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Found 272 Skills
Configures API gateways for routing, authentication, rate limiting, and request transformation in microservice architectures. Use when setting up Kong, Nginx, AWS API Gateway, or Traefik for centralized API management.
API gateway patterns and implementations. Kong, AWS API Gateway, NGINX as gateway, rate limiting, request routing, authentication offloading, and request/response transformation. USE WHEN: user mentions "API gateway", "Kong", "AWS API Gateway", "NGINX gateway", "gateway pattern", "request routing", "BFF" DO NOT USE FOR: reverse proxy basics - use infrastructure skills; service mesh - use `service-mesh`; rate limiting in app - use `rate-limiting`
Professional DOCX document creation and editing using OpenXML SDK. Useful for branded reports, polished proposals, and template-based authoring.
Migrate Kotlin (and Java) code from kotlinx.collections.immutable 0.3.x / 0.4.x to the latest 0.5.x. The 0.5.x line renames every copy-returning method on PersistentList / PersistentMap / PersistentSet / PersistentCollection to a participial form per KEEP-0459 (add→adding, removeAt→removingAt, set→replacingAt, put→putting, clear→cleared, …) and deprecates the old names (WARNING, with ReplaceWith). Driven by the compiler: bump the version, recompile, and apply the rename each deprecation warning names. Use when the user mentions kotlinx.collections.immutable 0.5.x, PersistentList migration, "Use adding() instead", KEEP-0459, or sees deprecation warnings from kotlinx.collections.immutable.
Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches. Use when creating new synthetic voices, fine-tuning existing Piper checkpoints, preparing audio datasets for TTS training, or deploying voice models to devices like Raspberry Pi or Home Assistant. Covers dataset preparation, Whisper-based validation, training configuration, and ONNX export.
Ktor server patterns including routing DSL, plugins, authentication, Koin DI, kotlinx.serialization, WebSockets, and testApplication testing.
.NET solution and project structure conventions. Covers .slnx format, Directory.Build.props, Directory.Packages.props for central package management, global usings, and naming conventions. Load this skill when setting up a new solution, adding projects, configuring build properties, or when the user mentions "solution structure", ".slnx", "Directory.Build.props", "central package management", "Directory.Packages.props", "global usings", ".editorconfig", "project layout", or "naming conventions".
Comprehensive tmux skill covering process management, session/window orchestration, and ricing (visual customization). Use when managing tmux sessions, running dev servers, setting up floating panes, configuring status bars, installing plugins via TPM, or when the user asks about tmux, tmux-sessionx, tmux-floax, catppuccin tmux theme, or making tmux look good.
Configure nginx and Traefik as reverse proxies. Implement SSL termination and routing. Use when setting up application gateways.
Export a trained getitune model (the Geti training library) to a deployable format. Use when a user wants to run `engine.export(...)` or `getitune export`, choose between OpenVINO IR and ONNX, set FP32 vs FP16 precision with `ExportFormat` / `Precision`, or understand where exported artifacts are written and how they load back for inference. Covers the export/load contract between training and OpenVINO/ONNX inference.
Loads and validates policies exported from Physical AI Studio for Runtime deployment. Use when working on InferenceModel, InferenceModel.from_pretrained, manifest.json, adapter auto-detection (onnx, openvino), backend/device kwargs, Hugging Face Hub policy packages, or the Runtime side of the export/load contract that Studio produces with physicalai export.
Run inference and evaluation with a getitune model (the Geti training library). Use when a user wants to call `engine.predict()` / `engine.test()` or `getitune predict` / `getitune test`, run inference with a PyTorch checkpoint versus an exported OpenVINO IR (`.xml`) or ONNX (`.onnx`) model, or understand how `OVEngine` loads deployed models via ModelAPI. Covers PyTorch, OpenVINO, and ONNX inference backends.