Qra

Documents

qra

Qra is an Agent Skill for documents published in the majiayu000/claude-skill-registry repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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

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QUICK START

How to use this skill

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Prompt to paste

Inspect the Agent Skill at https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/analysis/qra/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/qra, but wait for my approval before writing files or running scripts.

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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

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 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