# Prompt Improver

> Rewrite a rough prompt into a professional, structured prompt (role, context, task, instructions, constraints, output format, success criteria) using a LOCAL model — 0 cloud tokens — with markdown or strict JSON prompt output. Use when the user wants to improve/boost/structure a prompt, mentions "/p-amelioration", "prompt pro", "JSON prompt", prompt engineering, or wants a reusable prompt template built locally.

- Skill: `zedarvates/prompt-improver` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add zedarvates/prompt-improver`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zedarvates/prompt-improver/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: zedarvates (https://skillmd.com/u/zedarvates)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/zedarvates/prompt-improver

---


# prompt_improver — pro structured prompts, built locally

Improving a prompt is text transformation, not hard reasoning — so it runs on a
**local model for 0 cloud tokens** (`llm_backends`). Output as structured markdown
or a strict **JSON prompt object**.

## Run it (the /p-amelioration entry point)

```bash
python -m skills.prompt_improver.cli "make my code faster"
python -m skills.prompt_improver.cli "summarize this PR" --json
python -m skills.prompt_improver.cli "..." --no-local        # deterministic scaffold only
```

```python
from skills.prompt_improver import improve
improve("rends mon code plus rapide", as_json=True)["json_prompt"]   # cloud_tokens: 0
```

## The structure (well-established prompt-engineering practice)

| Field | Purpose |
|-------|---------|
| `role` | who the model acts as |
| `context` | only the background that matters |
| `task` | one explicit objective |
| `instructions` | ordered steps |
| `constraints` | hard rules / what NOT to do |
| `output_format` | exact shape of the answer |
| `examples` | optional few-shot `{input, output}` pairs |
| `success_criteria` | machine-checkable "done" conditions |

## How it behaves

- **With a local backend:** the model rewrites the rough prompt into the schema
  (markdown or strict JSON, robustly parsed even through code fences).
- **Without one (or `--no-local`):** returns a deterministic scaffold so the call
  never fails — then suggest installing a local model (see [[llm_backends]]).

Exposed via [[llm_mcp]] as the `improve_prompt` tool. Pairs well with
[[skill_finder]] (find tools for the improved task) and [[auto_router]] (run it
locally). Related global skills: prompt-engineering, prompt-builder, boost-prompt.