Skill UI

This skill enforces best practices for managing Go modules, focusing on stability, security, and maintainability. It guides users through essential workflows like dependency addition, safe upgrading (e.g., patch updates), cleanup (go mod tidy), and critical vulnerability scanning (govulncheck). Use this when initializing, updating, or auditing a Go project's dependency tree to ensure supply chain safety.

Guides learners through reconnaissance, scanning, vulnerability analysis, exploitation, and reporting phases of authorized penetration testing, pairing command examples with tool choices and professional deliverables.

Provides quick reference to penetration testing commands for network scanning, exploitation, password cracking, and web testing workflows, helping security professionals run tools like Nmap, Metasploit, Nikto, and SQLMap during assessments.

This comprehensive guide provides detailed procedural steps for configuring and executing CodeQL code scanning. It covers both implementing automated workflows using GitHub Actions (`codeql.yml`) and running scans via the CodeQL CLI. Users will learn how to manage language matrices, set build modes, interpret SARIF outputs, handle monorepo structures, and troubleshoot complex security analysis setups across multiple languages (e.g., Python, Java, C++).

A comprehensive guide detailing how to configure and manage GitHub's secret scanning feature. This skill covers enabling Secret Protection, setting up push protection to block leaked credentials before they reach the repository, defining custom regex patterns, and providing workflows for triaging alerts and resolving blocked pushes (including using git rebase or bypassing protections). Essential for maintaining secure code repositories and complying with security standards.

This guide details securing the CAST AI integration across multiple layers. It covers best practices for API key management (including rotation and secrets storage), implementing least-privilege RBAC using 'kubectl', deploying the Kvisor security agent for runtime scanning, and setting up Kubernetes Network Policies to restrict egress traffic. Use this when hardening production clusters or performing security audits.

A comprehensive guide to setting up CI/CD pipelines for Adobe API integrations. It covers secure OAuth credential management, unit testing, secret pattern scanning, and rigorous integration testing within GitHub Actions. Essential for maintaining high code quality in Adobe-integrated SaaS products.

This tool provides comprehensive AI security scanning using the Tencent AI-Infra-Guard framework. It systematically audits AI infrastructure, scans tools and skills, checks agent security, and performs advanced LLM jailbreak evaluations to identify vulnerabilities and ensure the robustness and reliability of AI systems.

A comprehensive guide and implementation for setting up Continuous Integration and Continuous Deployment (CI/CD) pipelines for dApps powered by Alchemy. It utilizes GitHub Actions to automate critical Web3 workflows, including running Hardhat tests against a mainnet fork, verifying smart contract integrity, and securely deploying to testnets like Sepolia. Features built-in key exposure scanning for robust security.

A comprehensive security tool designed for AI environments (OpenClaw). It functions as an 'antivirus' for AI skills, performing deep audits, scanning skills before installation, and running full security health checks. Its primary goal is to detect vulnerabilities, prevent data leaks, and mitigate privacy risks, ensuring the integrity and safety of the AI workspace.

A comprehensive guide for implementing robust policy enforcement and security guardrails in HubSpot integrations. This methodology covers secret scanning within CI/CD pipelines to prevent token leaks, utilizes ESLint rules for enforcing modern authentication practices, and leverages TypeScript for compile-time and runtime checks. Use this when setting up secure, scalable, and compliant codebases for HubSpot APIs.

This internal agent skill defines a systematic, multi-step procedure for scanning GitHub repositories to gather comprehensive evidence supporting or explaining bug clusters. The process covers searching open issues, inspecting recent commits, analyzing code paths, and checking deployment history. It assigns evidence confidence tiers (Exact, Strong, Moderate, Weak) to provide a deep, actionable understanding of potential software defects.