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Found 37 Skills
Retrieves MLflow traces using CLI or Python API. Use when the user asks to get a trace by ID, find traces, filter traces by status/tags/metadata/execution time, query traces, or debug failed traces. Triggers on "get trace", "search traces", "find failed traces", "filter traces by", "traces slower than", "query MLflow traces".
Text-to-Speech using Doubao (Volcano Engine) API. Use when converting text to natural-sounding speech, generating audio files from text, listing available TTS voices, or synthesizing speech with customizable speed/volume parameters.
Configure notification channels and settings for account alerts and events. This skill provides Python SDK examples.
Trains, validates, tests, and runs prediction for Physical AI Studio policies via the library Lightning stack. Use when running physicalai fit/validate/test/predict, calling physicalai.train.Trainer and Policy APIs from Python, writing or editing YAML configs under library/configs, wiring a model + datamodule + trainer, resuming from a checkpoint, or debugging a training run. Covers ACT, Pi0, Pi0.5, GR00T, and SmolVLA.
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
US options data: chain snapshots, contracts, trades, quotes, greeks, IV, OI. Use when pulling option chains or contract metrics for analysis (e.g. AAPL Jan calls, SPY chain, NVDA IV, weekly puts).
Use for authorized binary analysis in Binary Ninja, including HLIL/MLIL/LLIL inspection, strings/imports/exports, cross-references, types, patch review, Python API automation, and optional Binary Ninja MCP or localhost HTTP integration.
FastAPI Python async framework with Pydantic and automatic OpenAPI. Use for Python APIs.
One-click model liberation toolkit for removing refusal behaviors from LLMs via surgical abliteration techniques
Install cuOpt for Python, C, or as a server (pip, conda, Docker) — system requirements, install commands, and verification. Use when the user wants to install or verify cuOpt for any user-facing interface. For building cuOpt from source or contributing to cuOpt, see cuopt-developer.
Train a computer-vision model with the getitune library (the Geti training library) using its Python API or CLI. Use when a user wants to train, fine-tune, or evaluate a model with `create_engine(...)` and `engine.train()/engine.test()`, run `getitune train`/`getitune test`, pick or override a recipe under `getitune.recipe.<task>`, choose a device (cpu/gpu/xpu/cuda), warm-start from a checkpoint, or debug a training run. Covers classification, detection, instance/semantic segmentation, and keypoint detection.
Stateful LLDB debugging via LLDB Python API