Skill UI

This workflow is designed for comprehensive industrial asset and location management within the MaintainX platform. It enables users to establish detailed facility location hierarchies, register complex equipment assets (including serial numbers and models), query asset lists, and initiate maintenance work orders. Use it for managing equipment lifecycles, tracking physical assets, and maintaining structured facility records.

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 comprehensive guide provides step-by-step instructions and complete code examples for setting up and configuring the MaintainX REST API. It covers best practices for generating API keys, setting up secure environment variables, and implementing robust client wrappers in both TypeScript and Python. Use this skill when integrating CMMS data (such as work orders, assets, and locations) into external applications.

A comprehensive guide to setting up a local development loop for building and testing MaintainX integrations. This workflow utilizes TypeScript, hot reloading, mock API servers, and automated unit/integration testing. It provides a sandboxed environment for developers to efficiently develop and debug their services.

A comprehensive guide to implementing event-driven integrations with MaintainX. This skill covers setting up webhooks to capture real-time updates for work orders, asset changes, and maintenance notifications. It includes best practices like HMAC signature verification, idempotent processing to prevent duplicate actions, and setting up local development environments.

Memory provides persistent context across multiple coding sessions. It stores crucial project decisions, user preferences (like preferred package managers or specific libraries), and architectural conventions in a local memory file. This ensures that the AI maintains consistency, recalls past choices automatically, and applies project-specific rules without needing constant reminders, significantly improving workflow continuity and reliability.

Streamline Microsoft Clarity analytics exports inside Claude Code via the Composio integration, fetching heatmaps, session metrics, and engagement data segmented by browser, device, location, source, medium, campaign, channel, or URL over the last 1-3 days.

A comprehensive guide to establishing a fast, reproducible local development lifecycle for Mistral AI integrations. This workflow includes setting up project scaffolding, implementing hot reloading with tsx, performing unit tests with Vitest mocking, and executing live integration tests against the Mistral API. Ideal for setting up reliable AI application development environments.

An expert guide for designing, setting up, and optimizing large-scale monorepositories. Masters advanced build systems like Nx, Turborepo, Bazel, and Lerna to manage complex dependency graphs, implement efficient build caching (local and remote), and optimize slow CI/CD pipelines for multi-project development workflows.

Simulates a highly structured, sequential peer-review process using multiple specialized AI agents (e.g., Skeptic, Constraint Guardian, User Advocate). This skill is designed to rigorously stress-test a design, surface hidden assumptions, identify potential failure modes, and enforce non-functional constraints before implementation. It transforms initial concepts into robust, validated specifications by simulating formal critique and achieving consensus via a mandatory Decision Log.

Guides researchers through nnsight’s tracing and activation APIs so you can inspect or modify PyTorch internals locally or via NDIF-powered remote runs on 70B+ foundation models without local GPUs.

Runs build, test, lint, serve, and other tasks in Nx workspaces via run, run-many, and affected flows, honoring the detected package manager and target metadata while exposing filters, parallelism, and caching controls for CI or local workflows.