Skill UI

This tool functions as a post-task epilogue, designed to audit and record the entire research journey. It scans the conversation history at the end of a coding or research session to extract critical research artifacts, including decisions, experiments, dead ends, claims, heuristics, and major pivots. It systematically writes this provenance data into the structured `ara/` directory, ensuring a faithful and auditable trace of how the project truly evolved.

This framework enforces high output quality, intellectual honesty, and continuous improvement across all AppSec agents. It mandates structured processes, including self-critique loops, anti-rationalization guards, and self-reflection quality gates (≥8 scoring). Applicable during code review, SAST, SCA, and threat modeling, it ensures rigorous and verifiable security analysis.

This skill performs comprehensive, feature-level integration validation after all associated development tasks are implemented. It goes beyond checking individual tasks to ensure cross-task consistency, full test suite passage, and complete specification coverage. It validates data flow, architectural adherence, and performs critical security scans for residual issues (e.g., TODOs, hardcoded secrets), providing a holistic GO/NO-GO assessment for deployment. Use this skill as a final quality gate to verify that the completed feature set respects the designed boundaries and dependencies end-...

This skill guides users through building comprehensive Smart City and IoT solutions on the Azure platform. It covers the entire solution lifecycle, from initial high-level requirements and defining city domains (e.g., traffic, energy) to designing robust, scalable architectures. Outputs include detailed reference architectures, security checklists, cost strategies, and phased implementation roadmaps for enterprise deployment.

A reflection-based Dependency Injection (DI) container for Go. It is designed to facilitate the wiring of complex object graphs during application startup. Developers can use `Provide` to register constructors and manage dependencies using structures like `dig.In` and `dig.Out`. It supports named values, value groups, and interfacing, making it ideal for building robust, scalable, and highly testable Go applications and libraries.

Wraps any existing HTML artifact (landing page, dashboard, etc.) with a live sidebar panel of controls. This panel allows users to instantly adjust design variables—such as accent color, typography scale, layout density, and dark mode—by rewriting CSS custom properties in real time. All changes are persisted via localStorage, enabling continuous exploration and variant testing without losing state.

Cavecrew provides a set of specialized subagents (investigator, builder, reviewer) designed for efficient code development delegation. Unlike vanilla agents, Cavecrew compresses output results, drastically reducing the token usage injected back into the main context. This allows users to maintain context over much longer, complex coding sessions, effectively preventing context exhaustion during large-scale refactoring, debugging, or multi-step feature development.

Use this comprehensive skill to generate, manipulate, and export draw.io diagrams. It converts diagrams into native .drawio files and allows exporting to high-quality formats like PNG, SVG, and PDF. Crucially, it embeds the editable XML data, ensuring the resulting diagrams are professional, scalable, and easily integrated into documents.

A code-generation toolkit used for dependency injection in Go. Wire resolves the dependency graph entirely at compile time, emitting plain Go constructor calls without relying on runtime containers or reflection. This approach significantly improves type safety, performance, and error detection by shifting dependency resolution failures to the build phase. Ideal for building robust, scalable backend services in Go.

This comprehensive, zero-trust security auditor scans third-party AI agent skills before installation. It performs a rigorous 6-phase review process, analyzing surface patterns, script integrity, permissions, and supply chain risks. It is designed to detect malicious payloads, credential theft, instruction overrides, and prompt injection attacks, ensuring secure AI development and deployment.

Methodology for red team testing of on-prem Active Directory, hybrid AD/Entra ID, and ADCS. Covers reconnaissance, credential abuse, privilege escalation, lateral movement, persistence, trust abuse, and Defender for Identity evasion.

Offensive cloud security methodology for AWS, Azure, and GCP. Covers identity discovery, credential harvesting, enumeration, privilege escalation, persistence, data exfiltration, lateral movement, serverless abuse, Kubernetes paths, and detection evasion during authorized assessments.