Knowledge Connector

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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.

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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.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.