Skill UI

A structured runbook for handling Apollo.io API outages and production failures. It guides users through severity classification (P1-P4), quick diagnosis scripting, implementing circuit breakers, and graceful degradation strategies to maintain service availability during incidents.

A comprehensive runbook for diagnosing and resolving production incidents involving the Clay SaaS platform. It provides structured triage steps, severity definitions (P1-P4), and resolution guides for issues like credit exhaustion, webhook failures, and data flow outages, ensuring minimal downtime.

A comprehensive guide for engineering teams to manage critical incidents related to Clerk authentication and SaaS platform outages. It covers full incident lifecycle procedures, including initial triage, emergency auth bypass implementation, key rotation, session recovery scripts, and post-incident root cause analysis. Use this when facing production authentication failures or security breaches.

A comprehensive runbook for handling CodeRabbit service outages or malfunctions. It provides step-by-step procedures for quick triage, emergency bypasses (P1), checking installation health (P2), diagnosing delays (P3), and tuning incorrect review behavior (P4). Essential for maintaining continuous CI/CD workflows when the review bot fails.

A comprehensive toolkit for advanced debugging and systematic incident response within Customer.io. It provides structured playbooks, API debug clients, and investigation scripts to diagnose complex delivery issues, analyze user profiles, or troubleshoot campaign failures, ensuring robust incident management.

A comprehensive runbook designed for on-call engineers to manage Databricks incidents, outages, and major job failures. It provides structured procedures for immediate triage, decision tree guidance (covering cluster failures, code errors, and data quality issues), and steps for evidence collection and postmortem documentation, ensuring rapid and systematic incident resolution.

This skill provides a structured methodology for tackling complex bugs, performance bottlenecks, and production incidents. It guides users through reproducing issues, forming hypotheses, performing controlled experiments, and narrowing down the scope using proven strategies and tools. Ideal for deep dive troubleshooting and root cause analysis in complex software systems.

A comprehensive runbook for standardizing incident response to Deepgram production issues. It guides users through automated triage (checking status, API connectivity, and model performance), severity classification (SEV1-SEV4), and implementing mitigation strategies, including building fallback services and replaying failed requests to ensure business continuity during outages or degradation.

This skill defines a senior engineer specializing in the entire software development lifecycle (SDLC). It covers building robust CI/CD pipelines (e.g., GitHub Actions), containerizing applications with Docker, and managing infrastructure using Infrastructure as Code (IaC) tools like Terraform or Pulumi. Key areas include Kubernetes deployment (GitOps), cloud platform configuration (AWS/GCP/Azure), release automation, and incident response planning.

A comprehensive guide for managing Documenso integration incidents. It provides step-by-step procedures for diagnosing and resolving production outages, covering cloud service disruptions, self-hosted database errors, and webhook failures. Essential for on-call engineers to maintain system uptime.

This skill provides expert capabilities for diagnosing and resolving complex system failures in distributed environments. It is designed for investigating production incidents, performing deep root-cause analysis using observability tools (logging, tracing), and proposing robust, preventive fixes. Use it to significantly enhance system stability and reliability.

This skill provides expert-level diagnostic capabilities for complex distributed systems. It specializes in analyzing production incidents, identifying deep root causes, and designing comprehensive observability solutions. Use this when investigating recurring errors, reviewing system logs/traces, and implementing preventative reliability measures.