How do propagators differ from standard dataflow?

While standard dataflow is typically unidirectional, propagators are bidirectional; they can infer inputs from outputs just as easily as outputs from inputs by treating relationships as constraints.

What is a propagator network?

A propagator network is a collection of autonomous machines that monitor cells for new information, compute derived values, and update other cells until a system-wide fixpoint is reached.

What happens when the system encounters a contradiction?

The skill utilizes dependency-directed backtracking to trace the provenance of conflicting information, allowing the system to identify and resolve the specific assumptions causing the contradiction.

Is this skill suitable for physical modeling?

Yes, it is ideal for physical modeling, unit conversion, and engineering constraints where multiple variables (like Ohm's Law or Newtonian mechanics) are interdependent.

Constraint Propagator Networks

Name: Constraint Propagator Networks
Author: plurigrid

byplurigrid

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データサイエンスとML

Implements bidirectional constraint propagation and fixpoint derivation for complex autonomous information networks.

The Propagators skill enables Claude to utilize the Sussman/Radul model of propagator networks, a sophisticated alternative to traditional dataflow. It allows for monotonic, concurrent, and bidirectional information flow, where cells in a network gain information until a stable fixpoint is reached. This is particularly useful for solving interdependent constraints in CAD modeling, physical simulations, and advanced type inference where information must flow from both inputs to outputs and vice versa, while providing robust dependency-directed backtracking to resolve logical contradictions.

主な機能

01Dependency-directed backtracking for contradiction resolution

022 GitHub stars

03Fixpoint iteration for complex system stabilization

04Bidirectional constraint satisfaction and information flow

05Monotonic information merging using join-semilattices

06GF(3) triadic conservation and consistency verification

ユースケース

01Epistemic arbitrage for synthesizing information from conflicting data sources

02Multi-variable constraint modeling for engineering and physics simulations

03Bidirectional type inference and symbolic logic processing in language design

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi propagators

For use in Claude.ai and ChatGPT

Download Skill