Skill UI

This skill guides users through the entire statistical analysis workflow, covering everything from framing research questions and checking assumptions to running complex models (t-tests, ANOVA, regression, Bayesian methods). It emphasizes calculating effect sizes and generating fully formatted results compatible with APA guidelines, ensuring rigorous and reproducible research reporting.

SymPy is a powerful Python library designed for exact symbolic computation, allowing users to perform advanced mathematical operations without resorting to floating-point approximations. It provides comprehensive tools for symbolic algebra, differentiation, integration, solving complex equations (linear and non-linear), and handling linear algebra. It is essential for scientific computing, physics modeling, and advanced mathematical research, supporting multiple output formats like LaTeX.

PyTorch Geometric (PyG) is the definitive standard library for building Graph Neural Networks (GNNs) atop PyTorch. It provides specialized data structures for handling diverse graph types, including heterogeneous graphs. The library offers over 60 GNN layer implementations (such as GCN, GAT, GraphSAGE), full support for dataset loading (Planetoid, TUDataset, OGB), and necessary data transformations. It is essential for deep learning tasks involving node classification, graph classification, and link prediction.

This skill generates highly focused, evidence-based medical treatment plans in LaTeX/PDF format, suitable for all clinical specialties. It emphasizes brevity (3-4 pages), regulatory compliance (HIPAA), and actionable steps. Ideal for managing chronic diseases, physical rehabilitation, and mental health care, ensuring structured care pathways and incorporating AI-generated scientific schematics for visual clarity.

Access comprehensive LaTeX templates, formatting requirements, and detailed submission guidelines for major academic venues. This skill supports the creation of manuscripts destined for top-tier journals (Nature, Science, Cell), international conferences (NeurIPS, ICML, CVPR), research posters, and major grant proposals (NSF, NIH, DOE). It ensures your work meets the specific formatting standards required by the target publication.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.

This skill generates comprehensive and structured requirements documents for a new specification. It systematically aggregates context from project files, business rules, and technical constraints, ensuring all generated requirements focus on user-observable behavior (WHAT) rather than implementation details (HOW). It follows established standards like EARS format and includes a rigorous review gate to ensure scope clarity and completeness.

A comprehensive guide to implementing secure and compliant data handling within Algolia. This skill demonstrates how to transform source records, strip Personally Identifiable Information (PII), implement attribute filtering, and enforce data access controls. It provides robust workflows for GDPR and CCPA compliance, including data deletion (Right to Erasure) and Data Subject Access Requests (DSAR).

This utility collects comprehensive diagnostic evidence for Algolia support tickets or internal troubleshooting. It gathers index statistics, API key access control lists (ACLs), recent query logs, and network diagnostic information using API endpoints and shell scripting. Use it when diagnosing search performance or unexpected service behavior.

This utility generates a comprehensive JSON debug bundle for troubleshooting and support requests related to the Anima SDK. It collects vital information, including environment details, Figma API connectivity status, and verifies the Anima SDK generation capabilities. Use it when filing support tickets or debugging code generation issues.

A comprehensive tool for managing annotation datasets, supporting full CRUD operations, label management, and advanced export functionality. It specifically handles COCO format export and facilitates uploading datasets to platforms like Kaggle, streamlining the data pipeline for AI model development.

A comprehensive tool designed to collect all diagnostic evidence for troubleshooting failed or problematic Apify Actor runs. This bundle gathers run metadata, detailed logs, dataset samples, usage statistics, and environment information, making it essential for preparing detailed support tickets or performing in-depth debugging.