Skill UI

Gradio is a powerful Python library designed for building and sharing machine learning demo applications and user interfaces (UIs). It allows developers to quickly create interactive web prototypes, from simple interfaces to complex chat interfaces, streamlining the process of demonstrating ML models without needing extensive front-end coding knowledge. It supports advanced features like custom layouts and streaming outputs.

Guides creation of persona-targeted VS Code CodeTour .tour files, covering repo exploration, intent inference, step composition, and validation so each narrative walkthrough is anchored to real files and tailored depth.

Generates implementation-ready JSON-LD schema markup for 10 types (e.g., FAQPage, Product, Article). It validates all fields against Google rich result eligibility rules, flags missing data with fix instructions, and provides clean, ready-to-paste script blocks to optimize search engine visibility and achieve rich results.

Prevents silent decimal mismatches across EVM chains by querying runtime decimals, caching per chain/token, and normalizing balances for bots, dashboards, and DeFi tooling.

Practical workflow for finding exploitable, remotely reachable vulnerabilities, prioritizing those that qualify for bounties by confirming user-controlled attack paths, minimal PoCs, and real impact before reporting.

This skill provides a robust system for tracking all code modifications using structured JSON records and enforcing a defined state machine (e.g., planned -> deployed). It is critical for AI coding workflows, ensuring that if a session expires or is abandoned, the next session receives the full context, progress summary, and blockers to resume seamlessly. Ideal for complex, multi-stage development projects and onboarding new codebases.

A comprehensive guide to structuring Flutter applications using a feature-first, layered architecture. It defines clear separation of concerns across UI, Logic (Domain), and Data layers, ensuring maintainability and testability. This approach guides developers on organizing code by feature modules, making it ideal for building complex, scalable, and long-lived applications while remaining agnostic to specific state management solutions.

A comprehensive guide for integrating Firebase Crashlytics into Flutter applications. It teaches how to capture fatal, non-fatal, and isolate errors, customize reports with user identifiers, custom keys, and logs, and manage opt-in data collection to ensure robust crash monitoring in production environments.

Golang performance optimization patterns and methodology; applies when profiling or benchmarks highlight a bottleneck and you need targeted solutions for allocations, CPU, GC tuning, caching, pooling, or hot-path code reviews.

This skill teaches defensive programming techniques for Go, aiming to prevent common runtime bugs, panics, and silent data corruption. It covers critical topics such as nil safety (interfaces, maps, slices), handling slice aliasing via append, numeric overflow prevention, and correct resource management using defer.

Guides Go engineers on using samber/hot for in-memory caches with TTL, loader chains, sharding, and metrics, plus choosing eviction algorithms to fit work-set and stability needs.

A disciplined workflow that forces you to build in thin vertical slices, test, verify, and commit each working increment before moving on, ideal for multi-file features or large refactors.