research

Name: research
Availability: InStock
Author: mikkelkrogsholm

Ingest credible web sources into a linked knowledge graph with provenance.

Updated Feb 1, 2026

Search Tags:#research #ingestion #web-search #knowledge-graph #embedding #entity-extraction

Ready to run

npx skills add https://github.com/mikkelkrogsholm/bookstrap --skill research-mikkelkrogsholm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research
Source: https://github.com/mikkelkrogsholm/bookstrap/tree/main/.claude/skills/research
Command: npx skills add https://github.com/mikkelkrogsholm/bookstrap --skill research-mikkelkrogsholm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Source evaluation and ingestion for autonomous research enable reliable, traceable knowledge assembly from diverse materials, reducing manual curation and citation errors.

Core Features & Use Cases

Source reliability evaluation framework with scoring to filter credible material for claims
Semantic chunking, embeddings, and entity extraction (characters, locations, events, concepts) to build a linked knowledge graph
Provenance-enabled relationships and queries to trace evidence and sources across the graph

Quick Start

Instruct Claude Code to begin an autonomous research task: configure a web-search provider, ingest sources, perform chunking and embedding, extract entities, and build a knowledge graph.

Frequently Asked Questions about research

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

FAQPage Schema

How do I build a knowledge graph from ingested web sources?▼

Build a knowledge graph by configuring a web-search provider, ingesting sources, applying semantic chunking, generating embeddings, and extracting entities like characters, locations, events, and concepts into a linked structure with provenance.

How does source reliability scoring work for research ingestion?▼

Source reliability scoring evaluates and filters credible material during research ingestion by applying a configurable workflow that selects sources based on quality thresholds before chunking and entity extraction.

What's the best way to extract entities and relate them in a knowledge graph?▼

Entity extraction identifies characters, locations, events, and concepts from semantically chunked content, then relates them into a graph with provenance, enabling structured queries and evidence tracing across sources.

Does research ingestion require a vector-capable database for embeddings?▼

Yes, the workflow stores results in a vector-capable database after generating embeddings, enforcing embedding dimension consistency and validating pipeline quality thresholds for structured queries.

Can I trace provenance and evidence back to original sources in a knowledge graph?▼

Provenance-enabled relationships and queries allow tracing evidence and sources across the knowledge graph, linking extracted entities and claims directly back to their original ingested materials.

What are the limitations of autonomous research ingestion for knowledge assembly?▼

Autonomous research ingestion depends on provider configuration and web-search availability, requiring reliability scoring and validation pipelines to maintain quality thresholds for credible knowledge graph assembly.