doc2qra

Convert any document into Question-Reasoning-Answer pairs with a summary.

Input → Summary + QRA pairs → Memory

Quick Start

# PDF → QRA with summary
./run.sh --file paper.pdf --scope research

# With domain focus (recommended for better relevance)
./run.sh --file paper.pdf --scope research --context "ML researcher"

# Preview before storing
./run.sh --file paper.pdf --dry-run

# URL → QRA
./run.sh --url https://example.com/article --scope web

# Text file → QRA
./run.sh --file notes.txt --scope project

What It Does

1. Extract content from PDF/URL/text
2. Summarize the document (2-3 paragraph overview)
3. Split into logical sections
4. Generate Q&A pairs via LLM (parallel batch)
5. Validate answers are grounded in source
6. Store summary + QRAs to memory

Parameters

--file

--url

--scope

--context

--dry-run

--json

--sections-only

--summary-only

Output Format

When using

--json

{
  "summary": "A 2-3 paragraph summary of the document...",
  "extracted": 15,
  "stored": 15,
  "sections": 8,
  "source": "paper.pdf",
  "scope": "research",
  "qra_pairs": [
    {"problem": "What is...", "solution": "The document explains..."},
    ...
  ]
}

Examples

# Research paper with context
./run.sh --file arxiv_paper.pdf --scope research --context "ML researcher"

# Technical documentation
./run.sh --file api_docs.md --scope project --context "backend developer"

# Just get the summary
./run.sh --file paper.pdf --summary-only

# From extractor output (pipeline integration)
./run.sh --from-extractor /path/to/extractor/results --scope research

Environment Variables (Optional Tuning)

DOC2QRA_PDF_MODE

DOC2QRA_CONCURRENCY

DOC2QRA_GROUNDING_THRESH

DOC2QRA_NO_GROUNDING

Migration from distill/qra/doc-to-qra

This skill consolidates the functionality of:

• distill

  → Use

  doc2qra

  instead

• qra

  → Use

  doc2qra

  instead

• doc-to-qra

  → Use

  doc2qra

  instead

All three legacy skills now redirect to

doc2qra