인프라·DevOps 스킬

Build AI crisis monitoring and alerting systems. TRIGGERS - Use when user needs help with ai-crisis-monitor related tasks.

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Deploy AI models to browser environments. TRIGGERS - Use when user needs help with ai-browser-deployment related tasks.

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Design canary deployment strategies for AI. TRIGGERS - Use when user needs help with ai-canary-deployment related tasks.

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Build AI compliance monitoring systems. TRIGGERS - Use when user needs help with ai-compliance-monitor related tasks.

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Plan 5G network deployment with coverage. TRIGGERS - Use when user needs help with 5g-deployment-plan related tasks.

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Work with the upstash-box Python SDK for sandboxed cloud containers with AI agents, shell, filesystem, git, cron schedules, snapshots, and a headless browser. Use when building with Upstash Box in Python, creating a sandbox or isolated environment to run untrusted or agent-generated code, running AI coding agents in containers, giving an agent a cloud dev environment with a shell and repository, browser automation from a box, scheduling recurring jobs inside a box, saving and restoring snapshots, or orchestrating parallel boxes.

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Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM application. Also use when the user asks for a vector store, vector search, nearest-neighbor or kNN search, embeddings storage, semantic cache, recommendations or similarity features, or a hosted vector index that needs no infrastructure.

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Work with the @upstash/qstash TypeScript/JavaScript SDK, an HTTP-based message queue, task scheduler, and background job system for serverless and edge runtimes (Next.js, Vercel, Cloudflare Workers, Deno, Node.js). Use when publishing messages to HTTP endpoints or URL groups, running background jobs without a long-running worker process, scheduling with cron expressions, delaying messages, building FIFO queues with parallelism and flow control, configuring retries and callbacks, handling a dead letter queue (DLQ), deduplicating messages, fanning out to multiple endpoints, verifying QStash webhook signatures (Next.js App Router, Pages Router, and Edge Runtime), running a local QStash dev server, or migrating regions. Also use when the user asks for a serverless cron job, async task queue, job scheduler, delayed delivery, webhook delivery with retries, or event-driven messaging between services.

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Rate limiting for serverless and edge apps with the @upstash/ratelimit TypeScript/JavaScript SDK backed by Upstash Redis. Use when adding a rate limiter or throttling to an API route, Next.js middleware, Vercel Edge, Cloudflare Workers, or any HTTP endpoint; returning 429 Too Many Requests; choosing between fixed window, sliding window, and token bucket algorithms; limiting per user, IP, API key, or tenant with prefixes and custom keys; protecting login, signup, form, or AI endpoints from abuse, bots, and brute force; using deny lists, ephemeral caching, analytics, timeouts, and multi-region rate limits; or estimating the Redis command cost of rate limiting. Also use when the user says rate limit, rate-limiting, throttle, quota, request limits, or traffic protection.

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Work with any Upstash product, SDK, or tool, including serverless Redis (caching, sessions, leaderboards, key-value storage), Ratelimit (rate limiting and throttling), QStash (message queue, cron schedules, background jobs), Workflow (durable long-running functions), Vector (vector database for embeddings, semantic search, and RAG), Search (full-text and semantic search), Box (sandboxed cloud containers for AI agents, TypeScript/JavaScript and Python), Blob (serverless S3-compatible file storage with direct browser uploads), the Upstash CLI, and no-signup scratch Redis for agents. Use when the user mentions Upstash or needs any of these capabilities in a serverless, edge, or Node.js app.

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Work with the @upstash/blob TypeScript/JavaScript SDK for S3-compatible object storage with direct browser uploads, presigned URLs, multipart, and signed reads. Use when storing files or blobs, uploading avatars, images, videos, attachments or user documents, letting a browser upload straight to storage without proxying bytes through a server, generating public or time-limited signed URLs, serving private files, streaming large files with pause and resume, setting cache headers on stored objects, or reaching an S3-compatible bucket from the AWS SDK.

