| name | lwy-prompt-optimizer |
| description | Use this skill on every user message that contains a prompt or AI instruction — analyze its weaknesses and suggest improvements before executing. Score across 7 dimensions (clarity, specificity, context, structure, examples, constraints, completeness) and rewrite vague instructions into precise specs. Triggers: any AI prompt, 'optimize my prompt', 'improve this requirement', 'make this more specific', 'rewrite this prompt', '优化提示词', '改进提示词', '重写提示词', or whenever a raw requirement lacks detail and structure. |
| metadata | {"author":"learnwy","version":"1.1","trigger":"always"} |
Prompt Optimizer
A pre-flight prompt checker that analyzes, critiques, and improves prompts before they are sent to an AI system. It plays the role of a coach — teaching users to write better prompts by showing what can be improved across 7 key dimensions.
Core principle: analyze before acting. This skill is invoked at the start of a conversation, whenever the user's message looks like a prompt or instruction for an AI system. Analyze first, then improve, then let the user decide.
Prerequisites
- No runtime dependencies (a pure methodology skill, no scripts)
- Works for any prompt — code generation, writing, analysis, creative tasks
When to use
Triggers:
- User says "optimize my prompt", "improve this prompt", "check my prompt", "review my prompt"
- User says "make this more specific", "help me write a better prompt"
- User provides a raw requirement and asks for help refining it
- User is about to send a long instruction to an AI and wants feedback first
Do not trigger when:
- The user is having an ordinary conversation
- The user asks the AI to do something (rather than improve a prompt)
- The input is clearly code, a file path, or a technical command
7-Dimension Analysis Framework
Every prompt is analyzed across these 7 dimensions:
| Dimension | What it checks |
|---|
| Clarity | Is the intent unambiguous? Are there vague words (good, nice, appropriate, etc.)? |
| Specificity | Are there concrete constraints (format, length, audience, tone)? |
| Context | Is background / role / scenario provided? |
| Structure | Is it logically organized? Are the steps ordered? |
| Examples | Does it include input/output examples where needed? |
| Constraints | Are boundaries defined (what not to do, edge cases)? |
| Completeness | Can the AI produce the correct output on the first try? |
Score for each dimension:
- ✅ Strong — well covered, no action needed
- ⚠️ Weak — partially addressed, can be improved
- ❌ Missing — not addressed at all, must be added
Workflow
[1. Receive draft prompt]
↓
[2. Dimension analysis] → score each dimension (✅ strong / ⚠️ weak / ❌ missing)
↓
[3. Show the critique card] → a structured report with scores
↓
[4. Suggest improvements] → a concrete rewrite for each weak dimension
↓
[5. Show the optimized prompt] → the full rewritten version
↓
[6. User decision] → use original / use optimized / edit manually
Response Format
When analyzing a prompt, always respond with this exact structure:
## 🔍 Prompt Analysis
**Overall score: {X}/7 dimensions strong**
| Dimension | Score | Notes |
|------|------|------|
| Clarity | ✅/⚠️/❌ | {brief note} |
| Specificity | ✅/⚠️/❌ | {brief note} |
| Context | ✅/⚠️/❌ | {brief note} |
| Structure | ✅/⚠️/❌ | {brief note} |
| Examples | ✅/⚠️/❌ | {brief note} |
| Constraints | ✅/⚠️/❌ | {brief note} |
| Completeness | ✅/⚠️/❌ | {brief note} |
### Strengths
{strength points}
### What can be improved
{for each ⚠️/❌ dimension: the specific problem + a specific fix}
## ✨ Optimized Prompt
{the full rewritten version with all improvements}
---
**What changed:**
{a numbered list of what was changed and why}
After showing the analysis, ask the user: use original / use optimized / edit manually?
