qra — for Claude Code fetcher, community, for Claude Code, ide skills, context, dry-run, memory-agent learn, distill, QRA_CONCURRENCY, QRA_GROUNDING_THRESH

v1.0.0
GitHub

이 스킬 정보

적합한 상황: Ideal for AI agents that need extract question-reasoning-answer pairs from text and store in memory. 현지화된 요약: # QRA Skill Extract Question-Reasoning-Answer pairs from text and store in memory. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

기능

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

# 핵심 주제

for Claude Code context dry-run memory-agent learn distill QRA_CONCURRENCY
grahama1970 grahama1970
[1]
[0]
업데이트: 2/15/2026

Skill Overview

Start with fit, limitations, and setup before diving into the repository.

적합한 상황: Ideal for AI agents that need extract question-reasoning-answer pairs from text and store in memory. 현지화된 요약: # QRA Skill Extract Question-Reasoning-Answer pairs from text and store in memory. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

이 스킬을 사용하는 이유

추천 설명: qra helps agents extract question-reasoning-answer pairs from text and store in memory. QRA Skill Extract Question-Reasoning-Answer pairs from text and store in memory. This AI agent skill supports Claude Code

최적의 용도

적합한 상황: Ideal for AI agents that need extract question-reasoning-answer pairs from text and store in memory.

↓ 실행 가능한 사용 사례 for qra

사용 사례: Applying Extract Question-Reasoning-Answer pairs from text and store in memory
사용 사례: Applying Extract from text file
사용 사례: Applying ./run.sh --file document.md --scope research

! 보안 및 제한 사항

  • 제한 사항: Requires repository-specific context from the skill documentation
  • 제한 사항: Works best when the underlying tools and dependencies are already configured

About The Source

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Labs 데모

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ 및 설치 단계

These questions and steps mirror the structured data on this page for better search understanding.

? 자주 묻는 질문

qra은 무엇인가요?

적합한 상황: Ideal for AI agents that need extract question-reasoning-answer pairs from text and store in memory. 현지화된 요약: # QRA Skill Extract Question-Reasoning-Answer pairs from text and store in memory. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

qra은 어떻게 설치하나요?

다음 명령을 실행하세요: npx killer-skills add grahama1970/fetcher/qra. Cursor, Windsurf, VS Code, Claude Code와 19개 이상의 다른 IDE에서 동작합니다.

qra은 어디에 쓰이나요?

주요 활용 사례는 다음과 같습니다: 사용 사례: Applying Extract Question-Reasoning-Answer pairs from text and store in memory, 사용 사례: Applying Extract from text file, 사용 사례: Applying ./run.sh --file document.md --scope research.

qra 와 호환되는 IDE는 무엇인가요?

이 스킬은 Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer 와 호환됩니다. 통합 설치에는 Killer-Skills CLI를 사용하세요.

qra에 제한 사항이 있나요?

제한 사항: Requires repository-specific context from the skill documentation. 제한 사항: Works best when the underlying tools and dependencies are already configured.

↓ 이 스킬 설치 방법

  1. 1. 터미널 열기

    프로젝트 디렉터리에서 터미널 또는 명령줄을 여세요.

  2. 2. 설치 명령 실행

    npx killer-skills add grahama1970/fetcher/qra 를 실행하세요. CLI가 IDE 또는 에이전트를 자동으로 감지하고 스킬을 설정합니다.

  3. 3. 스킬 사용 시작

    스킬이 이제 활성화되었습니다. 현재 프로젝트에서 qra을 바로 사용할 수 있습니다.

! Source Notes

This page is still useful for installation and source reference. Before using it, compare the fit, limitations, and upstream repository notes above.

Upstream Repository Material

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Upstream Source

qra

# QRA Skill Extract Question-Reasoning-Answer pairs from text and store in memory. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

SKILL.md
Readonly
Upstream Repository Material
The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.
Upstream Source

QRA Skill

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

Happy Path

bash
1# Extract from text file
2./run.sh --file document.md --scope research
3
4# With domain focus (recommended)
5./run.sh --file notes.txt --scope project --context "security expert"
6
7# Preview before storing
8./run.sh --file transcript.txt --dry-run
9
10# From stdin
11cat meeting_notes.txt | ./run.sh --scope meetings

Parameters

FlagDescription
--fileText or markdown file
--textRaw text content
--scopeMemory scope (default: research)
--contextDomain focus, e.g. "ML researcher"
--dry-runPreview without storing
--jsonJSON 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

bash
1# Meeting transcript
2./run.sh --file meeting.txt --scope team --context "project manager"
3
4# Code documentation
5./run.sh --file README.md --scope code --context "Python developer"
6
7# From clipboard/pipe
8pbpaste | ./run.sh --scope notes --dry-run

Environment Variables (Optional Tuning)

VariableDefaultDescription
QRA_CONCURRENCY6Parallel LLM requests
QRA_GROUNDING_THRESH0.6Grounding similarity threshold
QRA_NO_GROUNDING-Set to 1 to skip validation

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