ReddRadar
Agent skill
qra
Extract Question-Reasoning-Answer pairs from text. Use --context for domain-focused extraction. Validates answers are grounded in source text.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/qra
Metadata
Additional technical details for this skill
- short description
- Extract grounded Q&A pairs from text
SKILL.md
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
- Split text into logical sections
- Extract Q&A pairs via LLM (parallel batch)
- Validate answers are grounded in source
- Store to memory via
memory-agent learn
When to Use
- Text content (not PDFs - use
distillfor 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 |
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