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Found 479 Skills
Guide for implementing HolmesGPT - an AI agent for troubleshooting cloud-native environments. Use when investigating Kubernetes issues, analyzing alerts from Prometheus/AlertManager/PagerDuty, performing root cause analysis, configuring HolmesGPT installations (CLI/Helm/Docker), setting up AI providers (OpenAI/Anthropic/Azure), creating custom toolsets, or integrating with observability platforms (Grafana, Loki, Tempo, DataDog).
Transcribe non-realtime speech with Alibaba Cloud Model Studio Qwen ASR models (`qwen3-asr-flash`, `qwen-audio-asr`, `qwen3-asr-flash-filetrans`). Use when converting recorded audio files to text, generating transcripts with timestamps, or documenting DashScope/OpenAI-compatible ASR request and response fields.
Feature-complete companion for the actual CLI, an ADR-powered CLAUDE.md/AGENTS.md generator. Runs and troubleshoots actual adr-bot, status, auth, config, runners, and models. Covers all 5 runners (claude-cli, anthropic-api, openai-api, codex-cli, cursor-cli), all model patterns, all 3 output formats (claude-md, agents-md, cursor-rules), and all error types. Use when working with the actual CLI, running actual adr-bot, configuring runners or models, troubleshooting errors, or managing output files.
Atlas Cloud API integration skill — quickly call 300+ AI image generation, video generation, and LLM models through a unified API. Use this skill when the user needs to integrate AI image generation (e.g., Flux, Seedream, DALL-E), AI video generation (e.g., Kling, Sora, Seedance), or call LLM APIs (OpenAI-compatible format) into their project. Applicable scenarios include: generating images, generating videos, calling large language models, using Atlas Cloud API, configuring ATLASCLOUD_API_KEY, querying available model lists, searching models by keyword, uploading local images/media files, one-step quick generation, image-to-video, text-to-image, text-to-video, AI content creation tool integration. Even if the user doesn't explicitly mention Atlas Cloud, this skill should be considered whenever AI media generation API integration development is involved.
Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
Universal AI video generation supporting OpenAI Sora, Google Veo 2/3, Runway Gen-3/Gen-4, Pika 2.2, Luma Dream Machine (Ray 2), FAL (Kling / Wan / Veo / Sora wrappers), Ark Seedance 1.5 Pro/Lite, Bailian Wanx (i2v), MiniMax Hailuo-02, and Vidu Q3. Use this skill whenever the user asks to generate, create, make, or synthesize a video from a text prompt or from a first-frame image. Covers text-to-video and image-to-video, with optional last-frame control on providers that support it. Typical phrases include "generate a video of ...", "make a 5-second clip of ...", "animate this image", "生成一段视频", "做个短片", or any mention of video-generation model families like Sora, Veo, Runway Gen, Kling, Wan, Seedance, Hailuo, Pika, Dream Machine, Vidu. Always use this skill even if the user does not name a specific model — pick a provider from their EXTEND.md defaults or available API keys. Do NOT use this skill when the user explicitly mentions 即梦 / Dreamina / Jimeng — those go to happy-dreamina instead.
Use PAL MCP to orchestrate multiple AI models (Gemini, OpenAI, Grok, Ollama) for code reviews, debugging, planning, and CLI bridging
Summarize a video by calling the VLM NIM or the Long Video Summarization (LVS) microservice directly. For short videos (under 60s) call the VLM's OpenAI-compatible chat completions endpoint; for long videos (60s or longer) call the LVS microservice. Use when asked to summarize a video, describe what happens in a video, analyze a recording, call or debug LVS summarize/model/health/recommended-config/metrics endpoints, or configure and troubleshoot the LVS service that backs long-video summarization.
Use whenever the user mentions LLM prompt/prefix cache misses, cached_tokens=0, cache_read_input_tokens/cache_creation_input_tokens, prompt_cache_key, cache_control/cachePoint placement, stable prefixes, tool/schema stability, TTFT/prefill latency, OpenAI/Claude/Bedrock/OpenRouter routing, vLLM/SGLang KV reuse, or LLM cost/speed regressions on repeated long prompts. Use when reviewing LLM request shape changes: prompt text, message order, request builders, tools, schemas, response_format, provider API surface, model/router settings, agent loop structure, context compaction, or inference deployment. Use for speeding up agents only when prompt-cache stability, TTFT, or cache cost is central. Do not use for generic prompt writing, generic RAG design, token counting, or non-LLM performance.
End-to-end AI video generation - create videos from text prompts using image generation, video synthesis, voice-over, and editing. Supports OpenAI DALL-E, Replicate models, LumaAI, Runway, and FFmpeg editing.
Eino component selection, configuration, and usage. Use when a user needs to choose or configure a ChatModel, Embedding, Retriever, Indexer, Tool, Document loader/parser/transformer, Prompt template, or Callback handler. Covers all component interfaces and their implementations in eino-ext including OpenAI, Claude, Gemini, Ollama, Milvus, Elasticsearch, Redis, MCP tools, and more.
[QwenCloud] Recommend the best Qwen model and parameters. TRIGGER when: choosing between Qwen models, comparing Qwen model pricing, understanding Qwen model capabilities, when an execution skill needs model selection advice, or user explicitly invokes this skill by name (e.g. use qwencloud-model-selector). DO NOT TRIGGER when: non-Qwen model discussions (OpenAI, Gemini, etc.), general AI questions unrelated to Qwen.