How does this improve agent safety?

By enforcing a 'No action without sufficient witness' policy, it stops the agent from attempting tasks it isn't competent to handle, reducing errors and hallucinations.

What is an ε-machine in this context?

An ε-machine is a minimal sufficient model used to map specific task requirements to the exact skills needed to complete them successfully without redundancy.

What does Dynamic Sufficiency actually do?

It acts as a validator that prevents Claude from performing an action unless it has verified that the necessary skills and tools are currently loaded and ready.

What is the significance of the -1 Trit?

In this triadic framework, -1 represents the Validator or Gatekeeper role, which balances the Generator (+1) and the Coordinator (0) to maintain system stability.

Can it automatically load missing skills?

Yes, if the pre-action gate detects missing sufficiency, it can trigger a LOAD_MORE verdict to fetch the required skills before proceeding.

Dynamic Sufficiency Gatekeeper

Name: Dynamic Sufficiency Gatekeeper
Author: plurigrid

byplurigrid

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Herramientas para Desarrolladores

Implements computational mechanics to prevent agent actions unless minimal sufficient skill coverage is verified.

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The Dynamic Sufficiency skill acts as a sophisticated safety gate for AI agents, utilizing the principles of Computational Mechanics and ε-machines from the Santa Fe Institute. By treating skills as world-generating memories, it ensures that an agent never undertakes an action without a 'sufficient witness'—the minimal set of capabilities required for success. It monitors causal states to partition tasks into equivalence classes, effectively preventing hallucinations or failed executions caused by missing tools or logic. This skill provides a rigorous framework for agent reliability, enforcing a variational bound that balances safe execution with maximum parallel efficiency.

Características Principales

Variational Bound enforcement for action safety
Minimal Sufficient Skill Profile verification
Predictive Information metrics for success likelihood
Causal State Gating via ε-machine models
GF(3) Trit-based skill conservation logic
2 GitHub stars

Casos de Uso

Creating self-improvising world models that learn from successful and failed action-skill pairings
Preventing autonomous actions in high-risk environments when required validation tools are missing
Optimizing skill loading by identifying the exact minimal toolset for complex task classes

Acerca de

Características Principales

Variational Bound enforcement for action safety
Minimal Sufficient Skill Profile verification
Predictive Information metrics for success likelihood
Causal State Gating via ε-machine models
GF(3) Trit-based skill conservation logic
2 GitHub stars

Casos de Uso

Creating self-improvising world models that learn from successful and failed action-skill pairings
Preventing autonomous actions in high-risk environments when required validation tools are missing
Optimizing skill loading by identifying the exact minimal toolset for complex task classes