Skill UI

AddressSanitizer accelerates fuzz testing by instrumenting C/C++ and unsafe Rust code to detect buffer overflows, use-after-free, and related memory corruption bugs at runtime, with configurable ASAN_OPTIONS for verbosity, abort behavior, and leak control.

Scans Algorand TEAL/PyTeal smart contracts for 11 platform-specific vulnerabilities such as rekeying, unchecked fees, group manipulation, and missing access controls, then reports findings with severity, suggested fixes, and optional Tealer automation.

This tool analyzes cryptocurrency options flow from major derivatives exchanges (e.g., Deribit). It tracks open interest, calculates put/call ratios, and analyzes implied volatility to identify institutional positioning, gauge market sentiment, and detect significant trading activity (block trades) in BTC and ETH options. It is essential for quantitative traders and market analysts seeking to predict market movements.

This tool analyzes cryptocurrency options flow from major derivatives exchanges (e.g., Deribit). It tracks open interest, calculates put/call ratios, and analyzes implied volatility to identify institutional positioning, gauge market sentiment, and detect significant trading activity (block trades) in BTC and ETH options. It is essential for quantitative traders and market analysts seeking to predict market movements.

This library serves as a low-level exporter for Python applications, enabling the reliable transfer of comprehensive OpenTelemetry telemetry data—including traces, metrics, and logs—to Azure Application Insights. It provides robust configuration options, including connection string setup, Azure AD authentication, custom sampling, and offline storage management, ensuring complete and reliable observability.

This skill automatically collects raw activity data (merged PRs, issues, commits, and optional Slack updates) from repositories. It formats this data using predefined templates, runs quality checks, and prepares a structured changelog draft. It can automatically commit the draft to a new branch and open a Pull Request (PR) for review, making it ideal for generating weekly or monthly release notes.

This comprehensive guide outlines essential, production-grade security best practices for implementing Clerk authentication. It covers securing environment variables, hardening middleware with crucial security headers (like CSP, X-Frame-Options), protecting API routes with granular authorization checks, and verifying webhooks using signature validation (svix). Use this when deploying or auditing an application to ensure maximum security posture.

A comprehensive, phased guide detailing how to migrate your code review process to CodeRabbit. It covers the entire journey, from assessing current tools (like SonarCloud, DeepSource, or manual reviews) and mapping existing rules, through to running a parallel pilot phase and finally achieving full adoption. Ideal for engineering managers or DevOps teams planning a major CI/CD tool transition.

Monitor the effectiveness and adoption of AI code reviews using CodeRabbit. This skill calculates critical metrics including review coverage percentage, time-to-first-review, and comment acceptance rates. It is ideal for building organizational dashboards to maintain high code quality and optimize development workflows.

This comprehensive checklist guides users through the process of elevating CodeRabbit from an optional AI review tool to a mandatory merge gate in a production environment. It covers configuration auditing, technical validation (YAML syntax, review coverage), team onboarding, and setting up branch protection rules to ensure a smooth transition to required status.

A comprehensive guide for administrators and teams on setting up Cursor for enterprise use. It covers organizational plan selection (Pro, Business, Enterprise), advanced features like SSO, SCIM provisioning, and Privacy Mode enforcement. The guide details setting up roles, managing usage analytics, defining shared project rules via Git, and providing a structured onboarding checklist to ensure seamless adoption and secure coding practices across the organization.

This comprehensive dashboard tracks and analyzes usage metrics for AI coding assistants (Cursor) in enterprise environments. It monitors total requests, model consumption, user adoption rates, and associated costs. Managers can use it to optimize resource quotas, control AI spending, and calculate the Return on Investment (ROI) to justify AI tool adoption.