Skill UI

A comprehensive reference architecture for building robust and scalable supply chain integrations with Flexport's logistics API. It outlines a structured pipeline using Ingest (webhooks/polling), Core (business logic services), and Expose (REST API/Dashboard layers), ensuring reliable data flow, caching, and queueing best practices.

This guide provides comprehensive security standards for integrating with the Flexport API. It covers critical best practices such as webhook signature verification, secure API key rotation, implementing least-privilege access controls, and data redaction for handling sensitive logistics and customs data. Essential for maintaining compliance and preventing data breaches in supply chain operations.

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.

A comprehensive guide detailing advanced security threats in Machine Learning and AI systems. Covers model supply chain attacks (RCE via pickle, poisoning), adversarial examples (FGSM, PGD, physical-world), data privacy breaches, and LLM-specific vulnerabilities. Essential for building robust, secure AI pipelines.

An expert playbook for identifying and simulating dependency confusion attacks in software supply chains. It comprehensively covers how internal package names can be squatted on public registries, leading to malicious code execution via package manager lifecycle hooks (e.g., npm, pip, Maven). Use this guide for authorized red-team exercises and deep vulnerability assessment of build processes.

This skill runs the Bumblebee scanner on macOS/Linux to perform read-only inventory and exposure checks. It detects compromised packages, developer tools, and host configurations against known security advisories. It is primarily used for supply-chain incident response and vulnerability assessment, providing developers with a clear picture of local system exposure.

A comprehensive guide for securing container images and runtime environments. Covers best practices from Dockerfile authoring (minimal base images, multi-stage builds, non-root users) through advanced runtime enforcement (seccomp, AppArmor) and Kubernetes security controls. Essential for production hardening and maintaining supply chain integrity.

This skill provides deep security review for GitHub Actions workflow files. It focuses on the unique Actions threat model—identifying vulnerabilities like script injection via ${{ }} interpolation, privilege escalation from triggers (e.g., pull_request_target), and ensuring least-privilege permissions for CI/CD pipelines. Use it to audit, secure, or harden existing workflows, preventing attacks from untrusted inputs and supply chain risks.

This skill performs a comprehensive audit of project dependencies, identifying critical security vulnerabilities (CVEs), analyzing license compliance against established policies, and detecting outdated or risky packages. It produces an actionable report including an overall dependency health score and a prioritized, time-bound remediation plan, ensuring the software supply chain is secure and legally compliant.

A comprehensive framework for managing third-party risk (TPRM) throughout the entire vendor lifecycle. This tool guides users through inventory, risk tiering, running due diligence questionnaires (SIG, CAIQ), reviewing security evidence (SOC 2, ISO 27001), codifying requirements in contracts, and establishing continuous monitoring protocols. It is essential for maintaining compliance, managing supply chain risk, and securing data handled by external vendors.

This skill provides comprehensive security auditing for Model Context Protocol (MCP) servers and their associated tool metadata. It is designed to detect sophisticated supply chain attacks, including tool poisoning (indirect prompt injection), Server-Side Request Forgery (SSRF), tool shadowing, and unauthenticated exposure. It is essential for securing AI agent stacks before integrating new tools or during CI/CD processes.

This skill provides comprehensive tools to detect data poisoning and model backdooring attacks throughout the machine learning lifecycle. It checks data provenance, detects label-flip anomalies (using Cleanlab), and analyzes model activations and spectra (using ART) to identify malicious triggers or compromised weights, ensuring ML supply chain integrity.