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Found 1,779 Skills
Generates .NET/C# SDKs from OpenAPI specifications using the AutoSDK CLI tool (autosdk.cli). Use when the user wants to create a C# client library from an OpenAPI/Swagger spec, scaffold a new SDK project, regenerate existing SDK code, or customize generated .NET API clients.
Embedded CAN/CAN-FD debugging tool for interface scanning, message monitoring, test frame transmission, log recording, database file decoding, and bus statistics. Automatically triggered when users mention CAN, CAN-FD, DBC decoding, bus packet capture, USB-CAN joint debugging, message transmission, bus statistics, PCAN, Vector, slcan, CAN interface scanning, CAN ID filtering, ASC logs, BLF files. Also compatible with explicit invocation via /can. Even if users only say "check CAN messages", "send a test frame" or "decode DBC", this skill should be triggered as long as the context involves CAN bus communication.
Write, scaffold, and debug Go CLI applications with `github.com/spf13/cobra`. Use this skill whenever the user mentions Cobra, `cobra.Command`, a Go command-line app, subcommands, persistent or local flags, required flags, argument validation, shell completions, generated docs, or wants to build or refactor a cobra-based CLI.
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".
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.
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.
Run GPU workloads on Modal — training, fine-tuning, inference, batch processing. Zero-config serverless: no SSH, no Docker, auto scale-to-zero. Use when user says "modal run", "modal training", "modal inference", "deploy to modal", "need a GPU", "run on modal", "serverless GPU", or needs remote GPU compute.
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.