By tonone-ai
Generate production-ready LLM prompt packages with system prompts, few-shot examples, output schemas, edge cases, and evaluation criteria when asked to engineer or improve prompts.
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npx claudepluginhub tonone-ai/tonone --plugin cortex-promptDesign and build networking infrastructure — VPCs, subnets, DNS, load balancers, firewall rules. Use when asked to "set up networking", "VPC design", "configure DNS", "load balancer setup", "network architecture", or "firewall rules".
Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".
Implement a reusable, accessible, typed component from a design spec. Use when asked to "create a component", "build a widget", "implement this design", or "reusable UI element".
Verify observability posture — audit monitoring coverage, find blind spots, prioritize gaps. Use when asked "is monitoring sufficient", "observability review", "are we covered", or "pre-launch monitoring check".
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".
Build prompt versioning systems — storage, A/B testing, regression tracking, rollback.
Engineering process for solo founders and teams up to 50 engineers. Agents do architecture, code review, QA, and security. You make two decisions per feature.
(forwward) Lean agent skills for building, shipping, strategy, and growth — no context bloat.
Ultra-compressed communication mode. Cuts 65% of output tokens (measured) while keeping full technical accuracy by speaking like a caveman.
Frontend design skill for UI/UX implementation
Memory compression system for Claude Code - persist context across sessions