How does this differ from simple validation?

While validation just rejects bad data, generalization creates a new 'refinement type' that can be used by the system to prove properties downstream without needing to re-check the data.

What is a refinement type in this context?

It is a standard data type (like an Integer) paired with a logic predicate (like 'must be even and positive') that is formally proven to hold true for a specific value.

What is constraint generalization?

It is the process of deriving a single, more specific constraint from multiple combined logic paths, ensuring that proven data properties are preserved rather than lost during composition.

Can this skill handle contradictory constraints?

Yes, it uses an underlying SAT solver (3-MATCH) to detect unsatisfiable compositions, such as a value being required to be both even and odd, and triggers appropriate error handling.

Constraint Generalization & Refinement

Name: Constraint Generalization & Refinement
Author: plurigrid

byplurigrid

•

개발자 도구

Synthesizes and propagates complex logic constraints across composed data paths to enable formal refinement typing and verification.

Constraint Generalization is a high-level logic skill that enhances data navigation by automatically deriving new, more specific constraints when multiple processing paths are composed. By leveraging concepts from abstract interpretation and constraint logic programming, it prevents the loss of critical data properties during complex transformations, allowing Claude to maintain formal proofs of validity throughout a software pipeline. This results in the creation of 'refinement types'—such as an integer guaranteed to be both even and positive—which reduces redundant runtime checks and enables the generation of formal certificates of correctness for complex architectural patterns.

주요 기능

017 GitHub stars

02Automatic refinement type generation for downstream data propagation

03Formal proof certificate generation including witness examples and complexity analysis

04Abstract interpretation to generalize or weaken constraints via a logical lattice

05Conjunctive Normal Form (CNF) constraint composition using 3-MATCH satisfiability

06Seamless integration with type inference systems for verified data pipelines

사용 사례

01Generating formal proofs for safety-critical domain logic and invariant checking

02Verifying complex data filtering pipelines where multiple predicates must hold simultaneously

03Optimizing backend services by propagating pre-proven constraints to eliminate redundant validations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi constraint-generalization

For use in Claude.ai and ChatGPT

Download Skill