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erwinv2k-TKG
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observability

Analyzes logs, metrics, and traces across observability backends using statistics-first methodology.

Updated Mar 26, 2026
Search Tags:#root cause#observability#log analysis#datadog#distributed tracing#incident investigation#splunk
One-click install
npx skills add https://github.com/erwinv2k-TKG/AgentesVSC --skill observability-erwinv2k-tkg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: observability
Source: https://github.com/erwinv2k-TKG/AgentesVSC/tree/main/packs/incidentfox/sre-agent/.claude/skills/observability
Command: npx skills add https://github.com/erwinv2k-TKG/AgentesVSC --skill observability-erwinv2k-tkg

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Investigating production issues by reading raw logs is slow and error-prone. This Skill enforces a statistics-first methodology for analyzing logs, metrics, and traces, so you understand volume, distribution, and trends before sampling individual entries. ## Core Features & Use Cases - Statistics-First Analysis: Start with aggregated volume, error distribution, and trends before sampling specific log entries. - Multi-Backend Support: Query Coralogix, Datadog, Honeycomb, Splunk, Elasticsearch/OpenSearch, and Jaeger through dedicated sub-skills with transparent credential injection. - Structured Reporting: Produce standardized analysis summaries covering time windows, error rates, top failing services, patterns, and root cause hypotheses with confidence levels. - Use Case: When an alert fires for elevated error rates, use this Skill to aggregate error distribution across services, identify the first occurrence, correlate with deployments, and produce a root cause hypothesis. ## Quick Start Analyze the error logs from the last two hours and summarize which services are failing and why.

Frequently Asked Questions about observability

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

FAQPage Schema
How do I analyze logs during an incident investigation?▼

Start with aggregated statistics rather than raw logs: check total volume, error distribution by service, and trends over the time window. Then sample specific entries from error peaks and compare against a baseline period to identify patterns.

What observability backends does this skill support?▼

It supports Coralogix (DataPrime), Datadog (DQL), Honeycomb, Splunk (SPL), Elasticsearch/OpenSearch (Lucene/Query DSL), and Jaeger for distributed tracing. Each backend has a dedicated sub-skill with platform-specific query syntax.

Why should I avoid reading raw logs first when debugging?▼

Raw log sampling without context leads to biased conclusions and missed patterns. Aggregated statistics reveal the true error landscape—volume, affected services, and trends—so you sample strategically from actual error peaks instead of guessing.

Do I need to configure API keys for Datadog or Splunk?▼

No. Credentials are injected automatically by a proxy layer, so API keys will not appear in environment variables. Invoke the backend scripts directly and test connectivity with a simple query rather than checking env vars.

How do I correlate errors across multiple services?▼

Identify temporal patterns and service correlation in the statistics phase, such as Service A errors preceding Service B errors. For distributed tracing, use the Jaeger sub-skill to follow request flows and latency across service boundaries.

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