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qmdQMD Markdown 搜索

Agent Skill

qmd 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

288

周安装

12

GitHub Stars

5

下载量

96
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:qmd(QMD Markdown 搜索)
来源仓库:https://github.com/ckorhonen/claude-skills
仓库路径:skills/qmd
安装命令:
npx skills add https://github.com/ckorhonen/claude-skills --skill qmd
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/ckorhonen/claude-skills --skill qmd

简介

用于本地 Markdown 文件的快速检索,建立知识库索引后即时搜索。

  • 采用 BM25 算法为主,支持关键词与语义相似度双重查询模式。
  • 通过命令行安装,建议核验权限范围与维护状态,注意可能触发的文件读取操作。
  • 适用于个人笔记管理,推荐优先使用 qmd search 命令提升效率。
  • qmd 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

qmd - Quick Markdown Search

Local search engine for Markdown notes, docs, and knowledge bases. Index once, search fast.

When to use (trigger phrases)

  • "search my notes / docs / knowledge base"
  • "find related notes"
  • "retrieve a markdown document from my collection"
  • "search local markdown files"

Default behavior (important)

  • Prefer qmd search (BM25). It's typically instant and should be the default.
  • Use qmd vsearch only when keyword search fails and you need semantic similarity (can be very slow on a cold start).
  • Avoid qmd query unless the user explicitly wants the highest quality hybrid results and can tolerate long runtimes/timeouts.

Search modes

ModeCommandSpeedUse case
BM25 (default)qmd searchInstantKeyword matching
Vectorqmd vsearch~1 min coldSemantic similarity
Hybridqmd querySlowestLLM reranking (skip unless requested)

Common commands

qmd search "query"              # default - fast keyword search
qmd search "query" -c notes     # search specific collection
qmd search "query" -n 10        # more results
qmd search "query" --json       # JSON output
qmd search "query" --all --files --min-score 0.3

Useful options

  • -n <num>: number of results
  • -c, --collection <name>: restrict to a collection
  • --all --min-score <num>: return all matches above a threshold
  • --json / --files: agent-friendly output formats
  • --full: return full document content

Retrieve documents

qmd get "path/to/file.md"       # Full document
qmd get "#docid"                # By ID from search results
qmd multi-get "journals/2025-05*.md"
qmd multi-get "doc1.md, doc2.md, #abc123" --json

Maintenance

qmd status                      # Index health
qmd update                      # Re-index changed files
qmd embed                       # Update embeddings

Setup (if not installed)

# Install
bun install -g https://github.com/tobi/qmd

# Create collection
qmd collection add /path/to/notes --name notes --mask "**/*.md"
qmd context add qmd://notes "Description of this collection"  # optional
qmd embed  # one-time to enable vector + hybrid search

Performance notes

  • qmd search is typically instant
  • qmd vsearch can be ~1 minute on cold start (loads local LLM for query expansion)
  • qmd query adds LLM reranking on top of vsearch, even slower

Common Pitfalls

Search query too vague (irrelevant results)

Problem: Broad queries like "notes" or "project" return hundreds of low-relevance matches.

Solution:

  • Use specific keywords: qmd search "Python async context managers" not qmd search "Python"
  • Combine keywords: qmd search "deadline March quarterly review"
  • Use quotes for exact phrases: qmd search "exact phrase match"
  • Check result scores: qmd search "query" --all --min-score 0.5 to filter low-confidence matches

Index not updated (missing recent files)

Problem: Newly created or recently modified files don't appear in search results.

Solution:

# Check index status
qmd status

# Re-index all changed files
qmd update

# Full re-index if update doesn't help
qmd collection remove <name>
qmd collection add /path/to/notes --name <name> --mask "**/*.md"
qmd embed  # if vector search enabled

Path filters wrong (excluding relevant directories)

Problem: Search doesn't find files because the collection mask is too narrow or collection path is wrong.

Solution:

# List current collections
qmd status

# Verify collection path matches your notes location
# If path changed, re-add collection:
qmd collection remove <old-name>
qmd collection add /path/to/notes --name <name> --mask "**/*.md"

# Common issue: subdirectories excluded by mask
# If notes are in journals/2025/*, use: --mask "**/*.md"
# Not: --mask "*.md" (which only matches top level)

Search timeout on large vaults (performance issues)

Problem: qmd query or qmd vsearch hangs or times out on large knowledge bases (1000+ files).

Solution:

  • Avoid qmd query on large vaults (LLM reranking is too slow)
  • Use qmd search instead (BM25 is fast even on huge collections)
  • If semantic search needed, use qmd vsearch with -n 5 to limit results
  • Restrict search scope with -c <collection> to search one collection instead of all: qmd search "query" -c notes -n 5 # faster than searching all collections
  • If vault exceeds 5000 files, consider splitting into multiple collections

Missing configuration (qmd not initialized)

Problem: qmd status shows "No collections" or qmd search returns no results.

Solution:

# Install qmd if missing
bun install -g https://github.com/tobi/qmd

# Set up a collection
qmd collection add /path/to/notes --name notes --mask "**/*.md"

# Enable vector search (optional but recommended)
qmd embed

# Verify setup
qmd status  # Should show your collection with file count

Semantic search returns irrelevant results (vsearch/query)

Problem: Vector search matches by topic but misses what you actually wanted.

Solution:

  • Fall back to qmd search (BM25 keyword matching is more precise for specific terms)
  • Rephrase query to include actual keywords: not "What are my goals?" but "goals quarterly review objectives"
  • Use qmd query if available (adds LLM reranking), but only if you can tolerate slow runtime
  • Use -n 3 to get fewer, higher-confidence matches from vector search

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

38.07%
按下载量换算37

Claude

26.53%
按下载量换算25

Cursor

17.32%
按下载量换算17

Gemini CLI

8.82%
按下载量换算8

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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