By tonone-ai
User researcher — interviews, personas, Jobs-to-Be-Done, and customer feedback synthesis
Feedback synthesis — cluster support tickets, NPS verbatims, app store reviews, and churn surveys by theme, separate signal from noise, and produce an actionable insight report. Use when asked to "synthesize this feedback", "analyze support tickets", "what are users complaining about", "NPS analysis", "churn feedback synthesis", or "what's the feedback telling us".
Run a user interview — produce an interview guide and synthesize the output into an actionable insight report. Use when asked to "run a user interview", "synthesize these interview notes", "what do users actually want", "build a persona from this feedback", "find the JTBD in these transcripts", or "analyze this interview data".
Jobs-to-Be-Done analysis — given a product, user descriptions, transcripts, or tickets, produce a JTBD job map with switching forces analysis and opportunity ranking. Use when asked to "find the JTBD", "what jobs are users hiring us for", "job mapping", "what are users really trying to do", "JTBD framework", or "why are users switching".
User research reconnaissance — survey existing personas, research docs, interview notes, and feedback artifacts to establish what is already known about users. Use when asked to "what research exists", "review existing personas", "what do we know about our users", or before starting new research or synthesis work.
User segmentation and persona creation from mixed data sources — analytics, CRM, support tickets, reviews, or any combination. Use when asked to "build personas", "who are our users", "segment our users", "create user profiles", "define user archetypes", or "who is the target user".
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:
npx claudepluginhub tonone-ai/tonone --plugin echoEngineering + 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
Product team — 8 agents: Helm, Echo, Lumen, Draft, Form, Crest, Pitch, Surge
Use this agent when conducting user research, analyzing user behavior, creating journey maps, or validating design decisions through testing. This agent specializes in understanding user needs, pain points, and behaviors to inform product decisions within rapid development cycles. Examples:\n\n<example>\nContext: Understanding user needs for a new feature
Discovery & research skills: Discovery Interview Guide, Job Story Mapper, User Interview Synthesis, Assumption Mapper. Structure user research from screener to synthesis.
Agent-first PM toolkit with 9 specialist agents and 18 skills for solo developers and small teams
User-centered design and product excellence. Master user research, UX design, accessibility, product strategy, user journey mapping, and inclusive design practices.
Business analysis toolkit: competitive analysis, UX strategy artifacts, market sizing, canvas, PRD, personas