prompt-refiner

Refines vague coding requests into execution-ready prompts for other coding agents.

Updated May 20, 2026
One-click install
npx skills add https://github.com/TeXmeijin/agent-skills --skill prompt-refiner-texmeijin
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: prompt-refiner
Source: https://github.com/TeXmeijin/agent-skills/tree/main/.apm/skills/prompt-refiner
Command: npx skills add https://github.com/TeXmeijin/agent-skills --skill prompt-refiner-texmeijin

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Rough, vague, or frustrated implementation requests often cause coding agents to produce lazy or incorrect work. This Skill turns under-specified requests into well-grounded, copy-paste-ready prompts by verifying facts first and structuring only what matters. ## Core Features & Use Cases - Pre-investigation Pass: Verifies static facts before writing — files, symbols, GitHub Issues/PRs via gh, configs, and dates — so the refined prompt stands on confirmed evidence, not guesses. - Structured Prompt Output: Produces prompts with Background, Facts, Task, Ideal outcome, and Done condition, plus optional Constraints, Assumptions, and Open Questions. - Lazy-work Guardrails: Strengthens done conditions when a request is likely to produce shallow answers, and requires comparison when multiple solution paths exist. - Use Case: You type "the login flow is broken, fix it" — the Skill checks the actual auth code and related issues, then outputs a precise prompt with file:line references and a clear completion criterion for the next agent. ## Quick Start Ask the agent to refine your rough coding request into a prompt for another coding agent, pasting in your original request as-is.

Frequently Asked Questions about prompt-refiner

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I turn a vague coding request into a good prompt for an AI agent?

Provide your rough request and let the refinement pass verify referenced files, symbols, and GitHub issues first. The output is a copy-paste-ready prompt containing background, confirmed facts, the task, ideal outcome, and a done condition.

What should a prompt for a coding agent include?

A reliable coding-agent prompt includes background, verified facts with sources like file:line or PR numbers, a clear task, the ideal outcome, and a done condition. Add constraints, assumptions, and open questions only when they reduce ambiguity.

Does prompt refinement require access to my repository?

Repository access improves results because the pre-investigation pass confirms files, symbols, and configs actually exist. If you ask to skip investigation, the Skill honors that but marks unverified items as assumptions or open questions.

When should I not use a prompt-refining step?

Skip refinement for trivial, well-specified tasks where extra structure adds no value, or when you explicitly want the request passed through as-is. The Skill keeps small tasks small and only adds structure when risk or ambiguity is high.

Why do coding agents give lazy answers to short prompts?

Short prompts lack verified facts and completion criteria, so agents guess or stop early. Strengthening the done condition and grounding the task in confirmed file references forces more thorough execution.