qmd
Searches local markdown notes and documents using ir CLI. Use when searching notes, querying documents, managing collections, or retrieving document content.
Best use case
qmd is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Searches local markdown notes and documents using ir CLI. Use when searching notes, querying documents, managing collections, or retrieving document content.
Teams using qmd should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/qmd/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How qmd Compares
| Feature / Agent | qmd | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Searches local markdown notes and documents using ir CLI. Use when searching notes, querying documents, managing collections, or retrieving document content.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# ir - Local Markdown Search 로컬 마크다운 문서를 인덱싱하고 검색하는 CLI 도구. ## Status !`ir status 2>/dev/null || echo "ir not available"` ## CLI ```bash ir search "question" # Hybrid (기본, 최고 품질) ir search --mode bm25 "keywords" # BM25 only (빠름, no LLM) ir search --mode vector "question" # Vector only ir get "#abc123" # By docid ir get notes/meeting.md:50 -l 100 # 특정 라인부터 ``` ## Search Options ```bash --mode <MODE> # bm25 | vector | hybrid [default: hybrid] -n <num> # 결과 개수 (기본 10) --min-score <num> # 점수 임계값 -c, --collection <name> # 특정 컬렉션만 검색 --full # 전체 문서 내용 --files # docid,score,filepath,context 출력 --json # JSON 출력 --md # Markdown 출력 --csv # CSV 출력 --all # 전체 결과 (최대 4096) -v, --verbose # 파이프라인 결정 및 타이밍 ``` ## Collection Management ```bash ir collection list # 목록 ir collection add ~/notes --name notes # 추가 ir collection remove <name> # 삭제 ir update --pull # git pull 후 재인덱싱 ir embed # 벡터 임베딩 생성 ir status # 인덱스 상태 ``` ## Examples ### 노트에서 정보 검색 ``` User: "배포 관련 노트 찾아줘" → ir search "배포 방법 deploy" -n 10 → 결과 요약 제공 → 필요 시 ir get "#docid"로 상세 조회 ``` ### BM25 키워드 검색 ``` User: "vault 설정 찾아줘" → ir search --mode bm25 "vault config" -n 5 ``` ### 특정 컬렉션에서 검색 ``` User: "회의록에서 프로젝트 타임라인 찾아줘" → ir search "프로젝트 일정과 마일스톤" -c meetings -n 10 ``` ## 중요 원칙 1. **검색 모드**: 빠른 키워드 → `--mode bm25`, 최고 품질 → 기본 hybrid 2. **점수 해석**: 0.8+ 매우 관련, 0.5-0.8 적당, 0.5 미만 약함 3. **컬렉션 스코핑**: `-c` 옵션으로 범위를 좁히면 노이즈 감소 4. **파이프라인**: `--json`/`--files` 출력으로 후처리 연결
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