learn

Name: learn
Availability: InStock
Author: shakedfb

Research topics and synthesize provenance-backed findings into a knowledge graph.

Updated Feb 26, 2026

Search Tags:#automation #research #provenance #web-search #knowledge-graph #learn

Ready to run

npx skills add https://github.com/shakedfb/deterministic-agentic-sdlc-workflow --skill learn-shakedfb

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: learn
Source: https://github.com/shakedfb/deterministic-agentic-sdlc-workflow/tree/main/.claude/skills/learn
Command: npx skills add https://github.com/shakedfb/deterministic-agentic-sdlc-workflow --skill learn-shakedfb

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Researching a topic and building a linked knowledge graph can be time-consuming and error-prone. This skill automates topic exploration, captures full provenance, and chains results into a processing pipeline to keep your knowledge graph fresh and trustworthy.

Core Features & Use Cases

Automated topic discovery and synthesis using Exa deep researcher, web search, or basic search.
Provenance-rich results with links to sources and processing steps, enabling traceability and auditability.
Use Case: Build a topic dossier on a complex domain, then connect new findings to existing graph nodes for integrated decision-making.

Quick Start

Start a topic investigation by issuing /learn <topic> to begin building a provenance-backed knowledge graph.

Frequently Asked Questions about learn

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

FAQPage Schema

How do I automate topic research and build a knowledge graph with provenance?▼

Automating topic research involves synthesizing findings using web search and exporting results with provenance metadata to drive knowledge-graph updates. This approach captures full source traceability and chains results into a processing pipeline to keep your graph fresh.

What is provenance metadata and why is it needed for knowledge graphs?▼

Provenance metadata provides links to original sources and processing steps, enabling traceability and auditability. It is needed to ensure your knowledge graph remains trustworthy and to connect new findings to existing nodes for integrated decision-making.

How do I start an exploratory research investigation using web search?▼

To start an exploratory research investigation, issue a command with your target topic to begin building a provenance-backed knowledge graph. The system uses deep researcher and basic search tools to discover and synthesize domain knowledge.

Can I use deep research tools to synthesize complex domain knowledge?▼

Yes, deep research tools can be used to synthesize complex domain knowledge. The system supports deep researcher, web search, and basic search tools to automate topic discovery and capture provenance-rich results for your knowledge graph.

Does the knowledge graph research pipeline work without external dependencies?▼

Yes, the automated research pipeline operates without external dependencies. It uses built-in search and deep researcher tools to explore topics and export provenance metadata directly into your knowledge graph for chained processing.

What is the best way to maintain a trustworthy knowledge graph during topic exploration?▼

The best way to maintain a trustworthy knowledge graph is to automate topic exploration with full provenance capture. Chaining research results into a processing pipeline with source links ensures your findings remain traceable and auditable.