Skill UI

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.

A comprehensive guide and tool for building and configuring complex, multi-step AI agent workflows. Users can define sophisticated automation pipelines by chaining together customizable triggers (e.g., email, webhook), actions (e.g., Slack post, database update), conditional logic, and external knowledge bases. Ideal for automating business processes like customer support triage, data processing, and complex notifications, ensuring deterministic and scalable AI operations.

A comprehensive guide detailing advanced strategies for migrating complex, rule-based automations from platforms like Zapier, Make, n8n, or custom codebases to Lindy AI. It teaches users how to redesign workflows, leveraging AI agents to replace rigid conditional logic with adaptable reasoning, ensuring a smooth and intelligent transition across multiple phases.

Execute comprehensive load, stress, and soak tests to validate API performance, identify scaling bottlenecks, and establish throughput baselines. This tool simulates realistic, high-volume traffic patterns using industry-standard tools like k6 or Artillery, allowing users to define complex scenarios (e.g., e-commerce checkout flows, traffic spikes) and assert performance thresholds (p95 latency, error rates). It helps pinpoint resource limitations in databases, network layers, or compute resources, ensuring the API is ready for production scalability.

Provides comprehensive patterns for data synchronization, ETL processes, and data migration specifically for MaintainX. Use this when needing to keep data consistent between MaintainX and external databases, data warehouses (like BigQuery), or when performing bulk data exports to formats like CSV. It covers incremental syncing, full exports, and data reconciliation checks.

This deep-dive guide provides a comprehensive framework for migrating complex operational data into the MaintainX platform. It systematically guides users through assessing source data quality from legacy CMMS systems (like Maximo), spreadsheets, or custom databases. The process covers essential steps including data assessment, schema mapping, and executing robust ETL processes for work orders and assets, ensuring a smooth re-platforming transition.

Automates designing and implementing table partitioning strategies to keep large databases performant, optimize time-series workloads, and shorten maintenance windows, with guidance on key selection, SQL scripts, and query tuning.

Handles disaster recovery, PITR, and automated failover for production databases via the database-recovery-manager plugin, streamlining backup validation and recovery testing whenever users ask for database recovery support.

This comprehensive skill manages the entire lifecycle of database testing, ensuring data integrity and system reliability. It covers setting up isolated test environments (using Docker or in-memory databases), executing migrations, and implementing robust test isolation strategies (such as transaction rollback). Use cases include verifying CRUD operations, validating referential constraints, performance benchmarking (EXPLAIN ANALYZE), and ensuring backward compatibility across multiple databases (PostgreSQL, MySQL, MongoDB) and ORMs.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

A comprehensive workflow for advanced AI applications using Mistral AI. This guide covers generating text and code embeddings (1024 dimensions), implementing semantic search via cosine similarity, and building robust RAG pipelines. It also demonstrates advanced tool augmentation using function calling, allowing LLMs to interact with external APIs and databases.

Automates Neon serverless Postgres workflows via the Composio MCP, letting you list projects and branches, inspect databases, manage roles, and retrieve connection URIs for pipelines and tooling integrations.