/init

Build an EmpiricalWiki workspace from PDFs and notes with parallel ingestion.

77|16|Updated May 9, 2026
One-click install
npx skills add https://github.com/Lambenthan/empiricalwiki --skill init-lambenthan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: /init
Source: https://github.com/Lambenthan/empiricalwiki/tree/main/.claude/skills/init
Command: npx skills add https://github.com/Lambenthan/empiricalwiki --skill init-lambenthan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

/init turns your local research inputs (PDFs, notes, and optional web material) into a structured EmpiricalWiki workspace and an initial, curated paper set that can be ingested in parallel.

Core Features & Use Cases

  • Deterministic prepare + guided discovery: Normalizes local PDFs into prepared, canonical ingest sources and then uses planner-guided discovery to select a constrained set of candidate papers (including optional external discovery).
  • Scaffold-first wiki construction: Creates the wiki skeleton and provisional pages (Summary, topics, ideas, concepts, claims) before any heavy ingestion work, preserving provenance via an exact provisional notice.
  • Parallel ingest via isolated worktrees: Uses worktree-based fan-out/fan-in to ingest each selected paper safely and deterministically, then runs deduplication, rebuilding, and linting to produce a coherent final graph and index.

Quick Start

Run the init orchestration to build the wiki from your raw inputs and ingest the resulting paper set with discovery: init [topic].

Frequently Asked Questions about /init

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

FAQPage Schema
How do I build an empirical wiki from raw research PDFs?▼

Building an empirical wiki from raw research PDFs involves orchestrating deterministic PDF preparation to normalize local files into canonical ingest sources, then generating a structured workspace scaffold with provisional pages before parallel ingestion.

What is planner-guided discovery for research workflows?▼

Planner-guided discovery for research workflows is a methodology that selects a bounded candidate paper set, including optional external discovery, to ensure only relevant sources enter the empirical wiki ingestion pipeline.

Can I ingest multiple papers in parallel using isolated worktrees?▼

Yes, you can ingest multiple papers in parallel using worktree-based fan-out and fan-in. This worktree-isolated parallel ingestion safely processes each selected paper deterministically before executing post-merge deduplication and rebuilding.

How do I prepare PDFs for parallel ingestion into a knowledge graph?▼

Preparing PDFs for parallel ingestion into a knowledge graph requires deterministic normalization into canonical ingest sources. The orchestration then consumes a strict checkpoint order to execute worktree-isolated parallel ingestion safely.

What does an empirical wiki workspace break down for each paper?▼

An empirical wiki workspace breaks down variables, mechanisms, identification, robustness, heterogeneity, tables, and paper evidence for each ingested source, ultimately producing a coherent final knowledge graph and index.

What are the limitations of automated paper ingestion for empirical research?▼

Limitations of automated paper ingestion include the strict requirement to consume a final checkpoint order for ingest. You must also execute post-merge deduplication, rebuilding, and linting steps to maintain a coherent knowledge graph.