Total 56,346 skills, AI & Machine Learning has 9380 skills
Showing 12 of 9380 skills
Claude Code AI-assisted development workflow. Activate when discussing Claude Code usage, AI-assisted coding, prompting strategies, or Claude Code-specific patterns.
Optimize Ideogram costs through tier selection, sampling, and usage monitoring. Use when analyzing Ideogram billing, reducing API costs, or implementing usage monitoring and budget alerts. Trigger with phrases like "ideogram cost", "ideogram billing", "reduce ideogram costs", "ideogram pricing", "ideogram expensive", "ideogram budget".
Full-power feature implementation with parallel subagents. Use when implementing, building, or creating features.
Verify worktree plugin patches are intact after plugin updates. Checks compound-engineering and superpowers skills for Claude Code launch instructions.
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
Self-improvement and learning skill that helps Claude learn from user interactions, corrections, and preferences
Monitor 패턴으로 에픽 내 모든 Story의 구현을 조율한다. 모든 난이도의 Story를 agent에게 위임하고, sprint-status.yaml은 Lead만 갱신한다.
모드 기반 완전 자율 실행기. plan 모드에서 Story 구조를 생성하고, epic 모드에서 에픽 1개를 자율 실행(implement → E2E → review)한다. 사람과 직접 소통하지 않는다.
Tool for creating and validating Agent Skills. Use when users want to create a new skill or manage existing skills. Supports initialization, validation, and iterative development workflows.
Deploy and operate the RTVI-CV-3D stack (also known as MV3DT, Multi-View 3D Tracking, or RTVI-CV-MV3DT) — per-camera DeepStream perception plus BEV Fusion over multiple calibrated cameras. Use when the user says "deploy RTVI-CV-3D", "deploy rtvi-cv-3d", "deploy MV3DT", "deploy multi-view 3D tracking", "deploy rtvi-cv-mv3dt", "enable multi-camera tracking", "enable multi camera tracking", "set up multi-camera tracking", "multi-camera tracking", "run RTVI-CV-3D on my videos", "run MV3DT on my videos", "run RTVI-CV-3D / MV3DT on RTSP", "run on the sample dataset", "set up 3D tracking", or provides a 4-camera warehouse video/RTSP set. Routes between sample-data, custom-videos, and custom-RTSP flows; auto-chains to `vss-generate-video-calibration` when calibration data is missing.
Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets. Use when running nemo-rl-auto-research campaigns, experiments, training jobs, model or dataset downloads, shared cache-heavy commands, log-producing runs, checkpoint generation, W&B or Hugging Face authenticated workflows, or any workflow that may create large files on Brev.
Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container. Use when the user wants to fine-tune a HuggingFace model (full or LoRA), train a vision / VLM / LLM model end-to-end, generate a reproducible HF training pipeline, smoke-test a HuggingFace model locally before scale-up, push a fine-tuned model to the HF Hub with a model card, or emit a self-contained rerun skill for an existing HuggingFace finetune. Supports image classification, object detection, semantic / instance / panoptic segmentation, depth estimation, image-text-to-text VLM (SFT / LoRA), and LLM SFT / DPO / GRPO. Six-step workflow: inspect and qualify, hardware and NGC image, research, generate and smoke, train + eval + infer, push and emit rerun skill.