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Found 69 Skills
Command-line interface for Novita AI - An OpenAI-compatible AI API client for DeepSeek, GLM, and other models.
Reference Documentation for Jiekou AI Model Services, covering LLM API (OpenAI-compatible), Image/Video/Audio APIs, integration solutions, authentication/billing/pricing/rate limiting, and troubleshooting. Suitable for questions like "How to integrate Jiekou AI into tools such as OpenAI SDK / LangChain?" and issues like Jiekou AI request failures.
Configures TrueFoundry AI Gateway end-to-end. Covers unified OpenAI-compatible LLM access, provider account integrations, content safety guardrails, and observability (traces, costs, errors) via the spans query API.
Official Reference Guide for the PPIO Platform, covering LLM API (OpenAI-compatible), Agent Sandbox, GPU (Instances and Serverless), integration, authentication, pricing, rate limiting, and troubleshooting. Suitable for common questions such as 'How to integrate PPIO in specific application scenarios?' and PPIO request failures.
LangGraph-based agent framework for consistent tool calling with automatic tool loops. Use when you need reliable multi-step task execution with OpenAI-compatible providers (Z.AI/GLM-5, OpenRouter, Groq, DeepSeek, Ollama).
OpenAI-compatible SaaS gateway that reverse-engineers chatgpt.com to provide GPT Image 2, multi-account pooling, batch image generation, and billing management.
Measures the task accuracy of text models served by MAX using standard benchmarks such as GSM8K, MMLU, HellaSwag, ARC, AIME, GPQA, TruthfulQA, WinoGrande, and BABILong. Use when benchmarking a served model, comparing it with model-card or reference scores, verifying that a new MAX model produces correct answers, or running repeatable dataset evaluations against a MAX OpenAI-compatible endpoint.
Use this skill when working with the RTVI VLM or RT-VLM microservice API on VSS 3.1. Generate dense captions and alerts for stored video files and live RTSP streams via `/v1/generate_captions_alerts`; upload media via `/v1/files`; add and remove live streams with `/v1/streams/add` and `/v1/streams/delete/{stream_id}`; call OpenAI-compatible `/v1/chat/completions`; consume Kafka caption, incident, and error topics; or debug rtvi-vlm responses. For deployment, read `references/deploy-rt-vlm-service.md` first.
Use when working on vLLM Studio backend architecture (controller runtime, Pi-mono agent loop, OpenAI-compatible endpoints, LiteLLM gateway, inference process, and debugging commands).
Use this skill when exposing a Docker Agent as a server (MCP, HTTP API, A2A, ACP, or OpenAI-compatible chat), distributing an agent via an OCI registry with `docker agent share`, or measuring agent quality with `docker agent eval`. Even if the user just says they want to "turn my agent into an MCP server", "let Claude Desktop use my agent", "publish my agent to Docker Hub", "push my agent like an image", or "test my agent in CI", this skill applies. Covers `serve mcp/api/a2a/acp/chat` listen addresses and auth flags, `share push/pull`, eval session JSON format, scoring metrics, and the `--baseline` regression gate.
Use ElevenLabs Realtime models (`eleven_realtime_v1_mini`, `eleven_realtime_v1_max`), the low-latency models built for voice conversation. Use when the user wants to create an ElevenAgents agent on a Realtime model or switch an existing agent's Voice mode to Realtime, set `conversation_config.realtime_model`, choose between Realtime Mini and Max, or debug a Realtime agent that won't publish or transfer. Also use when migrating a voice app built on OpenAI's Realtime API and SDK (`@openai/agents-realtime`, `gpt-realtime`) to the OpenAI-compatible ElevenLabs Realtime API, including the `client_secrets` mint, the WebSocket transport, and audio after leaving WebRTC (closes with 4001/4003, no audio). Not for choosing an agent's LLM or TTS model in Custom mode, Scribe realtime speech-to-text, or migrating Chat Completions or Responses API text calls.
Run a bounded red-teaming autoresearch loop to generate LLM guardrail training data. You (the agent running the skill) are the attacker and the judge: you craft attacks and label every response. The only model the harness calls is the target under test, over any OpenAI-compatible API (OpenRouter, Moonshot/Kimi, Fireworks, Ubicloud, OpenAI, or custom), with its key in `.red-team/.env`. Every attempt (pass and fail) is written to JSONL ready for fine-tuning guardrails in Llama Guard format. Use when asked to red-team or stress-test an LLM for harmful content, jailbreaks, prompt injection, or backdoor/trigger behavior; to build a safe/unsafe dataset; or to mine novel attacks at scale. Complements the hacker skill (code/infra exploitability): this skill targets model behavior and produces data.