Results for “wit-ai”

Requires AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `aisa-provider`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Configure AIsa as a first-class model provider for OpenClaw, enabling production access to major Chinese AI models (Qwen, DeepSeek, Kimi K2.5, Doubao) through official partnerships with Alibaba Cloud, BytePlus, and Moonshot. Use this skill when the user wants to set up Chinese AI models, configure AIsa API access, compare pricing between AIsa and other providers (OpenRouter, Bailian), switch between Qwen/DeepSeek/Kimi models, or troubleshoot AIsa provider configuration in OpenClaw. Also use when the user mentions AISA_API_KEY, asks about Chinese LLM pricing, Kimi K2.5 setup, or needs help with Qwen Key Account setup.

Build real-time voice AI applications with bidirectional WebSocket communication using the Azure AI Voice Live SDK for .NET.

Installs and configures Apache Airflow via pip or Docker, with guidance on constraints, requirements, and deployment options.

Deploy, manage, or withdraw from an AI agent vault on the Versa onchain adversarial AI arena on Base.

Configure Azure API Management as an AI Gateway to govern AI models, MCP tools, and agents with policies for caching, rate limiting, content safety, and cost control.

Configure AIDE for file integrity monitoring, including baseline creation, scheduled integrity checks, change detection, and alerting.

Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.

Guides users to install and run the ai-ready skill for making repositories AI-ready with AGENTS.md, instructions, and CI workflows.

Configure AIsa as a first-class model provider for OpenClaw, enabling production access to major Chinese AI models (Qwen, DeepSeek, Kimi K2.5, Doubao) through official partnerships with Alibaba Cloud, BytePlus, and Moonshot. Use this skill when the user wants to set up Chinese AI models, configure AIsa API access, compare pricing between AIsa and other providers (OpenRouter, Bailian), switch between Qwen/DeepSeek/Kimi models, or troubleshoot AIsa provider configuration in OpenClaw. Also use when the user mentions AISA_API_KEY, asks about Chinese LLM pricing, Kimi K2.5 setup, or needs help with Qwen Key Account setup.

Manage Azure AI Foundry projects with .NET SDK: create and run agents, manage connections, datasets, deployments, evaluations, and indexes.

Integrate with Azure AI services including Search, Speech, OpenAI, and Document Intelligence for search, transcription, OCR, and text-to-speech tasks.

Navigate Oracle Cloud Infrastructure's Enterprise AI services: choose models, build agents with RAG and tools, estimate costs, secure access, and integrate with Oracle Database, APEX, and other platform services.

Design, operate, and troubleshoot OCI services including OKE, IoT, Functions, and Enterprise AI with OCI Generative AI models, agents, RAG, and cost estimation.

Manage Arize AI integrations that store LLM provider credentials for evaluators and other features. Supports creating, reading, updating, and deleting integrations with providers like OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, and NVIDIA NIM.

Integrate real-time bidirectional voice conversations with AI assistants using the Azure AI VoiceLive SDK for Java, including WebSocket streaming, turn detection, and voice configuration.

Search Azure AI Search indexes using the Python SDK for full-text, vector, hybrid, and semantic search with AI enrichment.

Build WinDAGs — the orchestration platform where AI agents accumulate genuine expertise through DAGs of skillful agents. Covers DAG design, execution engines, meta-DAG architecture, skill selection, dynamic mutation, visualization, and deployment. Activate on "windags", "agent DAG", "DAG of agents", "workflow orchestration", "agent pipeline", "dynamic DAG", "meta-DAG", "build windags", "implement windags". NOT for understanding WHY decisions were made (use windags-avatar), creating individual skills (use skill-architect), or managing skill libraries (use windags-librarian).

Designs and implements a Google Cloud solution for live, bidirectional multimodal streaming workloads with AI agents, covering requirements discovery, architecture design, and deployment planning.

Predicts the highest-impact next action for your project by running a 5-agent meta-DAG pipeline. Gathers project signals automatically (git, recent files, port-daddy, CLAUDE.md), then runs sensemaker → decomposer → skill-selector + premortem → synthesizer. When execution is approved, convert each predicted node into a skillful node prompt using skillful-node-prompt + skillful-subagent-creator, prefer live WinDAGs visualization backed by POST /api/execute and /ws/execution/:id, and fall back to ASCII only when live visualization is unavailable. Activate on: "what should I do", "what's next", "next move", "/next-move", "where should I focus", "what's the highest impact thing right now". NOT for: creating skills, debugging one specific bug, or promising topology-specific runtime behavior the current server cannot execute.

Use when designing AI product trust, privacy, or honesty UX — cloud disclosure, voice/mic consent, transcript retention, memory ownership, AI labeling, caveats, or audit footprints. Trigger on disclose cloud, recording consent, watermark, incognito, or data-ownership settings.

Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK, with support for function tools, hosted tools, MCP servers, conversation threads, and streaming responses.

Use when SaaS deployment, CI/CD pipelines, infrastructure automation, or platform operations is needed. This agent specializes in SaaS DevOps within the SaaSForge AI ecosystem.

Create and manage persistent AI agents with threads, messages, runs, and tools using the Azure SDK for Java.

Analyze text and images for harmful content using Azure AI Content Safety, with customizable blocklists and severity thresholds.

Use when user asks about writing trading strategies, backtesting, deploying Freqtrade bots, quantitative trading, or strategy optimization. Trigger words: 'write strategy', 'create strategy', 'backtest', 'deploy Freqtrade', 'deploy bot', 'quantitative', 'hyperopt', '写策略', '创建策略', '回测', '部署', '量化', '策略优化'. This skill provides: (1) create_strategy quick generator with 17 indicators, (2) AiCoin Python SDK (aicoin_data.py) for integrating real market data into custom strategies, (3) deploy/backtest/hyperopt tools. ALWAYS actively use AiCoin data (funding rate, L/S ratio, whale orders, etc.) in strategies when the user's API key supports it. For prices/charts use aicoin-market. For trading use aicoin-trading. For Hyperliquid use aicoin-hyperliquid.

Rent, manage, and destroy GPU instances on vast.ai. Use when user says "rent gpu", "vast.ai", "rent a server", "cloud gpu", or needs on-demand GPU without owning hardware.

Translate Word, PDF, Excel, PowerPoint, and other documents at scale using Azure AI Document Translation SDK with format preservation.

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

Build search applications with vector, hybrid, and semantic search using the Azure AI Search SDK for TypeScript.

Deploy and serve AI/ML models on Modal's serverless cloud platform with on-demand GPUs, autoscaling containers, persistent storage, and scheduled jobs.