Skill UI

A comprehensive checklist for ensuring LangChain applications are robust, secure, and scalable for production environments. It covers critical areas including configuration management, advanced error handling, observability setup (e.g., LangSmith), performance optimization, security best practices (like prompt injection prevention and PII handling), rigorous testing, and zero-downtime deployment strategies. Use this guide when preparing for a major launch or auditing existing LLM systems.

This comprehensive guide outlines essential security best practices for implementing and using Lindy AI agents. It covers crucial topics such as robust API key management, webhook signature verification, strict agent permission scoping, and secure data handling protocols. Use this resource when auditing agent activity, configuring enterprise security features (SSO/SCIM), or ensuring compliance and minimizing the attack surface area of autonomous AI systems.

Provides a structured workflow for defining agent-agnostic subagents with isolated context, covering purpose definition, metadata configuration, prompt writing, and common specialist patterns for verification, debugging, security auditing, and review.

Framework for auditing and optimizing GEO content for each AI search platform, scoring Google AI Overviews, ChatGPT, Perplexity, Gemini, and Bing Copilot with tailored checklists and action steps.

Amazon Macie is a fully managed AWS service utilizing advanced machine learning and pattern matching to discover, classify, and protect sensitive data (PII, financial records, credentials) stored in Amazon S3. It provides deep visibility into your cloud data, enabling comprehensive Data Loss Prevention (DLP) and compliance auditing by identifying specific sensitive data types through automated and targeted discovery jobs.

Create cross-platform agent skills from workflow descriptions. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, spec validation, and security auditing of third-party skills before install. Triggers on requests to automate repetitive workflows or create custom agents.

AKF is the native metadata format specifically designed for tracking AI-generated or AI-modified content. It allows users to stamp files (including DOCX, PDF, images, and code) with detailed trust scores, source provenance, and compliance metadata. This tool is essential for professional workflows requiring strict auditing, compliance assurance, and verifiable data lineage, especially concerning regulations like the EU AI Act, HIPAA, and SOX.

Plans, audits, and safely runs ChicagoHAI HypoGeniC/HypoRefine workflows for LLM-assisted hypothesis generation from labeled text datasets, with pinned installs, config validation, dataset leakage checks, and bounded local review before any model call.

A comprehensive security auditing framework designed to systematically detect sensitive information disclosure from Large Language Models (LLMs). It uses multi-phase, escalating dialogue probes to test for leaks such as system prompts, API keys, PII, and internal configurations. Essential for red teaming and rigorous model security assessment.

This skill audits Model Context Protocol (MCP) servers to detect critical AI security vulnerabilities. It scans for tool poisoning, tool shadowing, rug pulls, SSRF, and unauthenticated exposure. Use it before integrating new tools or during security reviews to ensure agent reliability and prevent supply chain attacks.

This skill guides the entire lifecycle of repeatable AI agent loops. Use it to discover recurring work patterns within codebases, find established solutions in a library, or interview users to craft bounded, effective loops. It supports auditing, repairing, running, and preparing AI workflows for publication, treating the loop as a robust feedback system.

Manages the creation and orchestration of complex, multi-step workflows. Supports two powerful methods: persistent MCP templates for human-gated, resumable pipelines, or native JavaScript scripts for advanced, programmatic agent fan-out. Ideal for complex business logic such as auditing, research, or multi-stage data processing.