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qra

Extract Question-Reasoning-Answer pairs from text. Use --context for domain-focused extraction. Validates answers are grounded in source text.

personAuthor: jakexiaohubgithub
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QRA Skill

Extract Question-Reasoning-Answer pairs from text and store in memory.

Happy Path

# Extract from text file
./run.sh --file document.md --scope research

# With domain focus (recommended)
./run.sh --file notes.txt --scope project --context "security expert"

# Preview before storing
./run.sh --file transcript.txt --dry-run

# From stdin
cat meeting_notes.txt | ./run.sh --scope meetings

Parameters

| Flag | Description | |------|-------------| | --file | Text or markdown file | | --text | Raw text content | | --scope | Memory scope (default: research) | | --context | Domain focus, e.g. "ML researcher" | | --dry-run | Preview without storing | | --json | JSON output |

What It Does

  1. Split text into logical sections
  2. Extract Q&A pairs via LLM (parallel batch)
  3. Validate answers are grounded in source
  4. Store to memory via memory-agent learn

When to Use

  • Text content (not PDFs - use distill for PDFs)
  • Meeting transcripts
  • Code documentation
  • Notes and summaries
  • Any plain text you want to remember

Examples

# Meeting transcript
./run.sh --file meeting.txt --scope team --context "project manager"

# Code documentation
./run.sh --file README.md --scope code --context "Python developer"

# From clipboard/pipe
pbpaste | ./run.sh --scope notes --dry-run

Environment Variables (Optional Tuning)

| Variable | Default | Description | |----------|---------|-------------| | QRA_CONCURRENCY | 6 | Parallel LLM requests | | QRA_GROUNDING_THRESH | 0.6 | Grounding similarity threshold | | QRA_NO_GROUNDING | - | Set to 1 to skip validation |