Splunk Analyzer

by srsubramanian

tooldata

Automate Splunk queries and analyze results using Chrome DevTools MCP. Use when the user wants to run Splunk searches, export log data, or analyze Splunk results. Triggers on requests like "check error rates", "search Splunk for X", "run a Splunk query", "analyze logs from Splunk", or "find errors in payment-service".

Skill Details

Repository Files

4 files in this skill directory


name: splunk-analyzer description: Automate Splunk queries and analyze results using Chrome DevTools MCP. Use when the user wants to run Splunk searches, export log data, or analyze Splunk results. Triggers on requests like "check error rates", "search Splunk for X", "run a Splunk query", "analyze logs from Splunk", or "find errors in payment-service".

Splunk Analyzer

Automate Splunk searches via browser and analyze exported results.

Configuration

SPLUNK_URL: https://your-splunk-instance.com

Workflow

1. Navigate to Splunk

Navigate to: {SPLUNK_URL}/en-US/app/search/search

If login page appears, inform user: "Please authenticate in the browser. Let me know when you're logged in."

2. Build SPL Query

Convert natural language to SPL. See references/spl-patterns.md for patterns.

Query structure:

index=<index> sourcetype=<sourcetype> <filters> | <transformations>

If user provides raw SPL, use it directly.

3. Execute Search

See references/splunk-ui.md for UI selectors.

  1. Find search bar (textarea with data-test="search-bar" or class ace_text-input)
  2. Clear existing text, enter SPL query
  3. Click search button (button with data-test="search-button" or "Search" text)
  4. Wait for results (watch for "X events" or results table)

4. Export Results

  1. Click "Export" button above results
  2. Select "Raw" format
  3. Set filename, click "Export"
  4. Wait for download to complete

5. Analyze Results

Run analysis script on exported file:

python3 scripts/analyze_splunk.py <exported_file> [--charts]

Analysis includes:

  • Event count and time range
  • Top error patterns / log levels
  • Field value distributions
  • Anomaly detection (spikes, unusual values)
  • Trend visualization (with --charts)

Quick Reference

User Request Action
"Check errors in service X" index=* "error" source="*X*" | stats count by message
"Show me logs from last hour" index=* earliest=-1h
"Find slow requests" index=* duration>1000 | stats avg(duration) by endpoint
"Summarize today's exceptions" Run query + full analysis with charts

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Skill Information

Category:Technical
Last Updated:12/30/2025