DevOps skills

Route CubeStudio MLOps platform deployment, customization, and operation tasks.

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Use this self-contained Browser Use repo skill for Python agents that automate websites, browser sessions, custom tools, LLM/provider setup, persistent CLI sessions, cloud/sandbox production deployment, MCP, skills, and troubleshooting. Prefer ChatBrowserUse by default and route to focused sub-skills for implementation details.

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Use BiSheng's enterprise LLM application DevOps platform repository, including FastAPI backend, workflow/RAG/Linsight runtimes, permissions, dual React frontends, deployment, and maintainer workflows.

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Use ASAP to train humanoid policies, retarget SMPL motions, and run sim2sim or sim2real deployment workflows for humanoidverse-based robots.

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Use BentoML to author model-serving Services, build and containerize Bentos, run HTTP/gRPC servers and clients, manage models, operate the CLI and BentoCloud, and configure observability or production runtime behavior. Use when tasks mention BentoML, bentoml service.py, bentofile.yaml, Bento build/serve/deploy, BentoCloud, or BentoML model stores.

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Route Baichuan2 chat inference, deployment/quantization, and fine-tuning workflows.

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Routes AWS SDK for pandas (awswrangler) workflows across S3 lakehouse data, Athena/Glue catalogs, SQL connectors, runtime configuration, and AWS service integrations.

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Routes AutoViz users to automated EDA, data-quality, and text/wordcloud workflows.

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Route Apache Airflow repo tasks across Dag authoring, operations, providers, deployment, and contribution workflows.

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AgentScope repo skill for building agents, provider connectors, RAG and memory workflows, service deployments, and local or sandboxed workspaces.

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Use Argilla 2.x for human-feedback dataset annotation, Python SDK dataset workflows, server deployment/operations, webhooks, and legacy v1/Rubrix migration.

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Routes Agent Starter Pack tasks to the right workflow for creating, maintaining, and deploying generated agent projects.

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Use anomalib for anomaly-detection install, data/model selection, training and evaluation, deployment and inference, and benchmark pipelines.

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Routes substantive ML, AI, data, scientific-computing, and software-engineering requests to the smallest useful set of managed repository skills. Invoke proactively when a request names or implies a package, framework, model family, dataset, modality, workflow, backend, deployment target, evaluation method, or implementation approach that may benefit from repository guidance, even if no repository is named. Narrow progressively from area to family to repository root: inspect only the one or two most likely area pages; compare candidates by capability, task surface, model/data format, training versus inference versus evaluation intent, runtime constraints, and root-skill description; then open only the selected root and relevant sub-skills, references, or scripts. Select multiple repositories only when each adds a distinct capability. Do not load the whole collection, treat dependencies or incidental integrations as capabilities, choose by name alone, or force a match when no exact taxonomy family applies.

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Routes AppAgent setup, exploration, and deployment workflows for Android-device task automation.

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Computes DORA delivery-performance metrics from git and GitHub API. Use when assessing deployment frequency, lead time, or change failure rate.

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Installs egregore watchdog daemon via launchd or systemd for autonomous relaunching. Use when setting up egregore on a new machine. Do not use on CI/CD runners.

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Removes the egregore watchdog daemon and its associated files. Use when stopping automated session relaunching or cleaning up egregore infrastructure.

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Scaffolds new projects with git, CI/CD workflows, pre-commit hooks, and build config. Use when starting a new Python, Rust, or TypeScript project from scratch.

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Applies serverless FaaS patterns for event-driven workloads. Use when designing bursty workloads with minimal infrastructure and pay-per-execution cost model.

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Applies pipes-and-filters for sequential data transformations. Use when data flows through discrete stages like ETL, streaming analytics, or CI/CD pipelines.

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Applies hexagonal architecture isolating domain from infrastructure. Use when designing systems where testability and port/adapter separation are priorities.

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Applies coarse-grained service architecture for deployment independence. Use when independent deployment is needed but shared databases rule out microservices.

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Applies microservices for independent deployment and per-service scaling. Use when teams need autonomous release cycles with distinct capability scaling needs.

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