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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".
Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.
Auxiliary development skill for Tongda Xin quantitative platform (TdxQuant). Use this skill when users mention "Tongda Xin", "TDX", "TdxQuant", "quantitative trading" or ask about the usage of the `tqcenter` module. It can help with environment setup, API calling, market data acquisition and trading strategy implementation.
Design production-grade REST, GraphQL, gRPC, and Python library APIs with correct schemas, error contracts, auth, and versioning. Use when the user asks to design an API, define endpoints, create an OpenAPI/Swagger spec, design a GraphQL schema, build a gRPC service, model request/response with Pydantic, add pagination, or review API contracts. NOT for building MCP server tools (use mcp-server). NOT for Node.js/Express API routes or backend patterns (use backend-patterns or typescript-development).
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.
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.
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.
FastAPI Python async framework with Pydantic and automatic OpenAPI. Use for Python APIs.
Expert skill for using OpenViking, the open-source context database for AI Agents that manages memory, resources, and skills via a filesystem paradigm.
Solve LP, MILP, QP (beta) with cuOpt Python API — linear/quadratic objectives, integer variables, scheduling, portfolio, least squares.
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).
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.