Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add autohandai/community-skills --skill building-vulnerability-dashboard-with-defectdojogit clone --depth 1 https://github.com/autohandai/community-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/autohandai/community-skills/building-vulnerability-dashboard-with-defectdojo)<a href="https://agentmods.dev/skills/autohandai/community-skills/building-vulnerability-dashboard-with-defectdojo"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/building-vulnerability-dashboard-with-defectdojo/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/autohandai/community-skills/building-vulnerability-dashboard-with-defectdojo"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/building-vulnerability-dashboard-with-defectdojo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.01892 |
| Opus 5 | $0.00018 | $0.00946 |
| Sonnet 5 | $0.00007 | $0.00378 |
| Haiku 4.5 | $0.00004 | $0.00189 |
Grade A, and why
building-vulnerability-dashboard-with-defectdojo scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post(f"{DD_URL}/product_types/", headers=HEADERS, json={ This is a copy
91% identical to building-vulnerability-dashboard-with-defectdojo — 31 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building Vulnerability Dashboard with DefectDojo
Overview
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.
Prerequisites
- Docker and Docker Compose
- 4GB+ RAM, 2+ CPU cores, 20GB+ disk
- PostgreSQL 12+ (included in Docker deployment)
- Python 3.9+ for API integration scripts
- Jira instance (optional, for ticket integration)
Deployment
Docker Compose Deployment
# 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
Environment Configuration
# 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
Organizational Structure
Hierarchy
Product Type (Business Unit)
└── Product (Application/Service)
└── Engagement (Assessment/Sprint)
└── Test (Scanner Run)
└── Finding (Individual Vulnerability)
Setup via API
import 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"]
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 228 lines · 37 tokens per session scan A 2510dae74d1e
building-vulnerability-dashboard-with-defectdojo is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 1,892 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 91% identical to building-vulnerability-dashboard-with-defectdojo, differing in 31 lines, and is treated as a copy.
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