Agent Skills: runtime-skills
Universal Runtime best practices for PyTorch inference, Transformers models, and FastAPI serving. Covers device management, model loading, memory optimization, and performance tuning.
ID: llama-farm/llamafarm/runtime-skills
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- run-posthogStart, inspect, and drive the PostHog dev stack. Use for /run and /verify on this repo — when asked to launch PostHog, check whether the stack is healthy, inspect a running process, or verify a UI change against the live app.
- neurokit2Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
- webf-native-plugin-devDevelop custom WebF native plugins based on Flutter packages. Create reusable plugins that wrap Flutter/platform capabilities as JavaScript APIs. Use when building plugins for native features like camera, payments, sensors, file access, or wrapping existing Flutter packages.
- teams-channel-post-writer>-
- hf-cloud-sagemaker-production-defaultsCreate a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.
- releaseRelease preparation workflow - security audit → E2E tests → review → changelog → docs