Doc2qra

doc2qra

Convert any document (PDF, URL, text) into Question-Reasoning-Answer pairs with a document summary. Stores to memory for later recall.

majiayu000/claude-skill-registry·0installs663starsSecurity

SKILL.md

Full skill instructions

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

Extract content from PDF/URL/text
Summarize the document (2-3 paragraph overview)
Split into logical sections
Generate Q&A pairs via LLM (parallel batch)
Validate answers are grounded in source
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.

Install skill

Copy and run in your terminal

npx -y skills add https://github.com/majiayu000/claude-skill-registry --skill doc2qra

View on GitHub

skills/data/doc2qra/SKILL.md

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