By tonone-ai
Inventory observability tools across services, map metrics, tracing, logging, and alerting coverage, and highlight monitoring blind spots
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub tonone-ai/tonone --plugin vigil-reconVerify 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".
Editorial "Observability & Monitoring" bundle for Claude Code from Agentic Awesome Skills.
Production reliability and observability across all environments. Master Datadog, CloudWatch, monitoring, incident response, SRE practices, and audit logging for enterprise compliance.
Use this agent when you need to implement comprehensive monitoring, observability, and alerting systems for enterprise B2B applications. This agent specializes in APM, logging, metrics, distributed tracing, SLA monitoring, and proactive incident management for business-critical systems. Examples:
Design 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".
Metrics collection, logging infrastructure, distributed tracing, SLO implementation, and monitoring dashboards
Monitoring and alerting configuration with dashboard generation
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