Senior ML Engineer with 10+ years building scalable systems and ML infrastructure across startups and enterprise.
I build production-grade tools that make AI work reliably in real codebases, not demos.
The nv: skills collection. Production AI methodology, distilled into one-command installable Claude Code skills.
Live experience: skills.nichevlabs.com · production case studies, the research library, and the full synthesis.
| Skill | What it does |
|---|---|
| nv:context | Context engineering for AI coding agents. 200+ research sources, 8 distilled laws, validated on 3 production repos. Case studies · Research · Synthesis |
| nv:design | Vibe Design methodology. Uses source code as design reference instead of prompts. Built skills.nichevlabs.com end to end. |
| nv:dev | Plan, test, debug. The development workflow loop for AI coding agents. |
| nv:ops | Guardrails, evaluation, multi-agent orchestration. |
npx skills add johnnichev/nv-context -g -yProduction-ready Python framework for AI agents with built-in guardrails, audit logging, and cost tracking. Powers the NichevLabs orchestration layer behind multiple SaaS products.
- 5 LLM providers · 146 models
- 4,612 tests · unit, integration, regression, E2E with real APIs
- Tool calling, prompt injection defense, execution traces, sessions, memory
- Hybrid BM25 + vector search, semantic chunking, cross-encoder reranking
- Property-based testing (Hypothesis), thread-safety smoke suite, production simulations
- AI agent infrastructure · tool calling, guardrails, prompt injection defense, execution traces, multi-provider orchestration
- RAG and search · hybrid BM25 + vector search, semantic chunking, cross-encoder reranking
- Full-stack engineering · React, Next.js, Node.js, TypeScript, Python, FastAPI
- Data and ML pipelines · PyTorch, recommendation engines, analytics pipelines
- Cloud and infrastructure · AWS, GCP, Docker, Kubernetes
For engineers who ship.