| name | prompt-improvement |
| description | Improve vague, incomplete, ambiguous, risky, or poorly structured prompts into clear executable prompts for AI agents, coding assistants, automations, reports, research, or model-specific workflows. Use when the user asks to rewrite, clarify, optimize, score, translate, structure, or improve a prompt. |
Prompt Improvement
Turn rough intent into a clear prompt without changing what the user asked for.
Core Principle
Preserve explicit intent. Add assumptions only when labeled.
Workflow
-
Classify the problem
- Identify whether the prompt is vague, ambiguous, overbroad, missing inputs, missing output format, missing audience, risky, or already good enough.
-
Extract intent
- Separate explicit requirements, inferred goals, missing information, assumptions, constraints, and risks.
- Do not invent requirements.
-
Decide whether to ask or rewrite
- Ask 1 to 3 concise questions only when missing information changes the correct outcome.
- Proceed with assumptions for low-risk drafting, brainstorming, research, reports, templates, and clear-enough tasks.
-
Rewrite
- Make the task executable.
- Add context, requirements, process, output format, quality rules, and language guidance only when useful.
- Keep identifiers, file paths, commands, schema keys, model IDs, and API names unchanged.
-
Check fidelity
- Confirm the rewrite preserves the user's request.
- Remove hidden goals.
- Mark assumptions and open questions clearly.
Default Output
## Improved Prompt
```text
[Prompt ready to use]
What Changed
Assumptions
Questions
## Reusable Prompt Skeleton
```text
Role:
Task:
Context:
Requirements:
Process:
Output Format:
Quality Rules:
Language And Tone:
Rules
- Respond in the user's language.
- Keep reusable model-facing prompt text in English when it improves portability.
- Do not make prompts longer unless the added structure improves execution.
- Do not remove explicit constraints.
- Do not convert uncertainty into fact.
