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PLG motion design — free tier definition, activation sequence, expansion trigger points, viral mechanic assessment. Given a product, output the PLG architecture and make the calls. Use when asked to "PLG strategy", "freemium model", "product-led growth plan", "self-serve motion", "how do we add a free tier", "upgrade triggers", or "viral loop design".
Growth state reconnaissance — scan existing onboarding flows, acquisition channels, conversion funnels, and growth experiment logs to understand current growth state. Use when asked to "what's our growth state", "audit the funnel", "what growth experiments have we run", "acquisition channel inventory", or before designing new growth experiments.
Retention diagnosis + intervention plan — analyze the retention curve, identify the primary drop-off point, and produce a specific intervention plan with expected impact. Use when asked to "improve retention", "why are users churning", "build a retention playbook", "reduce churn", "win-back campaign", or "users aren't coming back".
Privacy & ToS Drafter — GDPR-compliant privacy policies, ToS, cookie policies, DPAs
Infrastructure Specialist Team — Terra: Terraform and IaC — module design, state management, drift detection, and IaC best practices
AI Operations Team — Token: Context window optimization, token counting, truncation strategies, and chunking patterns for LLM efficiency.
Design token budgets — system/user/assistant allocation, overflow handling, context compression.
Design chunking strategies — semantic splitting, overlap tuning, retrieval-aware chunk sizing.
Audit token usage patterns — avg context size, waste, truncation frequency, budget adherence.
Design Token Engineer — Design token engineering — token architecture, theming systems, style-dictionary pipelines
First-run onboarding tour — guided walkthrough of tonone's 23 agents, key skills, and worktree sessions. Two paths: expert (~90 sec) and newcomer (~8 min). Use when asked "how do I use tonone", "what can tonone do", "show me around", or "first steps".
Mobile audit — app size, startup time, crash reporting, store compliance, accessibility, offline behavior. Use when asked for "mobile review", "app store readiness", "mobile performance", or "crash analysis".
Produce a mobile feature spec — user story, technical approach, component breakdown, platform-specific considerations, edge cases. Use when asked to "add a screen", "spec this feature", "mobile feature", "new tab", "push notifications", or "deep link".
Mobile reconnaissance — understand the app's tech stack, architecture, dependencies, and health for takeover. Use when asked to "understand this app", "mobile assessment", or "app health".
Set up mobile release pipeline — Fastlane, code signing, CI, beta distribution, versioning. Use when asked about "app store setup", "release pipeline", "fastlane", "beta distribution", or "signing".
AI Operations Team — Trace: LLM tracing, span capture, prompt/completion logging, cost attribution, and AI system debugging.
Debug AI system behavior using traces — prompt reconstruction, output comparison, failure attribution.
Instrument LLM calls with tracing — span structure, token counts, latency, model metadata.
Audit LLM observability coverage — trace gaps, logging completeness, cost attribution accuracy.
LLM Fine-tuning Engineer — LLM fine-tuning — PEFT/LoRA, RLHF, instruction tuning, prompt optimization