zedarvates 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)
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
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.