Requirements Ingest

Transforms requirements documents (PDF/DOCX/Markdown/Email) into structured, atomic chunks with classification and traceability.

Core Function

Input: Raw files + project_id Output: JSON with chunked requirements, each tagged and traceable to source

Usage

GitHub Copilot Integration (Recommended):

markdown

Use this skill directly in Copilot by providing requirements documents.
Copilot will automatically extract and classify requirements using its built-in AI.

Example prompt:
"Use requirements-ingest skill to process this requirements document and return structured JSON with atomic requirements, classifications, and traceability."

Traditional Script Approach:

python

from requirements_ingest import RequirementsIngestor
ingestor = RequirementsIngestor()
result = ingestor.process_files(files=["requirements.pdf"], project_id="PRJ-001")

Output Schema

ALWAYS return exactly this JSON structure:

json

{
  "project_id": "string",
  "requirements": [
    {
      "id": "R-001",
      "source_file": "requirements.pdf",
      "location_hint": "page 3, para 2",
      "text": "The system shall authenticate users within 2 seconds",
      "tags": ["functional", "performance"],
      "confidence": 0.95
    }
  ],
  "glossary_suspects": ["authentication", "API", "dashboard"]
}

GitHub Copilot Integration

Direct Usage in Copilot Chat

Simply paste your requirements document and ask:

code

@workspace Use the requirements-ingest skill to process this document:

[PASTE YOUR REQUIREMENTS DOCUMENT HERE]

Project ID: MY-PROJECT-001

Extract atomic requirements with:
- Unique IDs (R-XXX format)  
- Source traceability
- Classification tags
- Confidence scores
- Glossary terms

Return structured JSON following the schema.

Copilot Prompt Template

code

Analyze requirements document using requirements-ingest methodology:

1. EXTRACT: Break into atomic requirements (max 200 tokens each)
2. CLASSIFY: Tag as functional|nonfunctional|constraint|assumption|out-of-scope  
3. TRACE: Preserve source location (section, page, paragraph)
4. SCORE: Confidence 0.0-1.0 based on clarity
5. GLOSSARY: Identify domain terms (2+ occurrences)

Output exact JSON schema with project_id, requirements array, glossary_suspects.

Advantages of Copilot Integration:

•✅ No API Keys Required: Uses Copilot's built-in AI capabilities
•✅ Context Aware: Understands your workspace and project context
•✅ Interactive: Can ask follow-up questions and refine results
•✅ Integrated Workflow: Works seamlessly with your development process

AI Classification Prompt

code

Classify each requirement using these tags:

- **functional**: What the system does (features, user actions, behaviors)
- **nonfunctional**: How well it does it (performance, security, usability)
- **constraint**: External limitations (budget, technology, regulations)
- **assumption**: Dependencies and prerequisites
- **out-of-scope**: Explicitly excluded items

Multiple tags allowed. Explain reasoning for complex cases.

Processing Rules

•Chunk Size: Max 200 tokens per requirement
•Atomic: One verifiable requirement per chunk
•Traceability: Preserve source file + location hint
•Confidence: 0.0-1.0 based on clarity and context