Skill UI

This comprehensive guide helps users build, audit, and debug robust analytics tracking systems using GA4 and Google Tag Manager. It focuses on establishing a consistent event taxonomy and capturing every meaningful user action—from initial visits to complex conversions—ensuring data integrity for accurate decision-making.

Semgrep is an open-source static analysis tool used to find bugs, detect vulnerabilities, and enforce coding standards. This guide covers writing custom rules in YAML format to implement advanced SAST checks, such as detecting SQL injection, hardcoded secrets, or tracking tainted user input. It is essential for building robust DevSecOps pipelines and ensuring compliance.

Trackio is an essential library for logging, visualizing, and analyzing machine learning training experiments. It provides multi-interface support: a Python API for real-time metric logging and alert firing during training; a Command Line Interface (CLI) for retrieving historical metrics and alerts; and synchronization capabilities to Hugging Face Spaces for persistent dashboards. This tool is crucial for debugging, diagnosing training issues (like loss spikes), and automating ML workflow iteration.

This skill enables users to create, update, and manage GitHub issues via MCP tools or CLI. It supports bug reports, feature requests, and task tracking. Users can add labels, assignees, milestones, and link issues. It handles issue types and dependencies, streamlining project management.

A comprehensive methodology for pinpointing the root cause of complex bugs, regressions, or unexpected behavior in large codebases. Techniques include binary search debugging, Git bisect for tracking regressions across commits, strategic data/control flow logging, component isolation, and advanced breakpoint strategies (conditional breakpoints, watch expressions). Ideal for scenarios where the bug source is unclear or difficult to reproduce.

This guide provides a systematic approach to deep debugging complex HubSpot API issues. It includes layer-by-layer network testing (DNS, TCP, TLS), correlation ID tracking, and timing analysis to isolate intermittent or hard-to-reproduce errors. Use this when standard troubleshooting fails or when compiling detailed evidence for technical support.

This skill provides a comprehensive observability stack for Flexport logistics integrations. It sets up Prometheus metrics for API health tracking, uses structured logging (pino) for debugging, implements OpenTelemetry tracing for latency analysis, and defines actionable Grafana dashboards and alert rules. This ensures reliable, performant, and fully monitored logistics operations.

Provides comprehensive observability for Miro REST API v2 integrations. It captures critical metrics using Prometheus (request rates, latency, error tracking) and OpenTelemetry traces for end-to-end request flow visibility. Includes structured logging for detailed debugging and robust rate limit management, ensuring your applications are highly reliable and maintainable.

A comprehensive methodology for discovering potential software vulnerabilities. It outlines advanced techniques including static analysis, dynamic analysis, fuzzing, taint tracking, and modern attack surface mapping (e.g., kernel, drivers). Ideal for professional code audits, bug bounty hunting, and building robust vulnerability research pipelines.

This skill provides implementation patterns for integrating with the Ramp Developer API using OAuth2 authentication. It is designed for debugging and automating workflows related to corporate card management, expense tracking, and accounting synchronization within a fintech environment. Use it when building solutions that interact with Ramp's financial data.

This skill applies a rigorous, evidence-first discipline to coding tasks, inspired by Fable-like best practices. It guides agents through a structured workflow: inspecting the workspace, tracking goals and findings, grounding all conclusions in verifiable evidence, and executing thorough verification gates before declaring completion. Ideal for complex development, debugging, or adapting cross-model prompt guidance into robust, verifiable project rules.

This skill applies a rigorous, evidence-first discipline to coding tasks, inspired by Fable-like best practices. It guides agents through a structured workflow: inspecting the workspace, tracking goals and findings, grounding all conclusions in verifiable evidence, and executing thorough verification gates before declaring completion. Ideal for complex development, debugging, or adapting cross-model prompt guidance into robust, verifiable project rules.