Coordinates functional transformations through derivational pipe chaining and GF(3) state tracking.
The cat skill implements a mathematically rigorous approach to data pipelining by enforcing derivational succession over simple temporal ordering. By utilizing the GF(3) conservation principle, it ensures that sequences of operations—typically consisting of validation, coordination, and generation—maintain strict compositional coherence. This skill is ideal for developers building complex functional systems, working within the Plurigrid ASI ecosystem, or implementing category theory patterns where state transitions must be verifiable and auditable.
Key Features
01Ergodic coordination (Trit 0) for workflow bridging
02Seed-based state advancement mechanism
03Babashka-compatible functional implementation
04GF(3) conservation tracking for compositional integrity
052 GitHub stars
06Derivational pipe chaining macro for logical succession
Use Cases
01Coordinating multi-stage AI agent state transitions
02Implementing category theory bicomodules in functional code
03Building auditable data transformation pipelines
