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A comprehensive Java SDK client for securely and efficiently sending custom application logs to Azure Monitor. It utilizes Data Collection Rules (DCR) and Data Collection Endpoints (DCE) to manage the ingestion process. The client supports batch uploads, concurrent processing, and robust error handling, making it ideal for integrating custom logging into Azure Log Analytics workspaces.

A comprehensive guide and set of patterns for optimizing build processes using Bazel in large-scale monorepositories. Covers configuring WORKSPACE, managing remote caching/execution, defining custom rules (Python, TypeScript), and best practices for maintaining build performance and consistency across massive codebases.

This skill executes CodeRabbit's core workflow, providing automated, in-depth code reviews directly on pull requests. It analyzes code diffs based on custom rules (e.g., API validation, DB security), generates detailed walkthrough summaries, and posts line-level feedback. Ideal for setting up robust CI/CD quality gates and maintaining high code standards throughout the development lifecycle.

This skill guides the comprehensive, multi-step rollout of CodeRabbit AI code review across an entire organization. It covers setting organization-level defaults, customizing team-specific review rules (e.g., API security, frontend performance), and automating deployment via multi-repo scripts and branch protection enforcement. Use when establishing standardized, high-quality AI code review practices company-wide.

Provides comprehensive guidance for setting up and configuring Static Application Security Testing (SAST) tools, including Semgrep, SonarQube, and CodeQL. Learn how to integrate security scanning into CI/CD pipelines, define custom security rules, enforce quality gates, and manage compliance policies to proactively identify and remediate code vulnerabilities across multiple programming languages.

Guides you through building custom Semgrep rules with a testing-first workflow, AST analysis, and taint-mode preference to catch vulnerabilities or bug patterns while steering clear of overly broad or unverified shortcuts.

Implement comprehensive guardrails for Vercel applications, covering the entire development lifecycle. This solution includes custom ESLint rules to prevent secret exposure, pre-commit hooks for credential scanning, and robust CI checks to validate vercel.json, ensure edge runtime compatibility, and monitor bundle size, guaranteeing high quality and secure deployments.

This expert guides the optimization and design of the entire revenue engine, connecting marketing, sales, and customer success. It provides frameworks for lead lifecycle management, defining MQL-to-SQL handoffs, implementing comprehensive lead scoring models (fit and engagement), and structuring lead routing rules. Use this when optimizing cross-functional processes and systems.

Guides building custom Odoo modules by scaffolding manifests, models, views, and security rules, reviewing existing code, and applying ORM best practices across Community and Enterprise editions.

This skill provides a comprehensive guide for creating production-quality custom Semgrep rules. It teaches best practices for detecting security vulnerabilities, enforcing robust coding standards, and identifying specific bug patterns. By emphasizing a test-first approach and utilizing advanced techniques like taint mode, users can write highly accurate static analysis rules that drastically reduce false positives.

This guide provides a comprehensive walkthrough for deploying, configuring, and tuning Snort 3. It covers essential steps such as setting network interfaces to promiscuous mode, writing custom rules using Lua, integrating with SIEM platforms, and utilizing community rulesets. Ideal for network security professionals implementing robust, signature-based intrusion detection at critical network boundaries.

This skill guides SOC analysts and detection engineers on how to correlate complex, multi-stage attacks within the IBM QRadar SIEM platform. It covers advanced usage of AQL (Ariel Query Language) for deep event investigation, building custom correlation rules (Building Blocks and Offenses), and cross-source data correlation (e.g., auth failures with network flows). It is essential for reducing false positives and improving threat detection accuracy.