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Found 1,210 Skills
Provides exact patterns for diagnosing and fixing automatic batching regressions in React 18 class components. Use this skill whenever a class component has multiple setState calls in an async method, inside setTimeout, inside a Promise .then() or .catch(), or in a native event handler. Use it before writing any flushSync call - the decision tree here prevents unnecessary flushSync overuse. Also use this skill when fixing test failures caused by intermediate state assertions that break after React 18 upgrade.
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment setup, training-loop or other code authoring/scaffolding, contributor/CI/packaging questions, repo-specific questions in physicsnemo-sym/-cfd/-curator, or general (non-physics) ML/PyTorch.
Design and operate reconciliation processes that ensure data accuracy across portfolio management custodian and clearing systems. Use when building or evaluating a daily position cash or transaction reconciliation process, investigating discrepancies between internal systems and custodian records, diagnosing recurring break patterns especially from corporate actions or pricing differences, setting tolerance thresholds for position cash or market value matching, implementing three-way reconciliation across advisor system custodian and clearing firm, designing break investigation workflows with aging and escalation, normalizing data across multi-custodian feeds from Schwab Fidelity or Pershing, reconciling cost basis tax lots or accrued income across systems, evaluating reconciliation platforms like Arcesium Duco or Advent Geneva, or preparing for regulatory examinations on books and records accuracy.
Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples.
Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.
Deep linter reference for authoring or debugging a vigiles enforce() rule — plugin tables, AST selectors, type-aware rules, auto-fix, and edge cases for ESLint, Ruff, Pylint, RuboCop, and Stylelint. Use when you need the exact rule name or config for a specific linter, not for running a linter.
Provides comprehensive uni-app component and API integration guidance. Use when the user needs official uni-app components or APIs, wants per-component or per-API examples, or needs cross-platform compatibility details.
Exclusive skill set for the GoFrame development framework. Provides a complete framework usage guide for Go language developers, covering best practices for core components such as command-line management, configuration management, logging components, error handling, data validation, type conversion, cache management, template engines, database ORM, and I18n internationalization. Includes project engineering structure specifications, development model guidelines, solutions to common problems, and rich practical code examples. Suitable for building various Go projects such as RESTful APIs, gRPC microservices, and web applications, helping developers quickly master the features of the GoFrame framework and improve development efficiency and code quality.
Code simplification skill for improving clarity, consistency, and maintainability while preserving exact behavior. Use when simplifying code, reducing complexity, cleaning up recent changes, applying refactoring patterns, or improving readability. Triggers on tasks involving code cleanup, simplification, refactoring, or readability improvements.
Provides exact Enzyme → React Testing Library migration patterns for React 18 upgrades. Use this skill whenever Enzyme tests need to be rewritten - shallow, mount, wrapper.find(), wrapper.simulate(), wrapper.prop(), wrapper.state(), wrapper.instance(), Enzyme configure/Adapter calls, or any test file that imports from enzyme. This skill covers the full API mapping and the philosophy shift from implementation testing to behavior testing. Always read this skill before rewriting Enzyme tests - do not translate Enzyme APIs 1:1, that produces brittle RTL tests.
Count Korean text deterministically with exact grapheme, line, and byte contracts for self-intros and form limits.
Generate comprehensive Vitest tests for code examples in JavaScript concept documentation pages, following project conventions and referencing source lines