Skill UI

Guides Temporal workflow orchestration in the Python SDK, covering durable workflow patterns, saga/distributed transactions, async/sync activity models, retry and timeout policies, signal/query handling, type-safe APIs, and testing/deployment best practices.

torchforge is Meta's advanced PyTorch-native library designed for agentic Reinforcement Learning (RL). It fundamentally separates core RL algorithms from complex distributed infrastructure concerns. This allows researchers to rapidly prototype and experiment with novel RL methods (like GRPO) using clean, PyTorch-native abstractions, while automatically handling large-scale tasks like distributed training across thousands of GPUs using Monarch and TorchTitan.

Provision end-to-end observability for Vercel integrations by wiring metrics, distributed tracing, structured logging, and alerting so teams can monitor deployment health through dashboards and detect issues early.

Guides training large language models with Volcano Engine's verl library, covering PPO/GRPO workflows, distributed backends, multi-turn rollouts, reward configs, critic-enabled PPO, and production deployments using Ray workers.

Handles semantic version updates across Claude Code plugin metadata, marketplace catalog, and git tags whenever a bump or release request appears, keeping all distributed files consistent.

Guides engineers through Temporal workflow orchestration architecture, covering deterministic workflows vs activities, saga compensation, entity workflows, parallel execution, async callbacks, and best practices for resilient distributed systems.

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.

Dask is a Python library that scales pandas/NumPy workflows beyond RAM by parallelizing DataFrames, Arrays, Bags, and custom tasks across cores or clusters for ETL, log analysis, and distributed ML.

PyTorch Lightning Toolkit streamlines PyTorch model training by organizing LightningModules, LightningDataModules, Trainer configs, callbacks, logging, and distributed strategies for scalable multi-GPU/TPU runs in research or production workflows.

Expert guide for building durable distributed systems with the Temporal Go SDK, covering deterministic workflow rules, mTLS worker configuration, advanced patterns, and testing/observability strategies for production workloads.

Guides Go distributed-system engineers through protobuf layout, interceptor design, error handling, TLS/mTLS setup, bufconn testing, and streaming RPC patterns so they can implement, review, and debug real-world gRPC servers and clients with production-grade operability.

Aspire is a code-first polyglot toolchain that orchestrates containers, executables, and cloud resources through an AppHost project, guiding creation, debugging, configuration, deployment, and troubleshooting for distributed applications across languages.