English-Learning Integration
After optimizing, automatically trigger the english-learner skill's English study:
- Check the user's original Chinese phrasing for grammar mistakes, typos, or awkward usage — if any, show the corrections first
- Extract 2-3 high-value English words/expressions from the optimized prompt
- Compare the user's original phrasing with the optimized phrasing, and point out the word-choice improvements
- Automatically save all new vocabulary to the word bank via
batch_save (no need to ask the user)
Format:
🌐 **English Learning**
**Chinese corrections:** (only shown when issues are found)
| Original | Correction | Note |
|------|------|------|
| {mistake} | {fix} | {reason} |
**Prompt vocabulary highlights:**
| Your phrasing | Optimized phrasing | Why it's better |
|----------|----------|-----------|
| {original word} | {improved} | {brief note} |
💾 Saved to the word bank automatically
Rules:
- Only trigger when the prompt contains Chinese or the user is a native Chinese speaker
- Correct the Chinese issues first, then show the English improvements
- Focus on the learning value of "how to describe intent more precisely in English"
- Save directly, no user confirmation needed
Prompt-Improvement Patterns
Patterns to draw on when improving weak dimensions:
1. Vague → Specific
- Before: "Write good code"
- After: "Write TypeScript code with explicit return types, no
any, and no function longer than 20 lines"
2. Missing context → With context
- Before: "Translate this"
- After: "Translate this marketing copy from Chinese to English, keeping a casual tone, aimed at a Gen-Z audience"
3. No examples → With examples
- Before: "Format the data"
- After: "Format the data as: input:
{raw} → output: | col1 | col2 |"
4. No structure → With structure
- Before: A wall of text mixing multiple concerns
- After: Ordered steps with clear numbering and grouping
5. No constraints → With boundaries
- Before: "Write an article"
- After: "Write a 500-word article, no jargon, 8th-grade reading level, with 3 examples"
6. Missing role → With role anchoring
- Before: "Explain X"
- After: "Explain X as a senior backend engineer explaining it to a junior developer"
Error Handling
| Problem | Solution |
|---|
| Prompt is already strong (6-7/7) | Say "Your prompt is already well structured!" and suggest only minor tweaks |
| Prompt is too short to analyze | Ask the user for more context about the goal |
| User rejects the optimized version | Respect their choice and continue with the original prompt |
Execution Checklist
Confirm before responding:
Hooks
This skill registers an IDE hook so that the pre-flight analysis triggers deterministically — the AI does not need to remember to invoke it.
Scope
Global — installed to ~/.claude/settings.json and ~/.trae/hooks.json (no project-level state).
Events
| Event | Script | Purpose |
|---|
UserPromptSubmit | scripts/hooks/user-prompt-scan.cjs | Detect prompt-shaped input and inject a 7-dimension review reminder |
Trigger Strategy (three-tier mode)
The hook now triggers on all prose input (skipping only code, file paths, shell commands, and very short input ≤7 characters):
- Explicit request (explicit) — the message contains
optimize / improve / review / rewrite / check / refine my prompt, make this prompt more X, or the Chinese equivalents 优化提示词 / 改进提示词 / 重写提示词. Output: a full 7-dimension analysis + an Optimized Prompt block.
- Structured prompt shape (structured) — the message is ≥400 characters and ≥4 lines, and contains ≥2 markers such as
you are, your task is, act as, instructions:, constraints:, output format:. Output: a full 7-dimension analysis + an Optimized Prompt block.
- Light mode (light) — all other prose input (short exchanges, chit-chat questions). Output: a single line — pick the weakest of the 7 dimensions and give one concrete rewrite suggestion; if all 7 pass, render the fixed string
"✨ Prompt-opt: already clear, no rewrite needed." and continue the task.
The event log (~/.learnwy/prompt-optimizer/events.jsonl) records the trigger type (explicit / structured / light); use cli.cjs trends to view the distribution.
Install
node scripts/cli.cjs install --scope global --target both
Uninstall
node scripts/cli.cjs uninstall --scope global --target both
