What input formats does Spec Analyzer support?

Spec Analyzer supports markdown, plain text, and structured text formats to identify headers, requirements, and project sections.

Can it identify dependencies between features?

Yes, the skill analyzes the context of your specifications to detect which features depend on others, such as authentication requirements.

How does the skill define feature priority?

It uses a 1-5 scale ranging from Critical (core functionality) to Backlog (future considerations) based on explicit or inferred importance.

Does it handle non-functional requirements?

Yes, it is designed to identify and extract hidden features like error handling, performance benchmarks, and accessibility standards.

Is the output compatible with automation tools?

Absolutely. It generates a standardized JSON schema designed to be parsed by siftcoder or other developer workflow automation tools.

Spec Analyzer

Name: Spec Analyzer
Author: ialameh

byialameh

생산성 및 워크플로

Transforms natural language specification documents into structured, testable feature sets for automated implementation.

소개

Spec Analyzer is a specialized Claude Code skill designed to bridge the gap between high-level project requirements and technical execution. By analyzing markdown, plain text, or structured documentation, it extracts granular, implementable features complete with testable acceptance criteria, priority rankings, and dependency mapping. This skill ensures that every requirement is translated into an actionable deliverable, identifying often-overlooked details like error handling and accessibility while providing a standardized JSON output that streamlines the transition from planning to active coding.

주요 기능

Generation of clear, testable acceptance criteria for every requirement
Automated feature extraction from markdown and text specifications
0 GitHub stars
Intelligent priority assignment and dependency mapping for sequencing
Detection of hidden requirements like edge cases and validation rules
Standardized JSON output for integration with implementation workflows

사용 사례

Translating natural language feature requests into technical requirements
Converting a project MVP document into a structured feature backlog
Breaking down complex epics into small, session-sized coding tasks

소개

주요 기능

Generation of clear, testable acceptance criteria for every requirement
Automated feature extraction from markdown and text specifications
0 GitHub stars
Intelligent priority assignment and dependency mapping for sequencing
Detection of hidden requirements like edge cases and validation rules
Standardized JSON output for integration with implementation workflows

사용 사례

Translating natural language feature requests into technical requirements
Converting a project MVP document into a structured feature backlog
Breaking down complex epics into small, session-sized coding tasks