prompt-optimizer

Optimize raw user prompts by identifying intent and missing requirements for ECC components.

Updated Sep 13, 2025
One-click install
npx skills add https://github.com/llmh333/employee_management_spring --skill prompt-optimizer-llmh333
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: prompt-optimizer
Source: https://github.com/llmh333/employee_management_spring/tree/main/.gemini/skills/prompt-optimizer
Command: npx skills add https://github.com/llmh333/employee_management_spring --skill prompt-optimizer-llmh333

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill turns a rough or underspecified user prompt into a clear, actionable, ready-to-paste prompt that better matches the ECC component ecosystem, reducing ambiguity and improving output quality.

Core Features & Use Cases

  • Intent & gap analysis: Detects what the user is trying to do and identifies missing requirements (context, constraints, acceptance criteria).
  • ECC component alignment: Maps the user’s intent to the appropriate ECC components (skills/commands/agents/hooks) and proposes a workflow that fits the task type.
  • Advisory-only optimized prompts: Produces an optimized prompt while explicitly avoiding execution and avoiding refactoring/performance “wrong-interpretation” traps.

Use case example: You paste a vague request like “help me improve this instruction for building an endpoint,” and the Skill generates an optimized prompt with the right planning, TDD/review/verify guidance, and clear boundaries.

Quick Start

Use prompt-optimizer to optimize your draft prompt by pasting it and asking to “rewrite this prompt for ECC.”

Frequently Asked Questions about prompt-optimizer

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

FAQPage Schema
What is prompt optimization and how does intent detection improve task planning?

Prompt optimization analyzes your raw prompt to identify intent and missing requirements like context and constraints, producing a clear, actionable prompt that aligns with appropriate task planning workflows for better execution quality.

How do I rewrite a vague instruction into a runnable prompt for coding and testing?

To rewrite a vague instruction, paste your draft request and ask to rewrite it for ECC; the system detects missing TDD guidance and review boundaries, generating a ready-to-paste optimized prompt for coding, testing, and verification workflows.

Can I use prompt rewriting for multilingual requests with Chinese triggers?

Yes, you can use prompt rewriting for multilingual requests including Chinese triggers. The system detects intent across languages and maps the request to appropriate ecosystem components to produce an optimized, runnable prompt output.

Does the optimized prompt execute code or refactor directly within the workflow?

No, the optimized prompt operates with strict advisory-only behavior. It produces a ready-to-paste prompt with clear boundaries and do-not-trigger safeguards, explicitly avoiding execution or refactoring wrong-interpretation traps during task planning.

What's the best way to align a raw prompt with planning, review, and verification workflows?

The best way to align a raw prompt is to analyze its intent and map it to appropriate ECC components, which proposes a workflow that fits the task type and integrates planning, review, and verification guidance into the final optimized output.