armor-investigate

Name: armor-investigate
Availability: InStock
Author: anomalyarmor

Diagnose root causes of stale data and triggered alerts using lineage and historical monitoring data.

1|Updated Jan 31, 2026

Search Tags:#troubleshooting #root cause analysis #data quality #data lineage #data freshness #alert investigation

Ready to run

npx skills add https://github.com/anomalyarmor/agents --skill armor-investigate

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: armor-investigate
Source: https://github.com/anomalyarmor/agents/tree/main/skills/investigate
Command: npx skills add https://github.com/anomalyarmor/agents --skill armor-investigate

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires anomalyarmor, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you quickly identify the root cause of data issues, such as stale tables or triggered alerts, by analyzing dependencies, historical data, and AI-driven insights.

Core Features & Use Cases

Root Cause Analysis: Pinpoint why data freshness failed or why an alert was triggered.
Dependency Tracing: Understand upstream and downstream impacts using data lineage.
AI-Powered Insights: Leverage AI to explain issues and suggest solutions.
Use Case: When a critical dashboard shows stale data, use this Skill to trace the issue back to the source, identify the failing ETL job, and understand its impact on other tables.

Quick Start

Use the armor-investigate skill to find out why the 'orders' table is stale.

Frequently Asked Questions about armor-investigate

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

FAQPage Schema

How do I find the root cause of stale data in a critical dashboard?▼

Root cause analysis for stale data traces dependencies using data lineage. It integrates historical monitoring data and AI-driven intelligence to pinpoint failing ETL jobs, explain alert triggers, and identify upstream impacts on critical dashboards.

What is the best way to investigate data freshness alerts and pipeline failures?▼

Investigating data freshness alerts is best handled by analyzing historical data and data lineage. AI-driven intelligence explains why alerts were triggered and debugs data pipeline failures by mapping upstream and downstream impacts across the data observability platform.

How does data lineage help with troubleshooting data quality issues?▼

Data lineage helps troubleshooting data quality issues by mapping upstream and downstream dependencies. This traces issues directly to their source, identifies failing assets, and assesses the full impact of data pipeline failures on downstream tables.

Can I use automated issue diagnosis for data observability without manual dependency tracing?▼

Yes, automated issue diagnosis for data observability eliminates manual dependency tracing. It leverages AI-driven intelligence to automatically diagnose stale assets, explain alert triggers, and assess downstream impact using integrated historical monitoring data.

Do I need the anomalyarmor dependency to debug data pipeline failures?▼

Yes, you need the anomalyarmor dependency to debug data pipeline failures. It provides the core environment required to execute the scripts and references that enable AI-driven issue diagnosis and data lineage tracing for stale assets.