By tonone-ai
ML/AI engineer — model training, MLOps, feature engineering, LLM integration
Evaluate model performance — check for accuracy drops, data drift, and error patterns. Use when asked about "model accuracy dropped", "evaluate the model", "check for drift", or "model performance".
Design and implement an AI feature integration — model selection, architecture pattern, system prompt, data flow, error handling, cost estimate. Use when asked to "add AI to this", "LLM integration", "add Claude/GPT", or "AI-powered feature".
Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to "build ML model", "train a model", "prediction pipeline", "classification", or "regression".
Build a production-ready prompt package — system prompt, few-shot examples, output format, edge case handling, eval criteria. Use when asked to "prompt engineering", "build a prompt", "write a system prompt", or "improve this prompt".
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".
Uses power tools
Uses Bash, Write, or Edit tools
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Engineering + product, second to none.
Your elite AI team as Claude Code agents. 2 leads + 21 specialists. 125 skills. Every major engineering and product discipline covered.
Simple by default. Scalable by design.
Right now, everyone gets a generalized AI assistant. Engineers, product managers, designers, strategists — all prompting separately, getting separate outputs, then copying results into Slack threads for the next person to feed back into AI. It's a relay race where every handoff loses context.
That's the wrong unit of automation. Instead of giving each person an AI assistant, give the whole company an AI team. Specialists that talk to each other, share context, and run the show end to end — from user research to infrastructure to deployment — without the copy-paste relay.
That's Tonone. Not twenty-three copies of the same generalist. Twenty-three specialists, each owning one domain, coordinated by leads who know when to call who and at what depth.
Complexity is debt. Every unnecessary abstraction, every over-engineered solution, every "just in case" feature — it all accrues interest. It slows you down today and buries you tomorrow.
Scalability compounds. When you build simple, correct foundations, they carry more weight over time without breaking. Simple systems are easier to debug, easier to extend, and easier to hand off.
No boilerplate generators. No tutorial-grade scaffolds. Production-ready output that respects your codebase, your stack, and your time.
| Agent | Hat | What They Do |
|---|---|---|
| Apex | Engineering Lead | Orchestrates the team, scopes work, controls depth and budget |
| Forge | Infrastructure | Cloud services, networking, IaC, cost optimization |
| Relay | DevOps | CI/CD, deployments, GitOps, developer experience |
| Spine | Backend | APIs, system design, performance, distributed systems |
| Flux | Data | Databases, migrations, pipelines, data modeling |
| Warden | Security | IAM, secrets, compliance, threat modeling |
| Vigil | Observability + Reliability | Monitoring, alerting, SRE, incident response, SLOs |
| Prism | Frontend/DX | UI, internal tools, developer portals |
| Cortex | ML/AI | Model training, MLOps, feature engineering, LLM integration |
| Touch | Mobile | Native iOS/Android, cross-platform, app stores |
| Volt | Embedded/IoT | Firmware, microcontrollers, edge computing, protocols |
| Atlas | Knowledge Engineering | Architecture docs, ADRs, API specs, system diagrams |
| Lens | Data Analytics & BI | Dashboards, metrics design, reporting, data storytelling |
| Proof | QA & Testing | Test strategy, E2E suites, integration testing, flaky triage |
| Pave | Platform Engineering | Developer experience, golden paths, service catalogs |
| Agent | Hat | What They Do |
|---|---|---|
| Helm | Head of Product | Orchestrates the product team, writes briefs, hands off to Apex |
| Echo | User Research | User interviews, personas, Jobs-to-Be-Done, feedback synthesis |
| Lumen | Product Analytics | Metrics frameworks, funnel analysis, OKRs, A/B test design |
| Draft | UX Design | User flows, information architecture, wireframes |
| Form | Visual Design | Brand identity, color systems, typography, design system |
| Crest | Product Strategy | Roadmap planning, prioritization, competitive analysis |
| Pitch | Product Marketing | Positioning, messaging, value prop, GTM, launch copy |
| Surge | Growth | Acquisition channels, activation funnels, retention playbooks |
Prerequisites: Claude Code v1.0+
/plugin marketplace add tonone-ai/tonone
/plugin install tonone@tonone-ai
Then just talk to them:
Engineering + Product + Operations + Legal + Design + Data Science + Security Operations + Developer Experience + Infrastructure Specialist + AI Operations team — 100 agents as Claude Code specialists. Infrastructure, DevOps, backend, security, ML/AI, mobile, UX, analytics, growth, revenue, content, PR, customer success, finance, people, operations, support, contracts, compliance, IP, governance, regulatory, color systems, typography, motion, accessibility, design tokens, forecasting, feature engineering, model training, drift monitoring, vector search, LLM fine-tuning, pen testing, detection engineering, incident response, zero trust, API docs, SDK design, developer onboarding, Kubernetes, Terraform, FinOps, service mesh, edge computing, caching, queuing, multi-cloud, chaos engineering, model deployment, LLM evaluation, AI observability, guardrails, prompt engineering, embeddings, ranking, and more.
UX designer — user flows, information architecture, wireframes, and interaction design
Backend engineer — APIs, system design, performance, distributed systems
Platform engineer — developer experience, service catalogs, internal CLIs, golden paths, environment management
Growth engineer — acquisition channels, activation funnels, retention playbooks, and PLG strategy
npx claudepluginhub tonone-ai/tonone --plugin cortexEngineering team — 15 agents: Apex, Forge, Relay, Spine, Flux, Warden, Vigil, Prism, Cortex, Touch, Volt, Atlas, Lens, Proof, Pave
Use this agent when implementing AI/ML features, integrating language models, building recommendation systems, or adding intelligent automation to applications. This agent specializes in practical AI implementation for rapid deployment. Examples:\n\n<example>\nContext: Adding AI features to an app\nuser: "We need AI-powered content recommendations"\nassistant: "I'll implement a smart recommendation engine. Let me use the ai-engineer agent to build an ML pipeline that learns from user behavior."\n<commentary>\nRecommendation systems require careful ML implementation and continuous learning capabilities.\n</commentary>\n</example>\n\n<example>\nContext: Integrating language models\nuser: "Add an AI chatbot to help users navigate our app"\nassistant: "I'll integrate a conversational AI assistant. Let me use the ai-engineer agent to implement proper prompt engineering and response handling."\n<commentary>\nLLM integration requires expertise in prompt design, token management, and response streaming.\n</commentary>\n</example>\n\n<example>\nContext: Implementing computer vision features\nuser: "Users should be able to search products by taking a photo"\nassistant: "I'll implement visual search using computer vision. Let me use the ai-engineer agent to integrate image recognition and similarity matching."\n<commentary>\nComputer vision features require efficient processing and accurate model selection.\n</commentary>\n</example>
The team-architecture factory for Claude Code — a meta-skill that turns a domain description into an agent team and the skills they use, with six pre-defined team-architecture patterns (Pipeline, Fan-out/Fan-in, Expert Pool, Producer-Reviewer, Supervisor, Hierarchical Delegation). Claude Code용 팀 아키텍처 팩토리: 도메인 한 문장을 에이전트 팀과 스킬 세트로 변환하는 메타 스킬.
maenifold-enabled product team
Ultra-compressed communication mode. Cuts ~75% of tokens while keeping full technical accuracy by speaking like a caveman.
Comprehensive UI/UX design plugin for mobile (iOS, Android, React Native) and web applications with design systems, accessibility, and modern patterns