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

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• 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.

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。