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Found 1,410 Skills
Build AI agent interfaces with Polpo UI — composable React chat components, CLI tools, and starter templates. Use when the user wants to create a chat app, add chat components, install @polpo-ai/chat, scaffold a Polpo project, configure theming/dark mode, use ChatInput, ChatMessage, ChatSessionList, or any Polpo UI component. Triggers on "polpo ui", "chat UI", "chat component", "@polpo-ai/chat", "@polpo-ai/ui", "create-polpo-app", "chat input", "session list", "agent selector", "chat interface", "polpo chat", "chat widget", "multi-agent".
Select and create the perfect AI voice for your content using ElevenLabs, Qwen3-TTS, and other platforms—matching voice characteristics to brand personality and audience. Use when: Choosing an AI voice for video narration; Creating a consistent brand voice across content; Cloning a voice for scalable production; Comparing voice synthesis platforms; Designing voice characteristics by description
Imports a Claude Design (claude.ai/design) handoff bundle and scaffolds the proposed components into the project. Accepts a bundle URL or local file, parses and validates the schema, deduplicates components against the existing codebase via component-search, then pipes the survivors through the design-to-code pipeline. Writes provenance metadata so future imports can detect drift between design versions. Use after exporting a handoff bundle from claude.ai/design — this is the entry point that turns a design into code.
Create ShinkaEvolve task scaffolds from a target directory and task description, producing `evaluate.py` and `initial.<ext>` (multi-language). Use when asked to set up new ShinkaEvolve tasks, evaluation harnesses, or baseline programs for ShinkaEvolve.
Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.