Skill UI

This skill provides expert guidance on creating, reviewing, and managing production-grade AWS infrastructure using Terraform. It focuses on best practices for reusable module design, robust remote state management (S3 + DynamoDB), and adhering to modern HashiCorp Configuration Language (HCL) patterns like using 'for_each'. Ideal for teams building or auditing complex, scalable AWS environments.

A comprehensive, multi-phase workflow designed for Infrastructure as Code (IaC) using Terraform. It guides users through provisioning cloud resources, creating reusable modules, managing complex state, handling multi-environment deployments, and integrating robust CI/CD pipelines and security practices. Ideal for establishing scalable, repeatable, and auditable cloud infrastructure.

Utilizes structured critical reasoning to rigorously stress-test ideas, plans, and decisions. This skill allows you to play devil's advocate, conduct pre-mortems, execute red-teaming exercises, or audit underlying assumptions. It employs multiple advanced modes, including Socratic questioning, Hegelian dialectics, and Falsificationism, to identify blind spots, weaknesses, and unstated assumptions before commitment.

Scans FunC-based TON smart contracts for boolean, Jetton, and gas handling vulnerabilities, reporting critical issues with file references and offering remediation guidance for auditors and secure dApp launches.

Provides a structured reference of the 100 most critical web application vulnerabilities organized by category, helping security teams identify attack vectors, assess impacts, and follow remediation guidance during audits or penetration tests.

A comprehensive checklist designed to ensure TwinMind integrations are deployed securely, stably, and compliantly in a production environment. It covers critical best practices across multiple domains, including authentication, data encryption (PII, GDPR), robust infrastructure setup (auto-scaling, load balancing), advanced monitoring (metrics, tracing, alerting), and full CI/CD deployment workflows. Use this when preparing for go-live or conducting production audits.

This comprehensive guide details the process for managing service transitions on the TwinMind platform. It instructs users on how to audit current usage, export all key configurations (settings, integrations, webhooks), and execute plan upgrades (Pro or Enterprise) via both the dashboard and API. This ensures a seamless and controlled migration between service tiers.

Assesses AI/ML models and datasets for ethical risks, fairness concerns, and bias, generating reports and mitigation guidance via the ai-ethics-validator plugin whenever a user requests an ethics review or bias detection.

This expert skill validates the deployment readiness of Vertex AI Agent Engines. It executes a weighted audit across five critical dimensions: security, monitoring, performance, compliance, and best practices. The output provides a comprehensive 0-100% composite score, a detailed pass/fail report for each check, and a prioritized, actionable remediation plan, ensuring high-quality, production-grade deployments.

Performs pattern-driven searches for similar vulnerabilities across repositories after a known issue is found, guiding analysts through precise generalization, triage, and tool choices (ripgrep, Semgrep, CodeQL) to audit and prioritize true positives.

This guide demonstrates how to implement robust Role-Based Access Control (RBAC) and financial governance for cloud GPU environments. It allows organizations to allocate resources, set team-specific budgets, whitelist approved GPUs, and track spending via detailed audit logs. Ideal for multi-team research and engineering environments requiring strict cost accountability and access separation.

This comprehensive guide outlines security best practices for utilizing Vast.ai's cloud environment. It covers secure API key management, hardening SSH access to GPU instances, implementing proper data cleanup before instance destruction, and adhering to robust network security protocols. Use this when setting up, auditing, or deploying sensitive AI/ML workloads on shared cloud hardware.