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Overview

Connect CodeWolf to Grafana to create custom dashboards and visualizations for your code quality, performance, and security metrics.

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Prerequisites

  • Grafana instance (8.0+)
  • Admin access to Grafana
  • CodeWolf account and API key

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Setup

1

Install CodeWolf data source plugin

In Grafana, navigate to Configuration > Data sources > Add data source.Search for “CodeWolf” and click Install.
If the plugin isn’t available in the marketplace, you can install it manually from the CodeWolf GitHub repository.
2

Configure data source

After installation, configure the CodeWolf data source:
  • Name: CodeWolf
  • URL: https://api.codewolf.ai
  • API Key: Your CodeWolf API key from the dashboard
Click Save & Test to verify the connection.
3

Import dashboards

Import pre-built CodeWolf dashboards:Go to Dashboards > Import and use these dashboard IDs:
  • 15001: Code Quality Overview
  • 15002: Security Metrics
  • 15003: Performance Monitoring
  • 15004: Deployment Tracking

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Available metrics

Query these CodeWolf metrics in Grafana:

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Code quality

  • codewolf_quality_score: Overall code quality score (0-100)
  • codewolf_complexity: Cyclomatic complexity
  • codewolf_duplication: Code duplication percentage
  • codewolf_coverage: Test coverage percentage

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Security

  • codewolf_vulnerabilities_total: Total security vulnerabilities
  • codewolf_vulnerabilities_by_severity: Vulnerabilities grouped by severity
  • codewolf_security_score: Security posture score

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Performance

  • codewolf_build_duration: Build time in seconds
  • codewolf_test_duration: Test execution time
  • codewolf_deployment_frequency: Deployments per day

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Activity

  • codewolf_commits_total: Total commits
  • codewolf_pull_requests: Pull request count
  • codewolf_contributors: Active contributors

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Dashboard examples

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Code quality dashboard

{
  "datasource": "CodeWolf",
  "metric": "codewolf_quality_score",
  "timeRange": "7d",
  "aggregation": "avg",
  "groupBy": ["repository"]
}
Create panels for:
  • Quality score trend over time
  • Repository comparison
  • Top issues by category
  • Coverage vs. complexity scatter plot

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Security dashboard

Monitor security metrics:
  • Vulnerability severity breakdown (pie chart)
  • New vulnerabilities over time (time series)
  • Mean time to remediation (stat panel)
  • Security score by project (bar chart)

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Performance dashboard

Track development velocity:
  • Build duration trend
  • Test execution time
  • Deployment frequency
  • CI/CD pipeline success rate

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Query editor

Use the CodeWolf query editor in Grafana:
Select from available metrics, apply filters, and choose aggregation methods.
Metric: codewolf_quality_score
Filter: repository = "main-app"
Aggregation: avg
Group by: branch

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Alerting

Create Grafana alerts based on CodeWolf metrics:
1

Define alert rule

IF avg(codewolf_quality_score) < 70
FOR 1h
LABELS severity=warning
2

Configure notification channel

Send alerts to Slack, email, PagerDuty, or other channels.
3

Set alert message

Code quality has dropped below 70 for {{ $labels.repository }}
Current value: {{ $value }}

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Variables

Use dashboard variables for dynamic filtering:
  • $repository: Filter by repository
  • $branch: Filter by branch
  • $environment: Filter by environment
  • $time_range: Dynamic time range selection

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Annotations

Display CodeWolf events as Grafana annotations:
  • Deployment markers
  • Release tags
  • Security scan completions
  • Critical alerts
Enable annotations in dashboard settings: 
{
  "datasource": "CodeWolf",
  "type": "events",
  "tags": ["deployment", "release"]
}

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Troubleshooting

  • Verify your API key is correct and active
  • Check that Grafana can reach https://api.codewolf.ai
  • Ensure firewall rules allow outbound HTTPS traffic
  • Confirm your CodeWolf account has API access enabled
  • Check the time range includes data points
  • Verify metric names are spelled correctly
  • Ensure filters aren’t excluding all data
  • Confirm your account has access to the selected repositories
  • Reduce the time range for queries
  • Use aggregation to limit data points
  • Enable query caching in data source settings
  • Consider downsampling high-frequency metrics