Agent skills for VS Code

Generate mock data and test fixtures from schemas

Design a mock server for an API — tooling selection, response fixtures, and error scenarios.

Deploy execution scripts to Modal cloud.

Run GPU workloads on Modal — training, fine-tuning, inference, batch processing.

Comprehensive Modal.com platform knowledge covering all features, pricing, and best practices

Serverless GPU compute platform for AI model deployment — web endpoints, GPU functions, model serving, and TypeScript client patterns

Serverless GPU cloud platform for running ML workloads.

Automatically discover Modal platform skills when working with Modal.

Debugs Modbus RTU and TCP communications for embedded systems through register read/write operations and slave scanning.

Model documentation skill for generating model cards following Google's model card framework.

Avoid ensembling models with significantly different performance.

Expert at writing Rails 8 models with proper validations, associations, scopes, callbacks, and concerns.

Save, load, inspect, minify, and share scvi-tools model artifacts

Use when building the analytical model or operational mechanism at the core of a Manufacturing & Service Operations Management (M&SOM)…

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining.

Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models.

Guide for maintaining the MassGen model and backend registry.

Allocate models for one task from the runtime catalogue, with operator confirmation, light-worker eligibility rules, and a…

Generate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, classification, detection…

When to activate: model serving, inference, FastAPI ML, BentoML, Triton, vLLM, model deployment, online inference, batching

Vet the concrete third-party model a study will be built on — this repository, this revision, this checkpoint — not the architecture family.