By tonone-ai
Enforce AI safety policies at runtime with input/output filters, PII detection, content moderation, and guardrail gap analysis. Audit guardrail coverage, identify bypass vectors, and red-team AI systems to prevent policy violations.
Audit guardrail coverage — bypass vectors, false positive rates, policy gap analysis, red-team scenarios.
Design guardrail layers — input classifiers, output validators, PII scrubbers, policy rule engines.
Map current AI safety controls — filter inventory, coverage gaps, latency impact, incident history.
Uses power tools
Uses Bash, Write, or Edit tools
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npx claudepluginhub tonone-ai/tonone --plugin guardEngineering + 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.
Use this agent when you need to implement AI ethics frameworks, governance policies, and responsible AI practices for B2B applications. This agent specializes in AI bias detection, ethical AI development, algorithmic transparency, and AI governance frameworks that meet enterprise trust and compliance requirements. Examples:
AI Agent Team Operating System for Claude Code with 155 MCP tools (incl. 40+ ecosystem research tools), 25 agent templates, 14 hooks / 12 lifecycle events. Persistent team management, structured meetings, task wall with pipeline workflows, company loop engine, real-time React dashboard, and Ecosystem Research Platform v2 (progressive 4-stage deep-review funnel: shallow auto-summary → on-demand architecture → debate-based finalist evaluation → reference/integrate marking, with project-customizable thresholds, append-only history snapshots, and Failed self-learning).
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.
AI team orchestration. Give Claude Code an AI team — CTO, Engineer, QA, Designer work together while you watch.
Ultra-compressed communication mode. Cuts 65% of output tokens (measured) while keeping full technical accuracy by speaking like a caveman.
Design a fine-tuning pipeline
Diagnose runtime infrastructure issues — cold starts, timeouts, scaling problems, network failures. Use when asked about "infra is slow", "cold starts", "network issues", "why is this timing out", "scaling problem", "latency spikes", or "service is down".
Write UX copy for a feature, flow, or component
Audit existing model evaluation code
Identify open legal questions and research gaps