Skill UI

A comprehensive guide for implementing robust policy enforcement and security guardrails in HubSpot integrations. This methodology covers secret scanning within CI/CD pipelines to prevent token leaks, utilizes ESLint rules for enforcing modern authentication practices, and leverages TypeScript for compile-time and runtime checks. Use this when setting up secure, scalable, and compliant codebases for HubSpot APIs.

A comprehensive guide detailing security best practices for integrating with Klaviyo. Learn how to manage private and public API keys, implement least-privilege scoping for different environments (Dev, Staging, Prod), and critically, how to verify webhook payloads using HMAC-SHA256 signatures. This guide is essential for developers building robust and secure marketing automation pipelines.

This guide provides a comprehensive reference architecture for building production-ready, enterprise-grade applications on Palantir Foundry. It covers the entire data lifecycle, detailing best practices for the standard data pipeline (Raw > Clean > Model), Ontology design, external API integration patterns, and robust multi-layered security models. Use it when designing, planning, or optimizing complex data infrastructure within Foundry.

This guide outlines comprehensive security best practices for integrating with Palantir Foundry. It covers secure credential storage (using secrets managers like AWS Secrets Manager or Vault), implementing the Principle of Least Privilege via OAuth2 scopes, and establishing robust credential rotation procedures. Use this when securing production environments, setting up new data pipelines, or auditing existing API access controls.

This skill provides a structured guide for setting up CI/CD pipelines to test Persona identity verification integrations. It demonstrates how to use sandbox API credentials, simulate webhooks, and run comprehensive integration tests. It is essential for developers ensuring the reliability and security of KYC/identity services before deployment.

This guide covers essential security best practices for utilizing Together AI's OpenAI-compatible API for inference, fine-tuning, and model deployment. It provides code examples and architectural advice on secure API key management, robust webhook signature verification, input validation using schemas (like Zod), and implementing data redaction pipelines to protect sensitive information in logs and datasets. Essential for building secure AI applications.

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.

A comprehensive CI/CD pipeline for Quarkus applications, designed to verify code quality and deployment readiness. It covers all phases: build, static code analysis (SonarQube, Checkstyle), rigorous functional testing (Unit, Integration, API), security scanning (OWASP, dependency checks), native image compatibility testing, performance load testing (K6), and container image validation (Trivy, Grype).

A comprehensive guide for designing and implementing robust Infrastructure as Code (IaC) using Terraform or OpenTofu. Covers module hierarchy design, best practices for naming conventions, structuring multi-environment pipelines, advanced testing strategies (native tests, Terratest, static analysis), and integrating security scanning (trivy, checkov) into CI/CD workflows.

Dep handles the entire journey from local code to production deployment. This skill automates containerization using Docker and docker-compose, establishes robust CI/CD pipelines (linting, testing, security scanning, deployment), manages environment variables and secrets, and generates Infrastructure as Code (Terraform/Pulumi) configurations. It ensures observability and provides rollback procedures.

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.

Automate security regression testing for Large Language Model (LLM) applications by integrating Promptfoo and DeepTeam into CI/CD pipelines. This skill enforces a critical security gate, automatically testing endpoints against OWASP LLM Top 10, OWASP Agentic threats, and common jailbreaks/prompt injections. It ensures that security patches do not regress over time due to prompt or model changes, providing continuous risk monitoring.