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investigate

Correlate logs, traces, metrics, and code to diagnose production incidents.

Updated Jun 19, 2026
Search Tags:#root cause analysis#observability#log analysis#APM#production incident
One-click install
npx skills add https://github.com/sameer-thakkar/hackin26-next-deimos --skill investigate-sameer-thakkar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: investigate
Source: https://github.com/sameer-thakkar/hackin26-next-deimos/tree/main/.cursor/skills/investigate-ho
Command: npx skills add https://github.com/sameer-thakkar/hackin26-next-deimos --skill investigate-sameer-thakkar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps you investigate production incidents by correlating logs, traces, metrics, and code to find root cause and recommend mitigation. It is useful for debugging live issues, performing root cause analysis, and handling production anomalies.

Core Features & Use Cases

  • Correlation of Observability Data: Correlate logs, traces, metrics, and code to identify the root cause of production issues.
  • Connector Readiness: Load available observability connectors and verify their setup.
  • Triage: Extract symptoms, services, time windows, identifiers, and suspicions from user input.
  • Strategy: Decide on data sources and query order to investigate the issue.
  • Gather Signals: Execute queries for logs, APM, traces, codebase, and deploy history.
  • Correlate: Build a concrete evidence chain to map user-facing IDs to internal IDs and compare before/after deploy versions.
  • Mitigate First: Advise mitigation steps before full root cause analysis.
  • RCA Report: Generate a structured report detailing contributing factors and actions taken.

Quick Start

Load the investigate skill and provide the symptom, service, time window, identifiers, and suspicion details for the incident you are investigating.

Frequently Asked Questions about investigate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I diagnose production incidents using observability data like logs and traces?▼

To diagnose production incidents, you correlate observability data such as logs, traces, metrics, and code. This approach identifies the root cause of live issues by building an evidence chain that maps user-facing IDs to internal system IDs.

What is the best way to perform root cause analysis for a live production anomaly?▼

Root cause analysis for a production anomaly involves triaging symptoms, querying logs and APM data, and correlating results with deploy history. This process builds a concrete evidence chain to pinpoint contributing factors and recommend mitigation steps.

How do I start investigating a production issue when I only have a symptom and a time window?▼

Start investigating a production issue by providing the symptom, service, time window, and identifiers. The Skill triages this input to extract relevant details, decides on a data query strategy, and gathers signals from available observability connectors.

Do I need to configure connectors before I can investigate production issues?▼

Yes, you need to configure observability connectors before investigating production issues. The Skill loads available connectors and verifies their setup to ensure it can successfully gather signals from logs, traces, metrics, and deploy history.

Can I get immediate mitigation steps for a production incident before completing a full root cause analysis?▼

Yes, you can get immediate mitigation steps before a full root cause analysis. The Skill prioritizes advising mitigation actions to stabilize the production incident while it continues correlating data for the final RCA report.

How does correlating deploy history help with debugging live production issues?▼

Correlating deploy history helps debug live production issues by comparing before and after deploy versions. This comparison identifies if a recent deployment introduced the production anomaly, building an evidence chain to confirm the root cause.

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