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Found 2,484 Skills
PixVerse CLI — generate AI videos and images from the command line. Supports PixVerse, Veo, Sora, Kling, Hailuo, Wan, and more video models; Nano Banana (Gemini), Seedream, Qwen image models; and PixVerse's rich effect template library. Start here.
Use when the workflow feels too complex, has accumulated cruft, or has redundant steps and overlapping tools that need consolidation.
This skill should be used when a developer wants to autonomously execute all tasks under a fully-specified Epic or Feature — for example "go", "start building", "implement everything", "run the loop", "execute the feature", "build it all", "kick it off". Requires that the Epic/Feature/Task tree is fully written before starting. Chains implement → verify → PR for every task in dependency order, with targeted human-in-the-loop gates for contradictions and ambiguities.
Use when the user asks "what can Cekura do", "what commands are available", "help me with Cekura", "what skills do I have", "show me Cekura features", "what's available", "how do I use Cekura", or needs guidance on which Cekura skill to use for their task. Also relevant as the entry point when a user has just installed cekura-skills for the first time.
Create Huawei Cloud Flexus L Instance (Lightweight Server), deploy OpenClaw application platform on it, and support installation and configuration of models and channels for deployed OpenClaw instances. Web UI access needs to be manually enabled in Huawei Cloud console. Trigger words: "Deploy OpenClaw", "Deploy Flexus L Instance", "Deploy Huawei Cloud Lightweight Server", "Model setting", "Channel Setting", "部署OpenClaw", "部署Flexus L实例", "部署华为云轻量服务器", "设置模型", "设置通道"
Dynamic tool selection, composition, and error handling patterns for AI agents. Use when you need to efficiently leverage available tools and handle failures gracefully.
Task decomposition, goal-oriented planning, and adaptive execution strategies for AI agents. Use when facing complex multi-step tasks that require structured approach.
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
Use beads (bd) for persistent task tracking in coding projects. A git-backed issue tracker designed for AI agents with dependency graphs, hierarchical tasks, and multi-agent coordination.
Systematically debug issues, investigating bugs, troubleshooting problems, or tracking down errors with persistent state across context resets. Triggers include "debug", "investigate bug", "troubleshoot", "find the problem", "why isn't this working", and "debug session".
Amazon Bedrock AgentCore Evaluations for testing and monitoring AI agent quality. 13 built-in evaluators plus custom LLM-as-Judge patterns. Use when testing agents, monitoring production quality, setting up alerts, or validating agent behavior.
Retrieve real-time or historical cash flow statement data including Net Income, Operating Cash Flow, Investing Cash Flow, Financing Cash Flow, Free Cash Flow, and Cash Position for public companies. Use when analyzing cash generation, capital allocation, or liquidity trends.