可审查、可复用的 Agent Skills

Use when defining or implementing Go interfaces, designing abstractions, creating mockable boundaries for testing, or composing types through embedding. Also use when deciding whether to accept an interface or return a concrete type, or using type assertions or type switches, even if the user doesn't explicitly mention interfaces. Does not cover generics-based polymorphism (see go-generics).

Svelte 5 renderer for json-render that turns JSON specs into Svelte component trees. Use when working with @json-render/svelte, building Svelte UIs from JSON, creating component catalogs, or rendering AI-generated specs.

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Upgrade Three.js games from basic/prototype visuals to premium AAA-inspired browser graphics. Combines art-direction critique, procedural model building, technical art, mandatory external asset sourcing decisions, threejs-3d-generator assets, threejs-image-generator concept/texture workflows, scene visual polish, material/texture libraries, world prop kits, shaders, VFX readability, render budgets, LOD/instancing, render pipeline, and visual scorecard gates. For premium games with characters, vehicles, ships, weapons, buildings, signature props, skies, textures, decals, logos, icons, or GUI art, load the relevant generator skills before deciding procedural assets are enough.

飞书多维表格操作。记录 CRUD、字段管理、视图、权限、公式、关联。

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Capture a session learning.

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Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

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Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

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