Skill UI

Automated guidance for data normalization tool tasks in ML training, offering best practices, production-ready code/configuration generation, and output validation.

Provides a practical checklist and principles for designing database schemas, indexing strategies, query optimization, and migration planning for both SQL and NoSQL systems, plus guidance on spotting and fixing N+1 issues and denormalization trade-offs.

A comprehensive, expert-level skill for designing, analyzing, and optimizing robust database schemas. It covers normalization, index tuning, choosing between SQL/NoSQL, and implementing zero-downtime data migrations. Provides systematic workflows for schema analysis, performance modeling, and advanced SQL pattern generation.

This comprehensive skill covers the full scope of Firebase services, including Authentication, Firestore, Cloud Functions, and Hosting. It goes beyond basic implementation, focusing on critical architectural principles: adopting a query-first, denormalized NoSQL mindset. Key insights include mandatory security rule design, optimal data modeling for performance, and correctly utilizing modular SDKs to build scalable and secure backends.

Expert guidance on designing highly scalable, distributed NoSQL databases, specifically covering Apache Cassandra and Amazon DynamoDB. This skill emphasizes the 'Query-First' modeling approach, teaching best practices like partitioning, denormalization, single-table design, and avoiding common anti-patterns (e.g., hot partitions, joins) to ensure optimal read/write performance at massive scale.

OpenRouter provides a unified API gateway to access leading AI models (OpenAI, Anthropic, Google, Meta, etc.) from a single endpoint. This skill is essential for developers who need to compare model performance (latency, tokens) across different providers, build resilient cross-provider fallback workflows, or manage complex AI pipelines without writing provider-specific code. It supports feature normalization and BYOK (Bring Your Own Key) for high-volume production use.

Histolab streamlines whole-slide microscopy by automating tissue detection, tile extraction, and stain normalization for H&E scans, making it ideal for lightweight dataset prep and quick tile-based analysis.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analyses, covering QC, normalization, dimensionality reduction, clustering, marker discovery, annotation, and publication-ready visualization across h5ad/10X/CSV datasets.

This skill provides a comprehensive framework for ingesting, normalizing, and enriching threat intelligence (CTI) from multiple structured and unstructured sources. It guides users through scoring feeds, converting heterogeneous IOC formats (like OpenIOC, YARA) into the standardized STIX 2.1 format, and enriching indicators using external services (e.g., VirusTotal, PassiveTotal). Ideal for building robust, automated threat detection pipelines for SIEM and EDR systems.

This pipeline provides an automated solution for processing raw Indicators of Compromise (IOCs) from various sources, including IPs, domains, URLs, and emails. It performs crucial steps such as normalization, deduplication, and defanging (modifying indicators to prevent accidental execution while preserving readability). The final, processed data is converted into the standardized STIX 2.1 format and can be securely distributed via TAXII servers or MISP instances, streamlining threat intelligence sharing.

This skill guides users through performing sophisticated web cache deception attacks. It exploits path normalization discrepancies and cache key misconfigurations between Content Delivery Networks (CDNs) and origin servers. Use this methodology to test for sensitive data exposure, cache poisoning, and authentication bypass vulnerabilities in applications protected by aggressive caching layers like Cloudflare or Akamai.

This utility ensures the prevention of silent floating-point mismatch bugs across all EVM chains. It covers runtime querying of decimals, chain-aware caching, handling precision drift in bridged tokens, and safe normalization for bots, dashboards, and DeFi tools. It is crucial for accurate cross-chain balance comparison.