Structured collection of n8n workflows for real-world automations: API integrations, data ops, alerting, and orchestration of business-critical processes. Designed to be reusable, modular, and easy to adapt to different stacks.

Lamp is an AI-native orchestration system designed to autonomously translate unstructured product conversations into production-ready software. The platform integrates a multi-agent architecture built around a deterministic orchestrator that coordinates specialized LLM agents—Planner, Coder, Tester, Security, Ops, and Doc each w a responsability

An intelligent DesignOps pipeline bridging the gap between Figma tokens and production infrastructure. Automates UX consistency, accessibility enforcement, and design token synchronization using Generative AI.

A Secure-by-Design, Idempotent Automation Orchestrator for Ops & Finance.

Lifecycle orchestrator Claude Code skill. 7 phases (discovery→design→architecture→implementation→test→release→ops) with schema-validated JSON artifacts + live verification. Delegates implementation to harness skill.

RAG-Ops is a production-style Retrieval-Augmented Generation (RAG) system designed with an Ops-first mindset, combining agentic orchestration, vector databases, and LLMs with observability, retries, throttling, and scalable deployment on AWS.

Auto-Ops is an AI-driven Action Agent that analyzes unstructured business data, predicts operational risks, and automatically generates executable business actions with human approval. Designed to reduce administrative friction for SMEs, Sentinel-Ops integrates a React frontend, a Spring Boot orchestration backend, and Python-based AI services.

Agentic modernization pipeline to migrate legacy codebases to modern platforms. Covers repo inventory, golden tests, C4 mapping, SBOM generation, platform design, automated codemods, quality gates, shadow testing, cutover, hardening, all orchestrated by LLM agents & ops v1.

Orkestra is a multi-tenant internal job orchestration platform designed to execute DAG-based workflows with durability, retries, idempotency, and basic saga-style compensation. The MVP goal is to deliver a reliable execution engine with visibility through a minimal ops console UI.