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add-knowledge-connector

Build scalable Cognigy.AI Knowledge Connectors with TypeScript and @cognigy/extension-tools.

30|60|Updated Jul 30, 2020
Search Tags:#hashing#chunking#incremental-updates#knowledge-connectors#cognigy#extension-tools
One-click install
npx skills add https://github.com/Cognigy/Extensions --skill add-knowledge-connector
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: add-knowledge-connector
Source: https://github.com/Cognigy/Extensions/tree/main/.claude/skills/add-knowledge-connector
Command: npx skills add https://github.com/Cognigy/Extensions --skill add-knowledge-connector

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This guide provides a complete pattern for building Knowledge Connectors for Cognigy Extensions, enabling developers to fetch data from external sources, structure it into Knowledge Chunks, and integrate with Knowledge AI workflows.

Core Features & Use Cases

  • Step-by-step file structure templates and implementation patterns for connectors, including connections, knowledge-connectors, chunking, and incremental updates.
  • Guidance on error handling, cleanup, and documentation to ensure production-grade extensions, plus examples for Confluence, SharePoint, REST APIs, and web scraping.
  • Real-world use cases: index internal knowledge bases and external data sources to power AI agents with contextual information.

Quick Start

Create a new skill directory, add a SKILL.md frontmatter with name and description, and implement a minimal knowledge-connector using the provided template.

Frequently Asked Questions about add-knowledge-connector

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a Knowledge Connector for Cognigy.AI to fetch external data?▼

To build a Knowledge Connector for Cognigy.AI, you create a skill directory, add a SKILL.md frontmatter, and implement a TypeScript connector function using the @cognigy/extension-tools API to fetch external data and create Knowledge Chunks.

What is the best way to handle incremental updates for Cognigy Knowledge Connectors?▼

The best way to handle incremental updates for Cognigy Knowledge Connectors is to use the upsertKnowledgeSource function, which allows you to update existing data chunks efficiently without re-fetching the entire external dataset.

How does chunking work when indexing external data sources for AI agents?▼

Chunking works by structuring fetched external data into discrete Knowledge Chunks, enabling AI agents to process and retrieve contextual information effectively from sources like Confluence, SharePoint, or REST APIs.

Can I use the Cognigy extension-tools API to connect SharePoint and Confluence?▼

Yes, you can use the Cognigy extension-tools API to build production-grade Knowledge Connectors that fetch and index data from external sources like SharePoint, Confluence, and REST APIs.

What file structure is required to scaffold a scalable Cognigy Knowledge Connector?▼

Scaffolding a scalable Cognigy Knowledge Connector requires a directory containing a SKILL.md frontmatter with name and description, alongside optional scripts, references, and assets directories for robust organization.

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