Use when launching cloud VMs, Kubernetes pods, or Slurm jobs for GPU/TPU/CPU workloads, training or fine-tuning models on cloud GPUs, deploying inference servers (vllm, TGI, etc.) with autoscaling, writing or debugging SkyPilot task YAML files, using spot/preemptible instances for cost savings, comparing GPU prices across clouds, managing compute across 25+ clouds, Kubernetes, Slurm, and on-prem clusters with failover between them, troubleshooting resource availability or SkyPilot errors, optimizing cost and GPU availability, or migrating an existing Slurm workload (converting sbatch scripts, salloc sessions, job arrays or srun invocations into SkyPilot task YAMLs, and mapping Slurm commands, directives and SLURM_* environment variables to their SkyPilot equivalents).

source

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openai-docs

vmain

SkillSkillsMP

io.github.openai/codex/openai-docs

Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex.

source

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ml-engineering

vmain

SkillSkillsMP

skillsmp.stas00-ml-engineering-skill-md

Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference performance and memory; debugging multi-node/ multi-GPU hangs; and writing/running tests. Use when the user is training or fine-tuning large models, hits low TFLOPS/MFU, OOM, slow dataloading, a loss spike/divergence, a NCCL/InfiniBand or multi-node hang, node/GPU failures, checkpoint or preemption problems, storage/network bottlenecks, or needs to pick GPUs/cloud/file-systems or size inference latency/throughput. Distilled from "Machine Learning Engineering", the latest version of which can be found at https://github.com/stas00/ml-engineering The latest SKILL.md version can be found at https://github.com/st

source

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adding-activity-logging

vmaster

SkillSkillsMP

io.github.PostHog/posthog/adding-activity-logging

Adds or changes activity logging (the audit trail) for a Django model in PostHog. Use when a model's writes must show in the Activity side panel or the advanced activity logs, when adding ModelActivityMixin, an ActivityScope, a model_activity_signal receiver, an activity describer, or field exclusions, when auditing which write paths of a model are logged, or when a change is missing from the activity log. Covers the receiver-module convention, writes the signal cannot see (QuerySet.update, bulk_create), the actor outside requests, and product models on a separate database. Trigger terms - activity log, audit log, audit trail, ModelActivityMixin, log_activity, changes_between, activity describer, who changed this.

source

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gtars

vmain

SkillSkillsMP

io.github.K-Dense-AI/scientific-agent-skills/gtars

Use Gtars for local genomic interval models and set algebra, overlaps and counts, consensus and coverage, tokenization, fragment processing, and refget/BEDbase planning across Python, Rust, and the CLI.

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modal

vmain

SkillSkillsMP

io.github.K-Dense-AI/scientific-agent-skills/modal

Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.

source

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splitting-oversized-modules

vmaster

SkillSkillsMP

io.github.PostHog/posthog/splitting-oversized-modules

Split an oversized Python module (a thousand-plus-line logic.py, models.py, api.py, or its test file) into a package of one module per concern, mechanically and provably without changing behavior. Use on a request to split / break up / decompose a god module or move functions out of one, once a human has agreed to split one before some other change, or before restructuring code inside a module already over roughly a thousand lines — breaking up a long function or extracting helpers in place leaves everything in the same file, so check the worth-it gate and propose the split first. Covers that gate, assigning symbols to concerns with an acyclic dependency graph, the AST plus tokenize move script, and proving the result is a pure move. Python only — for frontend files use writing-ui-components. Not for extracting a shared helper into common/, and not for moving code between products, which is isolating-product-facade-contracts.

source

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open-source

vmain

SkillSkillsMP

io.github.browser-use/browser-use/open-source

Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill instead.

source

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writing-dataclasses

vmaster

SkillSkillsMP

io.github.PostHog/posthog/writing-dataclasses

House rules for Python dataclasses in PostHog: when to reach for one instead of a tuple or `dict[str, Any]`, which decorator to use (`@frozen` from `posthog.dataclasses`), how to name, construct, consume and evolve them, how to keep secrets out of `repr`, and when a function should accept a dataclass instead of its unpacked fields. Use when adding or changing a dataclass, returning or passing several values from a function, converting a tuple or dict payload, deciding `frozen=`/`slots=`/`kw_only=`, or passing a facade contract DTO through internal layers. Not for pydantic models used as HogQL/query schema, DRF serializers, or Django models.

