation section must account for all edits
• If the original prompt is already well-optimized (score ≥ 8/10), say so and note only minor suggestions
• Rate the original prompt 1-10 for context (e.g., "Original: 4/10 — Optimized: 9/10")
• If the prompt is for a specific model (Claude, GPT-4, etc.), apply model-specific best practices
• Don't over-engineer simple prompts — sometimes "write a haiku about coffee" doesn't need improvement

Playground

<span style='color:#8b949e'>a list of numbers and removes</span> <span style='color:#8b949e'>duplicates and also sorts it</span> <span style='color:#8b949e'>and returns it, in python</span></pre></div><div class='panel'><div class='label'>Output: Optimized prompt</div><pre><span style='color:#58a6ff'>Write a Python function with this</span> <span style='color:#58a6ff'>signature:</span>

<span style='color:#3fb950'>def dedupe_sorted(nums: list[int])</span> <span style='color:#3fb950'> -> list[int]:</span>

<span style='color:#58a6ff'>Requirements:</span> <span style='color:#8b949e'>- Remove duplicate values</span> <span style='color:#8b949e'>- Return elements in ascending order</span> <span style='color:#8b949e'>- Do not modify the input list</span> <span style='color:#8b949e'>- Handle empty list (return [])</span> <span style='color:#8b949e'>- Type-annotated, no external imports</span>

<span style='color:#58a6ff'>Example:</span> <span style='color:#8b949e'>dedupe_sorted([3,1,2,1,3]) → [1,2,3]</span> <span style='color:#8b949e'>dedupe_sorted([]) → []</span></pre></div></div></body></html>