- 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.txtSearch
kc search "machine learning"
kc answer "哪些文档把强化学习和规划连在一起？"
kc query "transformer" --sources
kc query --ask "哪些文档同时提到了强化学习和规划？"Map And Visualize
kc map --concept "人工智能" --depth 2
kc visualize --format html --output graph.html
kc visualize --concept "机器学习" --depth 2 --output ml-graph.htmlManage
kc stats
kc export --output backup.json
kc import --file backup.jsonOutput 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.