Skill UI

This guide provides essential security best practices for implementing Langfuse, an LLM observability platform. It covers critical areas such as secure credential management (public vs. secret keys), implementing PII scrubbing techniques before tracing, hardening self-hosted environments, and enforcing the principle of least privilege. Use this guide to ensure your LLM integrations meet strict data privacy standards (e.g., GDPR, HIPAA).

A comprehensive guide for safely migrating monolithic or aging codebases. It details incremental modernization strategies, dependency mapping, and the implementation of the Strangler Fig pattern. Learn how to build safety nets using characterization tests, manage rollouts with feature flags, and achieve zero-downtime refactoring to reduce technical debt without business disruption.

This skill provides comprehensive best practices for managing data within AI agent workflows. It covers data classification (PII, PHI), prompt-level controls, secure memory usage, and isolation techniques. It ensures that agents adhere to global regulations like GDPR, CCPA, and HIPAA when handling sensitive or regulated data.

A comprehensive diagnostic toolkit designed for advanced troubleshooting of Lindy AI agents. This bundle systematically collects environment information (OS, Python, Node.js), tests API connectivity for webhooks and major integrations (Slack, Notion, Gmail), reviews task history, and guides the user in generating a full, structured support ticket for technical support.

A comprehensive guide to optimizing interactions with the Linear API. Learn advanced techniques to improve application performance by eliminating N+1 queries, implementing robust caching strategies for static data (e.g., teams, states), managing cache invalidation via webhooks, and efficiently batching multiple mutations into single GraphQL requests. Essential for building scalable, low-latency SaaS applications.

This guide provides advanced techniques for managing API rate limits (429 errors) and complexity budgets when interacting with the Linear API. It covers reading rate limit headers, implementing exponential backoff with jitter for robust retries, controlling request bursts via token buckets, and optimizing GraphQL queries to minimize complexity scores. Use this when developing reliable integrations that must adhere to strict API quotas.

A comprehensive guide for authorized security assessments, detailing systematic methods to escalate user privileges from low-level access to root control on Linux systems. Techniques covered include comprehensive system enumeration, exploiting kernel vulnerabilities (e.g., dirty pipe), abusing misconfigured sudo permissions (using gtfobins), and exploiting vulnerable SUID binaries.

This skill simulates an expert prompt engineer, specializing in optimizing prompts for Large Language Models (LLMs). It applies advanced techniques such as Constitutional AI, Chain-of-Thought reasoning, and model-specific tuning. Use this tool to transform basic instructions into production-ready, high-performance prompts, significantly boosting accuracy, reducing hallucinations, and optimizing token usage.

Master comprehensive strategies for evaluating Large Language Model (LLM) applications. This skill covers automated metrics (BLEU, ROUGE, BERTScore), structured human evaluation dimensions, and advanced LLM-as-Judge techniques. Use it to systematically measure model performance, compare prompts, detect regressions, and build confidence in production systems.

This skill provides advanced techniques for optimizing throughput and reducing latency when interacting with the Lokalise API. Learn to implement cursor-based pagination for massive key sets, establish robust local caching strategies using project activity timestamps, and utilize efficient batch operations (up to 500 items) for creating or updating keys. Essential for building scalable, high-performance translation pipelines.

This guide demonstrates how to implement robust Role-Based Access Control (RBAC) for MaintainX integrations using TypeScript and Express. It defines multiple user roles (Admin, Manager, Technician, Viewer) with specific, granular permissions across various resources (work orders, assets, locations). The implementation utilizes permission middleware and scoped API keys to ensure that users can only perform actions designated by their assigned role, enforcing security and data integrity at the API level.

This guide provides advanced techniques for optimizing API integration performance with MaintainX. It covers essential concepts such as connection pooling, implementing multi-level caching (L1/L2 using Redis), batch data loading via DataLoaders, efficient cursor-based pagination, and request deduplication. Use this skill when optimizing slow API responses or improving overall system data throughput.