Loading...
Loading...
Found 495 Skills
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.
Use when doing dev-stage self-review on the current branch before pushing or opening a PR — runs an auto-loop of codex review (cross-model, OpenAI) + per-finding fix + re-review until findings converge or stop conditions fire. Codex follows pr-review's multi-role methodology (security / staff-engineer / sdet / spec-auditor). Triggers — 'self review', 'self-review', '自己 review', '自我 review', 'cross-model review', 'pre-push review', 'review and fix my branch'. NOT for live PR review with sticky/inline comments (use pr-review), NOT for managed PR babysitting (use pr-babysit), NOT for first-time review without intent to fix (use mode=review-only opt-in).
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.
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.
Build AI agents with in-process agent loops using Anthropic or OpenAI APIs, custom tools, MCP servers, and multi-turn conversations
Build high-quality /goal commands for OpenAI Codex CLI 0.128+ that maximize audit-friendliness and minimize false-completion. Use this skill whenever the user wants to write, draft, generate, improve, or refine a /goal prompt — even if they don't say "skill" — including phrases like "help me write a goal", "design a goal for X", "review my goal command", "make a goal for this repo", or any request involving long-running Codex tasks. Also trigger when the user mentions Ralph loop, persistent agent objectives, or asks Codex to "keep working until done". Produces a complete, copy-pasteable /goal command using the 5-section golden template (Objective/Scope/Constraints/Done when/Stop if), supports three interaction modes (step-by-step, full-description, hybrid), auto-detects project type (Node/Python/Swift/Go/Rust/static) by inspecting filesystem or repo URL, reads AGENTS.md/CLAUDE.md if present, and predicts audit-friendliness before output.
Add new LLM model pricing entries to Langfuse's default-model-prices.json. Use when adding model prices, updating model pricing, creating model entries, adding Claude/OpenAI/Anthropic/Google/Gemini/AWS Bedrock/Azure/Vertex AI model pricing, working with matchPattern regex, pricingTiers, or model cost configuration. Covers model price JSON structure, regex patterns for multi-provider matching, tiered pricing with conditions, cache pricing, and validation rules.
Expert knowledge for Azure AI Video Indexer development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Video Indexer APIs/widgets, live camera indexing, custom speech/brand models, or Azure OpenAI integrations, and other Azure AI Video Indexer related development tasks. Not for Azure AI services (use microsoft-foundry-tools), Azure AI Vision (use azure-ai-vision).
Expert knowledge for Azure Functions development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building HTTP/queue/event-triggered Functions, Durable orchestrations, containerized Functions, CI/CD, or Dapr/OpenAI integrations, and other Azure Functions related development tasks. Not for Azure App Service (use azure-app-service), Azure Logic Apps (use azure-logic-apps), Azure Container Apps (use azure-container-apps), Azure Kubernetes Service (AKS) (use azure-kubernetes-service).
Command-line interface for Novita AI - An OpenAI-compatible AI API client for DeepSeek, GLM, and other models.