Skill UI

This skill orchestrates the entire coding lifecycle as a state machine, ensuring tasks proceed from issue intake through implementation, structured review, deployment, and final acceptance verification. It enforces a rigorous 'Definition of Done' (DoD) proof, moving beyond simple code changes. Ideal for complex feature development requiring structured handoffs and explicit verification evidence.

Coordinate multiple AI agents concurrently via dmux’s tmux pane manager, running Claude Code, Codex, OpenCode and other tools in parallel tracks for research, implementation, testing, review, or multi-role coding tasks.

Automates review of Terraform infrastructure-as-code via Checkov, tfsec, Terrascan, and OPA/Rego policies to find permissive IAM roles, public exposure, missing encryption, and insecure defaults before cloud deployment.

This skill automates the multi-round research review process. It iteratively reviews provided work using advanced AI models (Codex MCP), identifying critical weaknesses, implementing fixes, and re-reviewing until the content achieves a positive assessment or the maximum round limit is reached. Ideal for refining complex academic papers, technical designs, or large codebases that require rigorous, iterative quality assurance.

A step-by-step playbook for spotting and abusing JavaScript prototype pollution in both browsers and Node.js services to trigger DOM XSS, remote code execution, and authorization bypasses during security hunts and code reviews.

Implements GitHub Advanced Security with CodeQL to provide automated static analysis, secret detection, dependency review, and push protection across repositories, enabling enterprise-scale vulnerability monitoring and compliance-aligned controls.

Analyzes PLC firmware to uncover vulnerabilities such as hardcoded credentials, insecure updates, backdoors, memory corruption, and undocumented debug interfaces. Covers extraction from Siemens, Allen-Bradley, and Schneider platforms, static and dynamic inspection, and baseline comparison to detect tampering.

This autonomous skill orchestrates the entire scientific research lifecycle, guiding users from a vague research direction to a polished, review-ready document. The pipeline systematically executes Idea Discovery (Workflow 1), full-scale Implementation, Experiment Deployment (Workflow 2), automated Review Loops, and optional Paper Writing (Workflow 3). It handles everything from preliminary literature review and novelty checks to generating high-quality, reproducible code and final academic drafts.

Vibers provides a human code review workflow for AI-generated GitHub projects; add the collaborator, GitHub Action, and commit guidance to receive spec- and security-focused feedback plus follow-up PR fixes.

A comprehensive 7-stage super workflow designed for advanced GitHub repository management. This tool automates deep audits to detect security secrets, junk files, and code quality issues. It further provides structured PR review capabilities, competitor benchmarking against similar projects, and synthesizes all findings into a prioritized, actionable plan, ensuring your repository is optimized and production-ready.

This skill reviews code differences (diffs) to identify opportunities for improvement in clarity, reusability, efficiency, and overall quality. It employs multiple specialized sub-agents (e.g., Code Reuse, Efficiency, Quality) to audit changes. It can provide high-confidence, behavior-preserving fixes when requested, ensuring the code adheres to best practices without introducing regressions. Best used when refactoring, cleaning up, or auditing a specific code scope.

This expert reviews and rectifies complex Swift Concurrency issues, such as actor isolation violations, Sendable safety breaches, and data races. It applies modern patterns (like `@MainActor` and `actor` usage) to ensure code adheres to Swift 6.2+ standards while minimizing behavior changes. Use it when debugging concurrency warnings or migrating legacy codebases.