Token导航 LogoToken导航TokenDH.com
研究检索external-servicegithub未标认证来源可访问许可证需确认审计通过

wiki-query维基查询

Agent Skill

wiki-query 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

21,192

周安装

883

GitHub Stars

801

下载量

7,064
CodexClaudeCursorGemini CLI

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:wiki-query(维基查询)
来源仓库:https://github.com/ar9av/obsidian-wiki
仓库路径:skills/wiki-query
安装命令:
npx skills add https://github.com/ar9av/obsidian-wiki --skill wiki-query
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/ar9av/obsidian-wiki --skill wiki-query

简介

wiki-query 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。

  • 适用于 Obsidian 维基知识检索和问题解答,支持预合成知识和交叉引用查询。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 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操作。
  • 当前无详细 SKILL.md 内容,需参考仓库中的 Wiki Query 了解知识检索机制和上下文获取方式。

SKILL.md

Wiki Query — Knowledge Retrieval

You are answering questions against a compiled Obsidian wiki, not raw source documents. The wiki contains pre-synthesized, cross-referenced knowledge.

Before You Start

  1. Read ~/.obsidian-wiki/config to get OBSIDIAN_VAULT_PATH (works from any project). Fall back to .env if you're inside the obsidian-wiki repo.
  2. If $OBSIDIAN_VAULT_PATH/hot.md exists, read it first — it gives you instant context on recent activity. If the user's question is about something ingested recently, hot.md may answer it before you even open index.md.
  3. Read $OBSIDIAN_VAULT_PATH/index.md to understand the wiki's scope and structure

Visibility Filter (optional)

By default, all pages are returned regardless of visibility tags. This preserves existing behavior — nothing changes unless the user asks for it.

If the user's query includes phrases like "public only", "user-facing", "no internal content", "as a user would see it", or "exclude internal", activate filtered mode:

  • Build a blocked tag set: {visibility/internal, visibility/pii}
  • In the Index Pass (Step 2), skip any candidate whose frontmatter tags contain a blocked tag
  • In Section/Full Read passes (Steps 3–4), do not read or cite any blocked page
  • Synthesize the answer only from allowed pages — do not mention that excluded pages exist

Pages with no visibility/ tag, or tagged visibility/public, are always included.

In filtered mode, note the filter in the Step 6 log entry: mode=filtered.

Retrieval Protocol

Follow the Retrieval Primitives table in llm-wiki/SKILL.md. Reading is the dominant cost of this skill — use the cheapest primitive that answers the question and escalate only when it can't. Never jump straight to full-page reads.

Step 1: Understand the Question

Classify the query type:

  • Factual lookup — "What is X?" → Find the relevant page(s)
  • Relationship query — "How does X relate to Y?" → Find both pages and their cross-references
  • Synthesis query — "What's the current thinking on X?" → Find all pages that touch X, synthesize
  • Gap query — "What don't I know about X?" → Find what's missing, check open questions sections

Also decide the mode:

  • Index-only mode — triggered by "quick answer", "just scan", "don't read the pages", "fast lookup". Stops at Step 3. Answers from frontmatter + index.md only.
  • Normal mode — the full tiered pipeline below.

Step 2: Index Pass (cheap)

Build a candidate set *without opening any page bodies*:

  • You've already read index.md above — use it as the first filter. It lists every page with a one-line description and tags.
  • Use Grep to scan page frontmatter only for title, tag, alias, and summary matches. A pattern like ^(title|tags|aliases|summary): scoped to vault .md files is far cheaper than content grep.
  • Collect the top 5–10 candidate page paths ranked by:

1. Exact title or alias match 2. Tag match 3. Summary field contains the query term 4. index.md entry contains the query term

If you're in index-only mode, stop here. Answer from summary: fields, titles, and index.md descriptions only. Label the answer clearly: "(index-only answer — page bodies not read; facts below are from page summaries and may miss nuance)". Then skip to Step 5.

Step 2b: QMD Semantic Pass (optional — requires QMD_WIKI_COLLECTION in .env)

GUARD: If $QMD_WIKI_COLLECTION is empty or unset, skip this entire step and proceed to Step 3.

No QMD? Skip to Step 3 and use Grep directly on the vault. QMD is faster and concept-aware but the grep path is fully functional. See .env.example for setup.

If QMD_WIKI_COLLECTION is set and the index pass didn't produce clear candidates — or the question requires semantic matching rather than exact terms — use QMD before reaching for Grep:

mcp__qmd__query:
  collection: <QMD_WIKI_COLLECTION>   # e.g. "knowledge-base-wiki"
  intent: <the user's question>
  searches:
    - type: lex    # keyword match — good for exact names, file paths, error messages
      query: <key terms>
    - type: vec    # semantic match — good for concepts, patterns, "what is X like"
      query: <question rephrased as a description>

The returned snippets act as pre-read section summaries. If they answer the question fully, skip Step 3 and go straight to Step 4 (reading only the pages QMD ranked highest). If not, use the ranked file list to guide which files to grep or read in Step 3.

Also search papers when the question may have source material in _raw/:

If QMD_PAPERS_COLLECTION is set and the user is asking about a topic likely covered by ingested papers (research, theory, background), run a parallel search against the papers collection. Cite raw sources separately from compiled wiki pages in your answer.

Step 3: Section Pass (medium cost — only if Steps 2/2b are inconclusive)

For each of the top candidates, pull the relevant section *without reading the whole page*:

  • Use Grep -A 10 -B 2 "<query-term>" <candidate-file> to get just the lines around the match.
  • This usually returns 15–30 lines per hit instead of 100–500.
  • If the section grep gives a clear answer, go straight to Step 5.

Step 4: Full Read (expensive — last resort)

Only when Steps 2 and 3 don't answer the question:

  • Read the top 3 candidates in full.
  • Follow at most one hop of [[wikilinks]] from those pages if the answer requires cross-references.
  • Check "Open Questions" sections for known gaps.
  • If you're still short, then fall back to a broad content grep across the vault. Tell the user you escalated — this is the expensive path and they should know.

Step 5: Synthesize an Answer

Compose your answer from wiki content:

  • Cite specific wiki pages using [[page-name]] notation
  • Note which step the answer came from ("found in summary" vs "grepped section" vs "full page read") — helps the user understand confidence
  • If the wiki has contradictions, present both sides
  • If the wiki doesn't cover something, say so explicitly
  • Suggest which sources might fill the gap

Step 6: Log the Query

Append to log.md:

- [TIMESTAMP] QUERY query="the user's question" result_pages=N mode=normal|index_only|filtered escalated=true|false

Answer Format

Structure answers like this:

Based on the wiki: [Your synthesized answer with [[wikilinks]] to source pages] Pages consulted: [[page-a]], [[page-b]], [[page-c]] Gaps: [What the wiki doesn't cover that might be relevant]

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

35.16%
按下载量换算2,484

Claude

34.43%
按下载量换算2,432

Cursor

18.07%
按下载量换算1,276

Gemini CLI

9.69%
按下载量换算685

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。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

继续浏览同类 Skills