gaia-formalize-fine

Formalize scientific sources into Gaia knowledge packages with probabilistic belief updates.

32|9|Updated Mar 2, 2026
One-click install
npx skills add https://github.com/SiliconEinstein/Gaia --skill gaia-formalize-fine
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gaia-formalize-fine
Source: https://github.com/SiliconEinstein/Gaia/tree/main/gaia/_skills/gaia-formalize-fine
Command: npx skills add https://github.com/SiliconEinstein/Gaia --skill gaia-formalize-fine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Gaia-formalize-fine helps you convert a scientific source into an executable Gaia knowledge package with complete, reviewable reasoning structure and probabilistic belief updates, so your argument becomes explicit and checkable rather than narrative and implicit.

Core Features & Use Cases

  • Six-pass, audit-grade formalization pipeline: extract knowledge nodes, connect reasoning relations, check completeness, refine strategy types, verify structural integrity, and polish for standalone readability.
  • Compile+check loop after every pass: ensures the intermediate graph remains valid and progressively improves, not a single end-to-end “surprise.”
  • Produces publishable deliverables: emits package DSL under src/<pkg>/, leaf priors in priors.py, and a critical-analysis ANALYSIS.md for handoff to rendering.
  • Bayesian tail for priors and inference: runs hole-based prior assignment, then executes inference to generate beliefs for interpretation and subsequent iteration.

Quick Start

Use gaia-formalize-fine to formalize a multi-section scientific paper by running a six-pass extraction-to-polish workflow that compiles and checks the Gaia package after each pass and ends with priors assignment and belief inference.

Frequently Asked Questions about gaia-formalize-fine

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

FAQPage Schema
How do I convert a scientific paper into an executable knowledge package with probabilistic reasoning?

To convert a scientific paper into an executable knowledge package, you formalize the source using a six-pass extraction-to-polish workflow that compiles and checks the Gaia DSL after each pass, followed by hole-guided priors assignment and Bayesian inference execution.

What is Bayesian inference tail assignment in knowledge formalization?

Bayesian inference tail assignment in knowledge formalization is the process of executing hole-based prior assignments to generate probabilistic beliefs, outputting a priors.py file and an ANALYSIS.md document for interpretation and iteration.

How do I extract and connect reasoning relations from a technical report for completeness auditing?

Extract and connect reasoning relations from a technical report by running a multi-pass formalization pipeline that identifies knowledge nodes, maps reasoning relations, and performs structural integrity checks to ensure complete audit-grade coverage.

Can I use this formalization workflow for textbook chapters and multi-section papers?

Yes, the formalization workflow is explicitly applicable to full scientific papers, textbook chapters, and technical reports where thorough extraction, connection, completeness auditing, and standalone polishing are required.

What is the best way to ensure structural integrity when converting scientific arguments into an executable format?

The best way to ensure structural integrity when converting scientific arguments is to run a compile and check loop after every formalization pass, progressively validating the intermediate graph before generating final package deliverables.

What deliverables do I get from audit-grade scientific knowledge formalization?

Audit-grade scientific knowledge formalization produces publishable deliverables including the package DSL under src/<pkg>/, leaf priors in priors.py, and a critical-analysis ANALYSIS.md file for downstream rendering handoff.