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.claude/skills/building-vulnerability-dashboard-with-defectdojo/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-16 | ✗→✓ | ▲ Improved | — | — |
| case-19 | ✗→✓ | ▲ Improved | — | — |
| case-03 | ✗→✓ | ▲ Improved | — | — |
| case-18 | ✗→✓ | ▲ Improved | — | — |
| case-15 | ✗→✓ | ▲ Improved | — | — |
DefectDojo is an open-source application vulnerability management platform that aggregates findings from 200+ security tools, deduplicates results, tracks remediation progress, and provides executive dashboards. It serves as a central hub for vulnerability management, integrating with CI/CD pipelines, Jira for ticketing, and Slack for notifications. DefectDojo supports OWASP-based categorization and provides REST API for automation.
bash# Clone DefectDojo repository git clone https://github.com/DefectDojo/django-DefectDojo.git cd django-DefectDojo # Start with Docker Compose (production mode) ./dc-up-d.sh # Alternative: manual Docker Compose docker compose up -d # Check service status docker compose ps # View initial admin credentials docker compose logs initializer 2>&1 | grep "Admin password" # Access DefectDojo at http://localhost:8080
bash# Key environment variables in docker-compose.yml DD_DATABASE_ENGINE=django.db.backends.postgresql DD_DATABASE_HOST=postgres DD_DATABASE_PORT=5432 DD_DATABASE_NAME=defectdojo DD_DATABASE_USER=defectdojo DD_DATABASE_PASSWORD=<secure_password> DD_ALLOWED_HOSTS=* DD_SECRET_KEY=<random_64_char_key> DD_CREDENTIAL_AES_256_KEY=<random_128_bit_key> DD_SOCIAL_AUTH_GOOGLE_OAUTH2_ENABLED=True
Product Type (Business Unit)
└── Product (Application/Service)
└── Engagement (Assessment/Sprint)
└── Test (Scanner Run)
└── Finding (Individual Vulnerability)pythonimport requests DD_URL = "http://localhost:8080/api/v2" API_KEY = "your_api_key_here" HEADERS = {"Authorization": f"Token {API_KEY}", "Content-Type": "application/json"} # Create Product Type resp = requests.post(f"{DD_URL}/product_types/", headers=HEADERS, json={ "name": "Web Applications", "description": "Customer-facing web application portfolio" }) product_type_id = resp.json()["id"] # Create Product resp = requests.post(f"{DD_URL}/products/", headers=HEADERS, json={ "name": "Customer Portal", "description": "Main customer-facing web application", "prod_type": product_type_id, "sla_configuration": 1, }) product_id = resp.json()["id"] # Create Engagement resp = requests.post(f"{DD_URL}/engagements/", headers=HEADERS, json={ "name": "Q1 2024 Security Assessment", "product": product_id, "target_start": "2024-01-01", "target_end": "2024-03-31", "engagement_type": "CI/CD", "status": "In Progress", }) engagement_id = resp.json()["id"]
bash# Upload Nessus scan results curl -X POST "${DD_URL}/reimport-scan/" \ -H "Authorization: Token ${API_KEY}" \ -F "scan_type=Nessus Scan" \ -F "file=@nessus_report.csv" \ -F "product_name=Customer Portal" \ -F "engagement_name=Q1 2024 Security Assessment" \ -F "auto_create_context=true" \ -F "deduplication_on_engagement=true" # Upload OWASP ZAP results curl -X POST "${DD_URL}/reimport-scan/" \ -H "Authorization: Token ${API_KEY}" \ -F "scan_type=ZAP Scan" \ -F "file=@zap_report.xml" \ -F "product_name=Customer Portal" \ -F "engagement_name=Q1 2024 Security Assessment" \ -F "auto_create_context=true" # Upload Trivy container scan curl -X POST "${DD_URL}/reimport-scan/" \ -H "Authorization: Token ${API_KEY}" \ -F "scan_type=Trivy Scan" \ -F "file=@trivy_results.json" \ -F "product_name=Customer Portal" \ -F "engagement_name=Q1 2024 Security Assessment" \ -F "auto_create_context=true"
| Scanner | Type String | Format | |---------|------------|--------| | Nessus | Nessus Scan | CSV/XML | | OpenVAS | OpenVAS CSV | CSV | | Qualys | Qualys Scan | XML | | OWASP ZAP | ZAP Scan | XML/JSON | | Burp Suite | Burp XML | XML | | Trivy | Trivy Scan | JSON | | Semgrep | Semgrep JSON Report | JSON | | Snyk | Snyk Scan | JSON | | SonarQube | SonarQube Scan | JSON | | Checkov | Checkov Scan | JSON |
yaml# .github/workflows/security-scan.yml name: Security Scan on: [push] jobs: scan: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Run Semgrep run: | pip install semgrep semgrep --config auto --json -o semgrep_results.json . - name: Upload to DefectDojo run: | curl -X POST "${{ secrets.DD_URL }}/api/v2/reimport-scan/" \ -H "Authorization: Token ${{ secrets.DD_API_KEY }}" \ -F "scan_type=Semgrep JSON Report" \ -F "file=@semgrep_results.json" \ -F "product_name=${{ github.event.repository.name }}" \ -F "engagement_name=CI/CD" \ -F "auto_create_context=true"
python# Configure Jira integration in DefectDojo settings jira_config = { "url": "https://company.atlassian.net", "username": "jira-bot@company.com", "password": "jira_api_token", "default_issue_type": "Bug", "critical_mapping_severity": "Blocker", "high_mapping_severity": "Critical", "medium_mapping_severity": "Major", "low_mapping_severity": "Minor", "finding_text": "**Vulnerability**: {{ finding.title }}\n**Severity**: {{ finding.severity }}\n**CVE**: {{ finding.cve }}\n**Description**: {{ finding.description }}", "accepted_mapping_resolution": "Done", "close_status_key": 6, }
python# Get finding counts by severity resp = requests.get(f"{DD_URL}/findings/?limit=0&active=true", headers=HEADERS) findings = resp.json() # Get SLA breach counts resp = requests.get(f"{DD_URL}/findings/?limit=0&active=true&sla_breached=true", headers=HEADERS) # Get product-level metrics resp = requests.get(f"{DD_URL}/products/{product_id}/", headers=HEADERS) product_data = resp.json()
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-08 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-16 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-19 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-06 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-11 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-07 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-21 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-17 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-03 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-12 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-23 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-10 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-18 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-05 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-04 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-02 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-20 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-15 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-09 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-13 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-14 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-22 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +22 percentage points is the difference between those two pass rates over the 23 comparable cases.
The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.
Other measured skills in the registry, with their headline benchmark lift.