部署与运维
4,382 个结果
- Ttscnagents365-aiMulti-platform Chinese & multilingual TTS text-to-speech via Edge/Doubao/CosyVoice/Qwen3/StepFun/GLM-TTS/Azure/Tencent/Baidu/MiniMax/Xunfei plus ElevenLabs/OpenAI/Google/Atlas Cloud — 15 backends, word-level timestamps, [PAUSE:x] pause markers, pinyin pronunciation overrides
- Type4me Macos Voice Inputreason-machinesMacOS voice input tool with local/cloud ASR engines, LLM text optimization, and fully local storage built in Swift
- Vercel Labs Emulatereason-machinesLocal drop-in API emulation for Vercel, GitHub, and Google services in CI and no-network sandboxes
- Vinext Vite Nextjsreason-machinesVite plugin that reimplements the Next.js API surface for deploying anywhere, including Cloudflare Workers
- Worldmonitor Intelligence Dashboardreason-machinesReal-time global intelligence dashboard with AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking
- Csharp WolverinefxwshaddixBuild .NET applications with WolverineFX for messaging, HTTP services, and event sourcing. Use when implementing command handlers, message handlers, HTTP endpoints with WolverineFx.HTTP, transactional outbox patterns, event sourcing with Marten, CQRS architectures, cascading messages, batch message processing, or configuring transports like RabbitMQ, Azure Service Bus, or Amazon SQS.
- GitLab Stack CreatorrknallCreate new GitLab stack projects from templates with proper directory structure, git configuration, validation hooks, and documentation. Use when initializing new Docker stack projects that follow GitLab stack patterns.
- Hyva Exec Shell Cmdhyva-themesUtility skill to detect Magento development environment and determine command wrapper. This skill should be used by other skills that need to execute shell commands in the Magento environment. It detects Warden, docker-magento, DDEV, and local environments and provides the appropriate command wrapper.
- UI/UX Design ReviewrknallComprehensive design review for websites and desktop applications with extensive accessibility analysis. Use this skill when users ask you to review UI/UX designs, wireframes, mockups, prototypes, or deployed interfaces for usability, accessibility (WCAG compliance), visual design, interaction patterns, responsive design, and best practices for web and desktop applications.
- Deployment Engineerzhaono1Deployment automation specialist for CI/CD pipelines and infrastructure. Use when setting up deployment, configuring CI/CD, or managing releases.
- Agent EvaluationMLflowUse this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing.
- Analyzing Mlflow SessionMLflowAnalyzes an MLflow session — a sequence of traces from a multi-turn chat conversation or interaction. Use when the user asks to debug a chat conversation, review session or chat history, find where a multi-turn chat went wrong, or analyze patterns across turns.
- Analyzing Mlflow TraceMLflowAnalyzes a single MLflow trace to answer a user query about it. Use when the user provides a trace ID and asks to debug, investigate, find issues, root-cause errors, understand behavior, or analyze quality. Triggers on "analyze this trace", "what went wrong with this trace", "debug trace", "investigate trace", "why did this trace fail", "root cause this trace".
- Build A ScorerMLflowHelp the user choose, create, iterate, or align MLflow judges and scorers for an agent, RAG app, LLM app, or GenAI workflow, including MemAlign and domain-expert labels. Build a small set of atomic quality criteria with the cheapest reliable implementation.
- Cloud Native ReadinesslabringDetermine whether a repository contains a supported cloud workload, then assess eligible targets for cloud-native readiness with a 0-12 score. Use for containerization readiness, Docker/Kubernetes compatibility, deployment feasibility, workload eligibility, or pre-deployment assessment. Also triggers on "/cloud-native-readiness".
- Debug AgentMLflowRoot-causes AI agent failures from MLflow traces without requiring a code change, regression suite, scorer, or evaluation run. Use when the user asks to debug, investigate, explain, or determine why an agent produced an output. Prefer an existing matching trace; reproduce only when no usable trace exists.
- Docker To SealoslabringConvert Docker Compose files or installation docs into production-grade Sealos templates with role-specific personal low-load resource sizing, official route and runtime semantics, KubeBlocks database and Job gates, and managed or optional S3 storage contracts.
- Dockerfile SkilllabringGenerate production-ready Dockerfile for any GitHub project. Supports monorepo, multi-stage builds, workspace detection, and iterative build-fix cycles. Use when user asks to create, generate, write, fix, or improve a Dockerfile, wants to containerize an application, mentions Docker build issues, needs a .dockerignore, or wants to package their app as a Docker image.
- Instrumenting With Mlflow TracingMLflowInstruments Python and TypeScript code with MLflow Tracing for observability. Must be loaded when setting up tracing as part of any workflow including agent evaluation. Triggers on adding tracing, instrumenting agents/LLM apps, getting started with MLflow tracing, tracing specific frameworks (LangGraph, LangChain, OpenAI, Gemini, DSPy, CrewAI, AutoGen), or when another skill references tracing…
- Landodysseus0Land a PR by monitoring conflicts, resolving them, waiting for checks, and squash-merging when green; use when asked to land, merge, or shepherd a PR to completion.
- Mlflow AgentMLflowMaster dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: "use mlflow", "help with mlflow", "mlflow agent", "add mlflow to my project", "trace my agent", "evaluate my agent", or any MLflow task without a specific skill in mind.
- Mlflow OnboardingMLflowOnboards users to MLflow by determining their use case (GenAI agents/apps or traditional ML/deep learning) and guiding them through relevant quickstart tutorials and initial integration. If an experiment ID is available, it should be supplied as input to help determine the use case.
- Querying Mlflow MetricsMLflowFetches aggregated trace metrics (token usage, latency, trace counts, quality evaluations) from MLflow tracking servers. Triggers on requests to show metrics, analyze token usage, view LLM costs, check usage trends, or query trace statistics.
- Retrieving Mlflow TracesMLflowRetrieves MLflow traces using CLI or Python API. Use when the user asks to get a trace by ID, find traces, filter traces by status/tags/metadata/execution time, query traces, or debug failed traces. Triggers on "get trace", "search traces", "find failed traces", "filter traces by", "traces slower than", "query MLflow traces".