Skill UI

A specialized diagnostic skill designed to analyze failing GitHub Actions workflow logs. It identifies the root cause of crashes, such as missing secrets, environment mismatches, or deprecated syntax. The output provides precise YAML or code diffs required to fix the pipeline and optimize CI/CD processes.

This skill provides expert guidance for Site Reliability Engineering (SRE) incident response. It covers rapid problem resolution, modern observability practices, and comprehensive incident management workflows. Use it for assessing severity, establishing command structures, stabilizing systems, investigating root causes using distributed tracing and metrics, communicating with stakeholders, and conducting blameless post-mortems to improve system resilience.

This skill orchestrates multi-agent incident response workflows using modern SRE practices. It guides users through detection, triage, investigation, and resolution phases. It helps classify severity, analyze observability data, implement mitigations, and conduct root cause analysis. Suitable for handling outages, security breaches, and performance issues with structured checklists and best practices.

This workflow leverages AI-assisted debugging tools and observability platforms to systematically diagnose and resolve production issues through multi-agent orchestration. It covers issue analysis, root cause investigation, fix implementation, and verification phases to reduce MTTR and improve system resilience.

A comprehensive set of production-ready runbook templates for managing technical incidents. These guides cover the full incident lifecycle, including detection, triage, mitigation, root cause analysis, and resolution procedures, ensuring systematic and rapid response to system outages and performance degradation.

This workflow guides developers to build and fix existing Laravel applications effectively. It emphasizes root-cause diagnosis, repository-native implementation, regression coverage, and risk-based verification. Suitable for feature implementation, bug fixing, and maintaining architectural conventions within established codebases.

Locate the root cause of a confirmed failure (stack trace, failing assertion, error message, or wrong-value observation) using backward-then-forward semi-formal tracing. Provides a structured report with Primary Fault, Fault Confidence, and minimal remedy.

Guides investigation of data incidents like freshness delays, volume anomalies, schema changes using Monte Carlo MCP tools. Combines observability metadata with direct data querying to find root cause.

Diagnose slow data pipeline jobs, expensive queries and latency regressions using Monte Carlo cross-platform observability, with a tiered approach that bridges jobs to tables and drills into root causes across Airflow, dbt, Databricks and warehouses.

Investigate and resolve data quality alerts using Monte Carlo MCP tools. Performs root cause analysis, assesses impact, discovers remediation tools, and executes fixes or escalates issues with full context.

Enforces a strict 5-phase debugging protocol (Reproduce, Isolate, Root Cause, Fix, Verify) to prevent premature code edits. Ideal for intermittent, regression, or performance bugs where disciplined root-cause analysis is required before applying fixes.

This skill provides a comprehensive guide for writing effective, blameless postmortems. It helps teams conduct post-incident reviews, identify root causes, and prevent recurrence. Ideal for DevOps, engineering, and organizational learning contexts. Includes templates, timelines, and best practices for fostering a blameless culture.