How does it handle missing data in the PRD?

If a section or table is missing or empty, the skill returns an empty array ([]) for that category to maintain JSON schema consistency.

Can I use this skill with any Markdown file?

Yes, as long as the file follows the expected 'All Needed Context' heading structure and uses markdown tables for data, it can parse any .md file.

Does it support nested JSON objects?

The skill flattens table rows into a list of objects based on the specific schema defined for each context subsection (e.g., path, purpose, and priority for code files).

What specifically does this skill look for in a PRD?

The skill searches for a second-level heading titled '## All Needed Context' and parses specific subsections like Code Files, Docs, Examples, Gotchas, and External Systems.

Is this skill required for Flowspec commands?

Yes, it is a core component invoked by /flow:implement, /flow:generate-prp, and /flow:validate to gather necessary technical context.

Context Extractor

Name: Context Extractor
Author: jpoley

byjpoley

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Productivity & Workflow

Transforms Product Requirements Documents (PRDs) into machine-readable JSON context for automated development workflows.

About

The Context Extractor skill bridges the gap between human-readable project specifications and automated execution by parsing Markdown-based PRDs. It specifically targets 'All Needed Context' sections to identify relevant code files, documentation, implementation examples, and external system dependencies. By converting these unstructured tables into structured JSON, it enables Claude and the Flowspec framework to intelligently prioritize file analysis, avoid known pitfalls, and validate technical requirements before implementation begins.

Key Features

Data Sanitization: Strips markdown artifacts like backticks and extra whitespace for clean programmatic use.
Priority Mapping: Captures 'Read Priority' levels to optimize how AI models consume codebase context.
Markdown Table Parsing: Converts complex markdown tables into structured, machine-readable JSON objects.
Error Diagnosis: Provides specific remediation steps for missing sections or malformed markdown syntax.
5 GitHub stars
Categorized Extraction: Automatically organizes data into code files, documentation, examples, gotchas, and external systems.

Use Cases

Verifying that all prerequisite files and documentation links exist before starting a development sprint.
Automating the preparation of context bundles for the /flow:generate-prp workflow.
Providing structured implementation guidance to Claude during /flow:implement tasks.

About

Key Features

Data Sanitization: Strips markdown artifacts like backticks and extra whitespace for clean programmatic use.
Priority Mapping: Captures 'Read Priority' levels to optimize how AI models consume codebase context.
Markdown Table Parsing: Converts complex markdown tables into structured, machine-readable JSON objects.
Error Diagnosis: Provides specific remediation steps for missing sections or malformed markdown syntax.
5 GitHub stars
Categorized Extraction: Automatically organizes data into code files, documentation, examples, gotchas, and external systems.

Use Cases

Verifying that all prerequisite files and documentation links exist before starting a development sprint.
Automating the preparation of context bundles for the /flow:generate-prp workflow.
Providing structured implementation guidance to Claude during /flow:implement tasks.