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RambleRainbow
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prompt-optimizer

Analyze raw user prompts and map tasks to ECC components.

Updated May 9, 2026
Search Tags:#workflow automation#prompt engineering#claude code#prompt optimization#ai assistant#ecc#task prompting
One-click install
npx skills add https://github.com/RambleRainbow/jd --skill prompt-optimizer-ramblerainbow
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-optimizer
Source: https://github.com/RambleRainbow/jd/tree/main/.claude/skills/prompt-optimizer
Command: npx skills add https://github.com/RambleRainbow/jd --skill prompt-optimizer-ramblerainbow

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Users often write vague or incomplete prompts for Claude Code ECC, leading to inefficient task execution, missed best practices, or incorrect use of commands, skills, and agents. This skill eliminates that friction by turning rough, unstructured task descriptions into optimized, production-ready prompts tailored to ECC's ecosystem.

Core Features & Use Cases

  • Intent & Scope Analysis: Automatically classifies user tasks (new feature, bug fix, refactor, etc.) and assesses scope (trivial to epic) to recommend the right workflow.
  • ECC Component Matching: Maps task requirements to the correct ECC commands, skills, agents, and recommended models based on tech stack and intent.
  • Multi-Language Support: Triggers on both English and Chinese prompt optimization requests, with localized trigger phrases.
  • Use Case: A developer with a rough prompt like "add user login" gets a fully optimized prompt with /plan, /tdd, /code-review steps, tech stack-specific skills, and clear acceptance criteria, ready to paste and run.

Quick Start

Use the prompt-optimizer skill to turn your rough task description into a fully optimized ECC prompt.

Frequently Asked Questions about prompt-optimizer

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

FAQPage Schema
How do I optimize rough prompts for Claude Code to include proper workflows and acceptance criteria?▼

To optimize raw prompts for Claude Code, the skill analyzes task intent and scope, then maps requirements to appropriate ECC commands, skills, and agents. It outputs copy-paste ready prompts with structured workflow steps, acceptance criteria, and scope boundaries aligned with ECC best practices.

What is the best way to structure a vague task description for an AI assistant before running it?▼

Structuring a vague task description involves classifying the task intent, assessing scope, and matching requirements to workflow components. The skill transforms unstructured requests into production-ready ECC instructions with recommended models, commands, and clear scope boundaries for efficient execution.

Does prompt optimization work with multi-language task descriptions for workflow automation?▼

Prompt optimization supports both English and Chinese task descriptions for workflow automation. The skill triggers on localized phrases to analyze raw input, identify missing context, and output optimized ECC prompts tailored to the specified tech stack and task type.

How do I map task requirements to the correct ECC commands, skills, and agents?▼

Mapping task requirements to ECC components is done by analyzing the raw prompt to classify task type and scope. The skill automatically matches your tech stack and intent to the correct ECC commands, skills, agents, and recommended models, generating a fully structured prompt.

Can I turn a rough feature request into a production-ready prompt with test-driven development steps?▼

Yes, you can turn a rough feature request into a production-ready prompt. The skill evaluates the input intent and generates an optimized prompt with structured workflow steps like /tdd and /code-review, specific ECC skills, and clear acceptance criteria ready to paste and run.

Why do unstructured AI assistant prompts lead to inefficient task execution and missed best practices?▼

Unstructured AI assistant prompts lack necessary context and scope boundaries, causing inefficient execution. The skill solves this by analyzing raw input for missing context, mapping requirements to ECC best practices, and outputting optimized prompts with explicit acceptance criteria and workflow steps.

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