name: Knowledge Connector description: Turn scattered notes and documents into an actionable knowledge graph. Use when the user wants an import wizard, cross-document answers, relationship maps, and concrete next-step suggestions instead of a passive graph dump.

Knowledge Connector

Knowledge Connector should feel like a product line, not another graph utility.

Its job is not just to extract concepts. Its job is to help the user: - import notes and documents with low friction - search across multiple documents from one query - visualize concept relationships in a way that is easy to inspect - get actionable graph results such as what to connect, review, or expand next

What This Skill Optimizes For

Default toward five high-value outcomes: - fast document import - guided import onboarding - cross-document knowledge retrieval - relationship-aware graph views - actionable next steps

Avoid drifting into “yet another adjacent knowledge skill”.

Primary Workflows

1. Import Experience

Use kc import-docs when the user wants to build a graph from multiple files or a notes directory. Use kc import-wizard when the user wants a preview-first onboarding flow.

Good import behavior means: - accept files or a directory - preserve source titles and paths - show how many documents, concepts, and relations were created - keep the user oriented after import

2. Cross-Document Search

Use kc search or kc query when the user asks: - where an idea appears across notes - which documents mention a concept - what concepts connect several documents

Results should show: - matching concepts - matching source documents - useful next actions

3. Relationship Visualization

Use kc visualize for full graph export and kc map for a concept-centered actionable subgraph.

Visualization should help the user answer: - what is central - what is weakly connected - what deserves review

4. Actionable Results

Do not stop at “here is the graph”.

The output should usually recommend one or more actions such as: - import more source material - auto-connect newly imported concepts - inspect a concept-centered subgraph - verify weak relationships from source documents - export a graph view for sharing or review

Core Commands

Import

kc import-wizard --dir notes/
kc import-docs --dir notes/
kc import-docs --files a.md b.md c.txt

Search

kc search "machine learning"
kc answer "哪些文件把強化學習和規劃連在一起？"
kc query "transformer" --sources
kc query --ask "哪些文件同時提到了強化學習和規劃？"

Map And Visualize

kc map --concept "人工智慧" --depth 2
kc visualize --format html --output graph.html
kc visualize --concept "機器學習" --depth 2 --output ml-graph.html

Manage

kc stats
kc export --output backup.json
kc import --file backup.json

Output Standard

When the skill returns results, prefer this structure:

What Matched

Show concepts and source coverage.

Why It Matters

Explain the meaningful relationship or pattern.

Next Step

Tell the user what to do next with the graph.

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Product Positioning

Knowledge Connector is strongest when the user has: - a growing notes corpus - repeated concepts spread across files - a need to move from storage to understanding

It is weaker if it only acts like a raw extractor with no import flow, no source-aware search, and no next-step guidance.

這是一款實用的知識管理工具，能幫你把散落的筆記文件快速匯入、提取概念、畫出關係圖，還能搜尋跨文件的知識關聯，操作體驗流暢清晰。但它的智慧化程度有限，概念提取主要靠關鍵詞匹配，可能遺漏重要概念或產生誤提取，自動關聯的準確性也有待提升。如果你有大量文件需要整理成知識網路，這個工具能提供一定幫助，但期望值不宜過高。