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Work with the @upstash/box TypeScript/JavaScript SDK for sandboxed cloud containers with AI agents, shell, filesystem, git, cron schedules, snapshots, and a headless browser. Use when building with Upstash Box, creating a sandbox or isolated environment to run untrusted or agent-generated code, running AI coding agents in containers, giving an agent a cloud dev environment with a shell and repository, browser automation from a box, scheduling recurring jobs inside a box, saving and restoring snapshots, or orchestrating parallel boxes.

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Initialize a local custom Elementor Widget plugin, design handoff workspace, and pending site-style context, choosing a Flat or Grouped plugin structure from expected Widget scale. Use once before the first custom Widget when no plugin scaffold exists; theme context is optional and read-only. Do not map Elementor Site Settings, design visuals, implement Widgets, deploy, or inspect a live WordPress installation.

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TanStack Start full-stack React framework best practices for server functions, middleware, SSR/streaming, SEO, authentication, and deployment. Use when building full-stack React apps with TanStack Start, implementing server functions, configuring SSR/streaming, managing SEO and head tags, setting up authentication patterns, or deploying to Vercel/Cloudflare/Node.

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Guidance for setting up CI/CD pipelines for DataRobot application templates using GitLab, GitHub Actions, and Pulumi for infrastructure as code. Use when setting up CI/CD pipelines, configuring deployments, or managing infrastructure for DataRobot application templates.

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Use when the user wants to design, build, code, or deploy an AI agent on DataRobot; mentions agent_spec.yaml, dress rehearsal, the DataRobot agent template, LangGraph, CrewAI, LlamaIndex, NAT, Base agents, MCP servers, backend APIs, custom frontends, or the DataRobot CLI.

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Tools and guidance for making predictions with DataRobot deployments, including real-time predictions, batch scoring, prediction dataset generation, and prediction explanations (SHAP/XEMP). Use when making predictions, running batch scoring, generating prediction datasets, or explaining individual predictions from a deployment.

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Tools and guidance for deploying DataRobot models, managing deployments, configuring prediction environments, and deployment operations. Use when deploying models, creating or updating deployments, or configuring prediction environments.

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Instrument any external or existing AI agent with OpenTelemetry to send traces, logs, and metrics to DataRobot for monitoring, observability, and governance. Use when the user says "add tracing/observability/monitoring to my agent", wants to instrument an existing agent project in their IDE, wants to send agent traces, logs, or metrics to DataRobot, or wants to record and replay agent trajectories (Tensile) from DataRobot traces.

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Tools and guidance for monitoring model performance, tracking data drift, managing model health, and detecting prediction anomalies. Use when monitoring deployed models, tracking drift, or investigating prediction anomalies.

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Use when an agent assist user wants to deploy their agent, put it on DataRobot, get a link to it, or share it with others, the agent can run as a single container, and the project has no `infra/Pulumi.yaml`. Not for agents built from the agent template, which deploy with Pulumi through datarobot-agent-assist. Users usually just say "deploy" or "deploy to DataRobot" and won't mention containers, workloads or the CLI.

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Interactively gather parameters for `python -m cli.inference.throughput_optimizer`, generate a deployment simulation command, explain assumptions, ask for execution confirmation, then run the simulation and summarize the best parallel strategy. Use when the user wants to evaluate a model on one or more hardware profiles, compare hardware targets, compare aggregation versus disaggregation versus PD ratio optimization, choose TP/EP/MOE-DP search ranges, or obtain recommended concurrency, throughput, TTFT, and TPOT results from throughput modeling.

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当用户需要部署 msmodeling optix 服务化自动寻优工具时使用。负责安装与验证。

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Adapt torch_npu.profiler interface for AI frameworks and training/inference code on Ascend NPU. Use this skill whenever users need to integrate profiling, add performance data collection, or instrument PyTorch code with torch_npu.profiler on Huawei Ascend NPU platforms. Triggers on: "torch_npu profiler", "NPU profiling", "Ascend profiler", "profiler adaptation", "profile training", "profile inference", "add profiler to framework", "性能采集", "profiling 数据", "昇腾 profiler", and any request to add performance monitoring to Ascend NPU code.

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