Skill UI

Integrate Langfuse into CI/CD pipelines (e.g., GitHub Actions) to automate the testing and validation of Large Language Models (LLMs). This guide covers prompt regression testing, tracing validation, and setting up experiment-driven quality gates, ensuring reliable and high-performing AI applications throughout the development lifecycle.

A comprehensive solution to integrate Linear project management with GitHub Actions CI/CD pipelines. This skill enables automating development workflows by running integration tests, automatically linking Pull Requests (PRs) to issues, transitioning issue states based on PR events, and creating new Linear issues directly from build failures. Ideal for ensuring development quality and maintaining up-to-date project tracking.

This skill enforces mandatory code quality assurance by executing industry-standard validation tools before any code is considered complete or committed. It covers linting (style and syntax checking), type checking (for TypeScript and Python), and security auditing (e.g., npm audit, bandit). It ensures developers maintain a high standard of code reliability across diverse ecosystems like Node.js and Python.

This skill guides the design and implementation of a complete, production-grade Machine Learning pipeline. It employs a multi-agent orchestration workflow, systematically covering all stages of the ML lifecycle: data ingestion and quality assurance, feature engineering, model training, optimization, and robust deployment (MLOps). It ensures built-in reproducibility, monitoring, and scalability.

This comprehensive guide provides templates for integrating MaintainX API testing into automated CI/CD pipelines. It covers multiple stages, including unit tests (mocked), live integration tests against staging environments, and security scans (like gitleaks). Use this when setting up robust, automated quality gates for your MaintainX SaaS integrations.

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.

Automate the validation and testing of prompts and AI responses within your CI/CD pipeline using Mistral AI. This integration provides robust prompt regression testing, model quality checks, automatic cost estimation for API usage, and deployment gates, ensuring the stability and reliability of your AI features upon every pull request.

This comprehensive checklist guides the safe and robust deployment of Mistral AI integrations to a production environment. It covers critical operational steps, including secure credential management, rigorous code quality gates, establishing health endpoints, implementing circuit breakers for resilience, executing gradual rollouts, and defining emergency rollback procedures to ensure system stability and high availability.

NeMo Curator streams GPU-accelerated curation for LLM training, with super-fast fuzzy deduplication, multimodal quality filters, semantic dedupl, PII redaction, NSFW checks, and RAPIDS-scaled pipelines for prepping massive corpora.

Automates the Continuous Integration and Continuous Deployment (CI/CD) process for OpenEvidence clinical decision support integrations. This setup ensures that unit tests (using mocked evidence queries and citations) and live API connectivity tests are run automatically on every pull request and merge to main. It validates critical functions like query formatting, evidence parsing, citation extraction, and response quality scoring, maintaining high reliability for medical AI applications.

Optimizes prompts for LLMs to cut token usage, lower costs, and boost response quality by analyzing, condensing, and rewriting instructions; useful when teams need faster, cheaper, clearer AI conversations.

A sophisticated AI tool that coordinates multiple specialized agents (e.g., Security, Architecture, Performance) to provide comprehensive, multi-perspective code assessments. It goes beyond single-point reviews by managing context, running hybrid execution workflows, resolving conflicts, and synthesizing unified, actionable reports for high-quality code assurance.