Skill UI

This skill guides users on fine-tuning CodeRabbit's review quality. It teaches the AI agent team preferences through PR feedback, integrates custom code guidelines (e.g., security policies), allows customization of the review tone, and implements path filters to drastically reduce false positives and noise. This ensures that all code feedback is highly relevant, actionable, and aligns with specific team standards.

Guides writing high-quality YARA-X detection rules for malware hunting, covering naming conventions, string selection, performance tuning, migrations from legacy YARA, and reducing false positives when authoring, reviewing, or debugging signatures.

Systematically verifies suspected security bugs to eliminate false positives and document evidence, guiding each finding through the standard or deep verification path and producing TRUE POSITIVE or FALSE POSITIVE verdicts.

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.

A comprehensive guide to configuring ModSecurity WAF with OWASP Core Rule Set (CRS). It covers establishing robust web application logging, tuning rules to minimize false positives, and implementing custom SecRules for advanced threat detection. Essential for security audits, compliance, and architectural hardening.

This skill guides developers through integrating Static Application Security Testing (SAST) tools like CodeQL and Semgrep into GitHub Actions CI/CD pipelines. It covers automating code scanning on pull requests and pushes, tuning rules to reduce false positives, uploading SARIF results, and establishing quality gates to prevent vulnerable code from merging into production.

This tool guides the process of reviewing security findings from DAST and SAST scanners. It uses the OWASP risk rating methodology to classify vulnerabilities (true positive, false positive, needs review) and calculate a comprehensive risk score based on likelihood and impact, ensuring development teams prioritize the most critical issues.

Analyzes Terraform plan JSON from the AzureRM provider to filter false-positive Set-type attribute diffs when reviewing changes to gateways, load balancers, NSGs, and similar resources.

This skill guides the deployment of decoy files, or "canary tokens," across critical file systems. These files act as tripwires, monitoring for unauthorized changes (modification, deletion, renaming). By tracking these high-value decoys, systems can detect ransomware encryption activity in real-time, providing a low false-positive, high-fidelity detection layer that complements traditional EDR and backup solutions.

This skill guides the full lifecycle of threat intelligence processing, transforming raw indicators of compromise (IOCs) from MISP feeds into actionable security detections. It covers feed curation, false positive reduction using warninglists, and automating the generation of high-fidelity rules for Suricata, Sigma, and Wazuh. Use this to mature your MISP instance from a repository into a proactive detection engine.

This skill is designed to mitigate false-positive safety triggers from strict AI classifiers (e.g., cyber, bio/chem, reasoning). It operates by making minimal, surgical edits to sensitive phrases, abstracting away specific domains or actions while preserving the original benign intent and structure. It ensures the prompt remains policy-compliant without losing its core purpose, making it ideal for academic or defensive inquiries.