Skill UI

LaminDB is a Python-driven open-source framework for managing biological datasets, ensuring traceable, reproducible, FAIR data through lineage tracking, schema validation, ontology annotation, workflow integration, and lakehouse-style querying.

Detects missing zeroization of secrets and flags compiler-optimized removals with mandatory assembly/IR evidence, helping auditors verify C/C++/Rust cryptographic code through dataflow, CFG, and runtime validation.

Access multi-dimensional cancer genomics data via cBioPortal REST API for mutation, copy-number, expression, and survival insights across TCGA/GENIE studies, enabling target validation and clinical association analyses programmatically with Python clients.

Access DepMap data to fetch CRISPR Chronos dependency scores, drug sensitivity readouts, and gene effect profiles for cancer cell lines, supporting target validation, biomarker discovery, and synthetic-lethality research via API or downloads.

This comprehensive guide facilitates the development of a production-ready Minimum Viable Product (MVP) for a SaaS business. It covers the entire lifecycle, including idea validation, modern tech stack selection (Next.js, Supabase, Stripe), architectural design for multi-tenancy, database schema planning, authentication setup, payment integration, and a detailed pre-launch checklist for technical and marketing readiness.

This skill provides deep technical expertise regarding the DWARF debugging format (v3-v5) and the DWARF standard. It assists in analyzing, parsing, verifying, and generating debug information from compiled binaries. Use cases include answering questions about the DWARF specification, writing code that interacts with DWARF data, and utilizing tools like `dwarfdump` or `readelf` for structural validation and quality metric extraction.

A comprehensive cheat sheet for the Either type in fp-ts, designed for robust error handling and flow control in TypeScript. It details how to use Either to manage synchronous operations that might fail, facilitating type-safe validation, fallible computations, and complex data chaining (e.g., replacing traditional try/catch blocks). Ideal for developers needing a quick, typed reference for functional programming patterns.

Master robust and type-safe patterns for React development using fp-ts. This guide covers managing state with Option, sophisticated form validation using Either (collecting all errors), and handling asynchronous data fetching with TaskEither. It promotes a highly functional, predictable coding style for React 18/19 and Next.js.

Use this skill to perform read-only API calls for exploring, previewing, searching, and filtering structured datasets hosted on Hugging Face. It allows users to validate dataset availability, list splits, paginate large datasets, and retrieve comprehensive metadata and statistics, making it ideal for data validation and content extraction workflows.

Master the Zod library for defining robust, type-safe schemas in TypeScript. This expert covers advanced validation techniques, including primitives, objects, unions, and discriminated unions. It specializes in type inference (`z.infer`), data coercion, complex refinements, and seamless integration with modern frameworks like React Hook Form, Next.js, and tRPC, ensuring consistency across your application state and API inputs.

This skill provides expertise in TanStack Query (React Query), a powerful tool for asynchronous state management in modern React and Next.js applications. It covers advanced patterns like data fetching, cache invalidation, optimistic updates, and handling SSR hydration, ensuring highly performant and reliable data layers.

Intelligent multi-strategy web scraper that selects between static WebFetch, browser automation, curl/bash, and WebSearch paths to extract structured tables, lists, pricing, and other data, with pagination handling, validation confidence, transforms, and CSV/JSON output for monitoring workflows.