Results for “shap”

Shape or refine the existing bead or caller intent without creating a second planning artifact. Triggers: "plan", "discover and plan", "shape this goal".

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.

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.

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.

Manage Miro boards, sticky notes, and shapes via the Miro API, enabling programmatic creation of collaborative whiteboards.

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.

Interpretabilidade e explicabilidade de modelos usando SHAP (SHapley Additive exPlanations). Use essa skill ao explicar predições de modelos de machine learning, computar importância de features, gerar plots SHAP (waterfall, beeswarm, bar, scatter, force, heatmap), depurar modelos, analisar vieses ou justiça de modelos, comparar modelos ou implementar IA explicável. Funciona com modelos baseados em árvores (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), modelos lineares e qualquer modelo black-box.

学术论文正文写作、结构设计与语言润色总入口。完整起草、续写、扩写、补写或实质性修订各学科的中英文论文正文时，进入paper-write-zh或paper-write-en；只做提纲、结构诊断或重排方案时，进入paper-shape结构模式；只做忠实润色或不新增研究内容的中译英时，进入paper-shape润色模式。研究评价转/doubao-academic-evaluator；独立系统性文献调研转/doubao-literature-research。

Exploit-phase writing — shapes raw material into an article paragraph by paragraph. Use when the user has a raw-material pile and wants the final article written.

Three.js geometry creation - built-in shapes, BufferGeometry, custom geometry, instancing. Use when creating 3D shapes, working with vertices, building custom meshes, or optimizing with instanced rendering.

Performs geospatial vector data analysis with GeoPandas, including reading/writing shapefiles, GeoJSON, GeoPackage, and PostGIS, geometric operations, spatial joins, overlays, coordinate transformations, and map visualization.

Guides the configuration of pfSense firewall rules, NAT policies, VPN tunnels, and traffic shaping to enforce network segmentation and protect network zones.

Extends pandas for geospatial vector data operations including reading/writing shapefiles, GeoJSON, and GeoPackage, performing spatial joins, coordinate transformations, and creating static or interactive maps.

Three shapes get argued about as if one were correct.

Extends pandas for geospatial vector data analysis, including reading/writing shapefiles, GeoJSON, GeoPackage, and PostGIS, performing spatial joins, geometric operations, coordinate transformations, and creating static or interactive maps.

Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fairness, comparing models, or implementing explainable AI across tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model. Part of the AlterLab Academic Skills suite.

Create MCP servers using the C# SDK and .NET project templates, covering scaffolding, tool/prompt/resource implementation, and transport configuration for stdio and HTTP.

Guiding workflows by decomposing complex tasks (Epics) into Atomic Steps under 15 minutes each, with progress tracking and drift prevention. Use when complex decomposition is needed.

Skeleton loading placeholders — gray-shape stand-ins that mirror final content dimensions, use subtle shimmer/pulse animation, and prevent layout shift. Use when replacing generic spinners with content-shaped placeholders, reducing perceived latency on slow routes, or preventing CLS on image-heavy cards.

Turns product ideas into structured specs for AI tools or stakeholder review, with quick feature specs and full project scopes.

>- Production Part Approval Process (PPAP) — verify PPAP submission level, audit all 18 elements, check completeness for customer approval, prepare PSW. Use when a supplier needs to submit parts for approval, when reviewing a PPAP package, or when determining which PPAP level is required. Covers AIAG PPAP 4th edition with Ford, BMW, VW, and Stellantis OEM-specific requirements.

Official GSAP skill for gsap.utils — clamp, mapRange, normalize, interpolate, random, snap, toArray, wrap, pipe. Use when the user asks about gsap.utils, clamp, mapRange, random, snap, toArray, wrap, or helper utilities in GSAP.

Model any API with Swamp, test it, and enrich *Claw with new capabilities — full lifecycle from idea to working.

Ensure C# types are documented with XML comments following best practices for summaries, parameters, returns, and exceptions.

Create ASP.NET Minimal API endpoints with proper types and comprehensive OpenAPI/Swagger documentation.

Calculates CRAP (Change Risk Anti-Patterns) scores for .NET methods by combining cyclomatic complexity with code coverage data from Cobertura XML reports.

Optimize GSAP animations for smooth 60fps by preferring transforms, avoiding layout thrashing, using will-change, batching reads and writes, and following best practices for stagger, quickTo, and ScrollTrigger performance.

Ensure that C# types are documented with XML comments and follow best practices for documentation.

Messaging systems fall into three fundamental shapes, and most bad choices come

Resize a product photo to Shopify's recommended 2048×2048px square format, sharpen, and convert to WebP for fast storefront load times.