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kinnerkarmanish
kinnerkarmanishCOMMUNITY
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learn

Search the web, fetch content, and normalize it with provenance metadata.

Updated Apr 26, 2026
Search Tags:#summarization#provenance#pipeline#web-search#learn#seed#knowledge-ingestion
One-click install
npx skills add https://github.com/kinnerkarmanish/mak --skill learn-kinnerkarmanish
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: learn
Source: https://github.com/kinnerkarmanish/mak/tree/main/library/skills/learning/learn
Command: npx skills add https://github.com/kinnerkarmanish/mak --skill learn-kinnerkarmanish

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Research and ingestion of external knowledge via web search with provenance tracking, enabling auditable knowledge growth.

Core Features & Use Cases

  • Web search and content fetch to surface relevant material for a topic.
  • Provenance metadata (source URL, author, date, fetch date) attached to each item and handed to downstream pipelines.
  • Seed into /seed with optional chaining into /pipeline for end-to-end knowledge processing.

Quick Start

Run /learn "enterprise AI pricing models 2026" to search the web, fetch relevant content, and seed it into the knowledge pipeline.

Frequently Asked Questions about learn

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

FAQPage Schema
How do I ingest external web research with provenance metadata?▼

You acquire external knowledge by web searching and fetching relevant material, then normalizing content while attaching provenance metadata like source URL, author, date, and fetch date to each item.

What is the best way to automate competitive analysis knowledge ingestion from the web?▼

The best way to automate competitive analysis knowledge ingestion is running a seed-to-pipeline flow that searches the web for your topic, fetches content, and hands it to downstream pipelines for end-to-end processing.

Does web research and content normalization require an HTML-to-markdown converter?▼

Yes, acquiring and normalizing fetched web content requires an HTML-to-markdown converter to transform raw pages into normalized text for downstream knowledge ingestion and pipeline processing.

Can I chain web search results directly into a knowledge pipeline?▼

Yes, you can chain web search results into a knowledge pipeline by seeding fetched and normalized content into the seed stage, which optionally connects to the pipeline for end-to-end processing.

What provenance tracking data is attached to fetched web content during knowledge expansion?▼

Provenance tracking during knowledge expansion attaches source URL, author, publication date, and fetch date metadata to each fetched web item before handing it to downstream pipelines.

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