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aspire-deployment

vmain

SkillSkillsMP

io.github.dotnet/eshop/aspire-deployment

**WORKFLOW SKILL** — Deploy Aspire apps from AppHost models to Docker Compose, Kubernetes, Azure, or AWS. WHEN: "deploy Aspire app", "publish Aspire artifacts", "deploy to Azure Container Apps", "generate Kubernetes artifacts", "tear down Aspire deployment". INVOKES: aspire CLI, Aspire docs, target cloud/container CLIs. FOR SINGLE OPERATIONS: use generic Azure, Kubernetes, Docker, or AWS tools only when no Aspire AppHost exists.

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openai-docs

vmain

SkillSkillsMP

io.github.openinterpreter/openinterpreter/openai-docs

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auditing-llm-gateway-parity

vmaster

SkillSkillsMP

io.github.PostHog/posthog/auditing-llm-gateway-parity

Audits services/llm-gateway against PostHog/ai-gateway and updates services/llm-gateway/PARITY.md from current implementation evidence. Use when either gateway changes auth, attribution, billing, endpoints, providers, models, routing, or metadata; when reviewing a Python gateway change; or when asked to refresh, verify, or report gateway parity. This skill updates the parity record but does not migrate callers.

source

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senior-ml-engineer

vmain

SkillSkillsMP

io.github.alirezarezvani/claude-skills/senior-ml-engineer

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.

source

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xlsx

vmain

SkillSkillsMP

io.github.K-Dense-AI/scientific-agent-skills/xlsx

Create, edit, analyze, or convert Excel spreadsheets (.xlsx, .xlsm, .xltx) where the workbook file is the primary deliverable. Use for formulas, formatting, financial models, multi-sheet workbooks, and tabular cleanup exported to Excel. Also applies to .csv/.tsv when the user wants spreadsheet output. Do NOT use for Word documents, HTML reports, standalone Python scripts, database pipelines, or Google Sheets API work.

source

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azure-openai-to-responses

vmain

SkillSkillsMP

io.github.microsoft/ai-agents-for-beginners/azure-openai-to-responses

Shift Python apps dem from Azure OpenAI Chat Completions go Responses API. E cover AzureOpenAI/AsyncAzureOpenAI client shift go v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, plus model compatibility checks. Na Python-focused, Azure OpenAI-specific. USE FOR: shift go responses API, change from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions go responses, gpt-5 migration, azure openai python migration, chat completions go responses, AzureOpenAI go OpenAI client, python azure openai upgrade. DO NOT USE FOR: build new apps from scratch (start with responses directly), Node/TypeScript/C#/Java/Go migrations (dis skill na Python-only), Azure infrastructure setup (use azure-prepare), deploy models (use microsoft-foundry).

source

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azure-openai-to-responses

vmain

SkillSkillsMP

io.github.microsoft/generative-ai-for-beginners/azure-openai-to-responses

Migrate Python apps from Azure OpenAI Chat Completions to the Responses API. Covers AzureOpenAI/AsyncAzureOpenAI client migration to the v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, and model compatibility checks. Python-focused, Azure OpenAI-specific. USE FOR: migrate to responses API, switch from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions to responses, gpt-5 migration, azure openai python migration, chat completions to responses, AzureOpenAI to OpenAI client, python azure openai upgrade. DO NOT USE FOR: building new apps from scratch (start with responses directly), Node/TypeScript/C#/Java/Go migrations (this skill is Python-only), Azure infrastructure setup (use azure-prepare), deploying models (use microsoft-foundry).

source

在 SkillsMP 查看

azure-openai-to-responses

vmain

SkillSkillsMP

io.github.microsoft/ai-agents-for-beginners/azure-openai-to-responses

source

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传输类型是什么？

stdio（本地进程）— MCP 以子进程运行在本地，通过标准输入/输出与 Cursor、Claude 等通信。最常见，适合 npx/uvx 启动的工具。
streamable-http（远程 HTTP）— MCP 托管在远程服务器，客户端通过 HTTP 连接。适合团队共享或无法本地安装的服务。
sse（远程 SSE）— 通过 Server-Sent Events 长连接访问远程 MCP。部分托管平台使用此方式。

来源

全部来源官方 MCP Registry SkillsMP ClawHub smithery GitHub MCP Hub 中国