Softmax
Official@metta-ai · United States of America
Offers specialized governance and performance validation for Cortical Fabric architectures, focusing on backend execution boundaries and cell-level scaling.
Agent Skills by Softmax
Showing 7 vetted skills indexed across 1 GitHub repositories.
cb.fabric-parity-gate
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cb.fabric-performance-loop
Identify and validate Fabric performance characteristics during profiling and benchmarking.
cb.fabric-cell-onboarding
Onboard new Cortical Fabric cell families by aligning registration patterns and tests.
cb.fabric-backend-boundaries
Enforce Fabric CUDA backend ownership boundaries during code edits.
cb.fabric-scaling-horizon
Expand Cortical Fabric scaling horizons and validate T streaming-horizon behavior.
cb.fabric-cell-boundaries
Enforce boundary rules between Fabric cell semantics and backend execution ownership.
fabric-training-loop
Plan and execute Fabric training experiments with structured logs under ai_docs/.
Frequently Asked Questions About Softmax
FAQPage SchemaWhat specific tasks does Softmax enable for infrastructure engineers?▼
Softmax enables engineers to enforce strict backend ownership boundaries, validate performance metrics during benchmarking, and manage the onboarding of new cell families within Cortical Fabric environments. It ensures system integrity by aligning registration patterns and validating streaming-horizon behaviors across distributed compute architectures.
Which technical personas benefit from these capabilities?▼
These capabilities are designed for infrastructure architects, systems engineers, and performance analysts working with Cortical Fabric. It is specifically targeted at technical teams responsible for maintaining backend execution boundaries and scaling compute horizons in high-performance environments.
What are the prerequisites for implementing these fabric-level controls?▼
Implementation requires an existing Cortical Fabric environment and access to the specific cell family definitions. Users must ensure their environment supports CUDA backend integration and that all training experiments are configured to output structured logs to the designated documentation directories.