Does it clean the extracted data for better processing?

Yes, it automatically removes markdown artifacts like backticks from file paths, trims whitespace, and preserves necessary newlines to ensure the data is ready for use in CLI tools.

Which Flowspec commands rely on this skill?

This skill is a core component of the Flowspec ecosystem, primarily invoked by the /flow:implement, /flow:generate-prp, and /flow:validate commands.

Can I use this with custom PRD templates?

Yes, as long as your PRD includes a '## All Needed Context' header and follows the standard markdown table formats for subsections like Code Files or Gotchas.

How does the Context Extractor handle missing PRD sections?

The skill is designed to be robust; if a section or table is missing or empty, it returns an empty array for that specific category, ensuring the JSON structure remains consistent for downstream tools.

PRD Context Extractor

Name: PRD Context Extractor
Author: jpoley

byjpoley

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Productividad y Flujo de Trabajo

Parses Product Requirements Documents (PRDs) to extract structured implementation context for AI-driven development workflows.

Acerca de

The Context Extractor is a specialized tool for Claude Code designed to bridge the gap between human-readable requirements and machine-executable tasks. It automatically parses the 'All Needed Context' section of PRD files, converting markdown tables into structured JSON containing code references, documentation links, implementation examples, potential gotchas, and external system dependencies. This structured output ensures that Claude has all necessary background information for implementation, validation, and pull request preparation, significantly reducing manual context gathering and improving the accuracy of AI-generated code.

Características Principales

Automated parsing of Markdown tables into machine-readable JSON
Robust error handling for malformed tables or missing document sections
Priority-based extraction of relevant source code files and documentation
Seamless integration with /flow:implement and /flow:validate commands
8 GitHub stars
Identification of 'Gotchas' and prior failures to prevent technical debt

Casos de Uso

Validating that all referenced files and external systems in a requirement doc exist
Generating comprehensive context bundles for complex feature implementation
Automating the ingestion of project context from PRDs into AI coding assistants

Acerca de

Características Principales

Automated parsing of Markdown tables into machine-readable JSON
Robust error handling for malformed tables or missing document sections
Priority-based extraction of relevant source code files and documentation
Seamless integration with /flow:implement and /flow:validate commands
8 GitHub stars
Identification of 'Gotchas' and prior failures to prevent technical debt

Casos de Uso

Validating that all referenced files and external systems in a requirement doc exist
Generating comprehensive context bundles for complex feature implementation
Automating the ingestion of project context from PRDs into AI coding assistants