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

| Flag | Description | |------|-------------| | --file | PDF, markdown, or text file | | --url | URL to fetch and convert | | --scope | Memory scope (default: research) | | --context | Domain focus, e.g. "security expert" | | --dry-run | Preview without storing | | --json | JSON output (includes summary) | | --sections-only | Extract sections only (no Q&A) | | --summary-only | Generate only the summary |

Output Format

When using --json, output includes:

{
  "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)

| Variable | Default | Description | |----------|---------|-------------| | DOC2QRA_PDF_MODE | fast | PDF mode: fast, accurate, auto | | DOC2QRA_CONCURRENCY | 6 | Parallel LLM requests | | DOC2QRA_GROUNDING_THRESH | 0.6 | Grounding similarity threshold | | DOC2QRA_NO_GROUNDING | - | Set to 1 to skip validation |

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 with deprecation warnings.