How does it handle ambiguity in requirements?

It uses iterative research loops to identify gaps in knowledge and then asks the user targeted questions to resolve those ambiguities before moving to the implementation phase.

When should I use this skill?

It is best used for 'brownfield' projects where you are adding significant features to an existing codebase and need to ensure compatibility and technical feasibility.

Spec Refine is a specialized skill for Claude Code that automates the process of creating detailed requirements and implementation specifications for new features through iterative research and user feedback.

Does it generate code directly?

No, it focuses on the planning phase. It produces a detailed 'How' spec and a set of actionable implementation tickets that guide the subsequent coding process.

Spec Refine: Iterative Feature Specification

Name: Spec Refine: Iterative Feature Specification
Author: dhofheinz

bydhofheinz

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Productivity & Workflow

Refines complex feature requirements and implementation plans through automated codebase research and human-in-the-loop reviews.

About

Spec Refine is a comprehensive workflow engine for Claude Code designed to bridge the gap between initial ideas and actionable implementation plans. By orchestrating a multi-phase process—including automated codebase analysis, requirements gathering, and technical design—it ensures that large features are thoroughly vetted against existing brownfield codebases. It minimizes ambiguity by iteratively researching the repository and presenting targeted questions to the developer, ultimately producing high-confidence documentation and ready-to-use implementation tickets.

Key Features

Automatic generation of actionable implementation tickets from finalized specs
Iterative codebase research loops to identify technical constraints
Convergence metrics to track specification stability and confidence levels
7 GitHub stars
Automated 'What' (requirements) and 'How' (implementation) spec generation
Human-in-the-loop review phases for resolving high-level ambiguities

Use Cases

Generating detailed documentation and JIRA-ready tickets for development teams
Planning complex new features in large, existing brownfield codebases
Translating vague business requirements into concrete technical implementation plans

About

Key Features

Automatic generation of actionable implementation tickets from finalized specs
Iterative codebase research loops to identify technical constraints
Convergence metrics to track specification stability and confidence levels
7 GitHub stars
Automated 'What' (requirements) and 'How' (implementation) spec generation
Human-in-the-loop review phases for resolving high-level ambiguities

Use Cases

Generating detailed documentation and JIRA-ready tickets for development teams
Planning complex new features in large, existing brownfield codebases
Translating vague business requirements into concrete technical implementation plans