What is the role of a 'solver' in this context?

A solver acts as a coordinator (the 0 trit in GF(3) logic) that finds optimal matches between intent creators and validators, earning a fee for successful coordination.

Is this skill compatible with smart contract languages?

Yes, it includes specific implementation patterns for Juvix, which is designed for high-assurance smart contracts, as well as standard Python implementations.

How does GF(3) conservation apply to fees?

It ensures that the total fee paid is perfectly distributed between the Generator (+1), Coordinator (0), and Validator (-1) so the economic sum remains balanced (sum ≡ 0 mod 3).

What fee models does this skill support?

It supports three primary models: PercentageFee (based on transaction value), GasPlusPremium (covering costs plus a fixed margin), and AuctionFee (using second-price auction mechanics).

Solver Fee & Economic Coordination

Name: Solver Fee & Economic Coordination
Author: plurigrid

byplurigrid

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API Development

Implements and manages fee distribution mechanisms for intent solvers in decentralized, Anoma-style architectures.

About

The solver-fee skill provides specialized guidance for implementing economic compensation models within intent-centric systems. It utilizes GF(3) triadic logic to act as a 'Coordinator' (0 trit), balancing the incentives between intent generators, solvers, and validators. By providing standardized implementations for percentage-based, gas-plus-premium, and competitive auction fee models, this skill ensures that solvers are fairly rewarded for finding optimal solutions while maintaining economic conservation across the network ecosystem.

Key Features

GF(3) triadic fee conservation logic
Cross-language support for Python and Juvix implementations
Pre-defined Percentage, Gas+Premium, and Auction fee models
Seamless integration patterns for Anoma-style intent architectures
Automated fee distribution between generators, coordinators, and validators
2 GitHub stars

Use Cases

Implementing fair-value fee auctions for blockchain solvers
Designing compensation models for decentralized intent-matching engines
Maintaining economic equilibrium in multi-party coordination protocols

About

Key Features

GF(3) triadic fee conservation logic
Cross-language support for Python and Juvix implementations
Pre-defined Percentage, Gas+Premium, and Auction fee models
Seamless integration patterns for Anoma-style intent architectures
Automated fee distribution between generators, coordinators, and validators
2 GitHub stars

Use Cases

Implementing fair-value fee auctions for blockchain solvers
Designing compensation models for decentralized intent-matching engines
Maintaining economic equilibrium in multi-party coordination protocols