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.

Allows users to resume an interrupted or paused autoresearch experiment. It automatically loads the full context, including configuration, strategy files, complete results history, and recent code changes. The system then summarizes the current best metrics and patterns, prompting the user to continue with a single iteration, set up an automated loop, or manually review the results.

This skill implements an autonomous, multi-round review loop for refining complex research or codebases. It iteratively reviews content using specified external backends (e.g., Codex, manual review), implements necessary fixes based on feedback, and re-reviews until the positive assessment criteria are met or the maximum round limit is reached. It manages state persistence and allows for configurable difficulty levels, making it ideal for projects requiring rigorous, self-correcting quality assurance.

This skill provides a systematic cryptographic audit tool designed to review applications for security vulnerabilities. It scans source code and configuration files to detect weak hashing algorithms (MD5, SHA-1), insecure cipher modes (ECB), hardcoded secrets, and deprecated protocols like TLS 1.0. Essential for security assessments, compliance checks, and maintaining secure coding practices.

This sophisticated tool automates the security review of serverless functions across major cloud providers, including AWS Lambda, Azure Functions, and GCP Cloud Functions. It systematically identifies critical misconfigurations such as overly permissive IAM execution roles (wildcard actions), hardcoded secrets in environment variables, and potential lack of runtime protections. Ideal for pre-production auditing and compliance checks.

Get a deep, multi-round, and adversarial critical review of your research work, ideas, or drafts. This skill simulates the perspective of a senior ML reviewer (e.g., NeurIPS/ICML level) using dedicated external backends (Codex or Manual). It is designed to find logical gaps, missing experiments, and narrative weaknesses, providing brutal honesty to elevate your academic contribution.

This comprehensive guide details a step-by-step workflow for reverse engineering malware binaries using Ghidra. It guides users through static and dynamic analysis techniques to study internal logic, C2 communication protocols, cryptographic routines, and obfuscation methods at the assembly and pseudo-C code level. Use this when deep code review is required to extract configuration data, understand exploit mechanisms, or develop detection signatures.

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 workflow designed to manage, audit, and optimize GitHub repositories. This tool automates critical tasks including identifying hardcoded secrets, cleaning up junk files, performing deep documentation reviews, benchmarking against competitors, and validating the final state, transforming messy codebases into production-ready assets.

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.

This skill provides a comprehensive, structured workflow for diagnosing performance bottlenecks in SwiftUI applications. It guides users through a code-first review to detect common issues like invalidation storms, layout thrash, or heavy main-thread work. When code review is insufficient, it provides detailed guidance on collecting runtime evidence using Instruments, enabling precise root cause analysis and targeted remediation strategies.