Verified:GitHub

requirements-ingest

Name: requirements-ingest
Availability: InStock
Author: zhongadamwang

Transforms scattered requirements from PDFs, DOCX, Markdown, and plain text into atomic, traceable chunks with IDs and location hints, outputting structured JSON with classifications, confidence scores, and glossary suspects.

1|Updated Dec 16, 2025

Search Tags:#requirements #classification #traceability #PDF #DOCX #extract #ingest

Ready to run

npx skills add https://github.com/zhongadamwang/AI_Slowcooker --skill requirements-ingest

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: requirements-ingest
Source: https://github.com/zhongadamwang/AI_Slowcooker/tree/main/.github/skills/requirements-ingest
Command: npx skills add https://github.com/zhongadamwang/AI_Slowcooker --skill requirements-ingest

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill consolidates disparate requirements documents into atomic, traceable chunks with per-chunk IDs and location hints.

Core Features & Use Cases

Atomic chunking: each requirement becomes a standalone unit with a unique ID and source traceability.
Classification & traceability: tags each chunk and preserves source location references for auditability.
Glossary extraction: identifies domain terms to build a consistent terminology glossary.

Quick Start

Use this skill to process a requirements document and obtain a structured JSON with project_id, requirements, and glossary_suspects.

Frequently Asked Questions about requirements-ingest

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

FAQPage Schema

How do I extract and chunk requirements from a PDF into structured data?▼

To extract and chunk requirements from a PDF, this skill parses the document and transforms scattered text into atomic, traceable chunks. It outputs a structured JSON schema containing unique IDs, location hints, and confidence scores for each requirement.

What is atomic requirement chunking and how does it help with traceability?▼

Atomic requirement chunking breaks down large specifications into standalone units, each with a unique ID and source location reference. This granularity ensures full traceability, allowing teams to audit and track individual requirements back to their original documents.

Can I use this requirements ingestion tool with Markdown and DOCX files?▼

Yes, you can ingest requirements from Markdown and DOCX files, as well as PDFs and plain text. The skill processes these varied input formats to classify text and extract a structured JSON schema with project IDs and glossary suspects.

How do I build a glossary from domain terms found in requirements documents?▼

You can build a glossary from domain terms using the glossary extraction feature during requirement ingestion. The skill automatically identifies and isolates domain terminology, returning them as glossary_suspects within the final JSON schema for terminology management.

What is the best way to classify and structure scattered plain text requirements?▼

The best way to classify scattered plain text requirements is to ingest them through an automated chunking workflow that assigns per-chunk IDs and confidence scores. This process consolidates disparate texts into a traceable, structured JSON format for rigorous management.

Does this requirements extraction method support confidence scoring for parsed chunks?▼

Yes, the requirements extraction method supports confidence scoring for parsed chunks. The workflow evaluates each atomic chunk and includes a confidence score within the structured JSON output to help you assess extraction reliability.