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Normalize requirements from any format into consistent, chunked representation with traceability and classification.
Transforms requirements documents (PDF/DOCX/Markdown/Email) into structured, atomic chunks with classification and traceability.
project_idFiles Generated:
outputs/projects/{project_id}/Analysis/requirements.md — Markdown format for downstream skillsoutputs/projects/{project_id}/Analysis/requirements.json — JSON format for machine processingGitHub Copilot Integration (Recommended):
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:
from requirements_ingest import RequirementsIngestor
ingestor = RequirementsIngestor()
result = ingestor.process_files(["requirements.pdf"], "MY-PROJECT")
# Creates:
# outputs/projects/MY-PROJECT/requirements.md (primary)
# outputs/projects/MY-PROJECT/requirements.json (secondary)
Command Line:
python requirements_ingest.py PROJECT-001 requirements.pdf specs.docx
# Saves to: outputs/projects/PROJECT-001/
# 📄 requirements.md (Markdown - for downstream skills)
# 📋 requirements.json (JSON - for machine processing)
Primary Format (Markdown) - Following Original Specification:
# Requirements Analysis Report
**Project**: PROJECT-001
**Source**: requirements.pdf
**Generated**: 2026-02-08T14:30:00Z
**Total Requirements**: 5
## Requirements
| ID | Section | Text | Tags | Confidence |
|----|---------|------|---------|------------|
| R-001 | Authentication | System shall authenticate users within 3 seconds | functional, performance | high |
| R-002 | Performance | API response times should not exceed 200ms | nonfunctional | high |
## Glossary Suspects
- OAuth2
- API
- PCI DSS
Secondary Format (JSON) - For Machine Processing:
{
"project_id": "PROJECT-001",
"generated_at": "2026-02-08T14:30:00Z",
"total_requirements": 5,
"requirements": [
{
"id": "R-001",
"source_file": "requirements.pdf",
"location_hint": "page 3, para 2",
"text": "System shall authenticate users within 3 seconds",
"tags": ["functional", "performance"],
"confidence": 0.95
}
],
"glossary_suspects": ["OAuth2", "API", "PCI DSS"]
}
Simply paste your requirements document and ask:
@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.
Analyze requirements document using requirements-ingest methodology:
1. EXTRACT: Break into atomic requirements (max 400-600 tokens each, optimized for modern LLMs)
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:
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.
