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Complete guide to the Zen-Ai-Pentest autonomous pentesting framework architecture.
Passed 23 of 23 test cases with the skill, and 5 of 23 without it.
and 19 more in the full report
18 fixed5 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 18 test cases the skill fixed. Same model, same prompt, with and without the skill.
What database software engine and Python ORM toolkit are specified in the project layout for persistent storage?
| Checked | Without skill | With skill |
|---|---|---|
| Names PostgreSQL as the database and SQLAlchemy as the ORM. | Missed | Passed |
The instructions your model reads when the skill loads.
Complete guide to the Zen-Ai-Pentest autonomous pentesting framework architecture.
zen-ai-pentest/
├── agents/ # ReAct AI agents (11 specialized personas)
├── api/ # FastAPI backend + WebSocket
├── core/ # Orchestrator, models, caching
├── tools/ # 72+ integrated security tools
├── risk_engine/ # CVSS, EPSS, false positive detection
├── modules/ # Scanner, exploit modules
├── web_ui/ # React frontend + dashboard
├── database/ # PostgreSQL models (SQLAlchemy)
├── reports/ # PDF/HTML/DOCX generator
├── docker/ # Dockerfiles + compose
├── tests/ # 43,000+ tests (unit, integration, security)
└── mcp/ # MCP servers (8 total)
from agents.react_agent import ReActAgent, ReActAgentConfig
config = ReActAgentConfig(max_iterations=10, use_vm=False)
agent = ReActAgent(config)
result = agent.run(target="example.com", objective="Full security assessment")
| Persona | CLI | Purpose |
|---|---|---|
| Reconnaissance | deep-recon | OSINT, subdomain, port scan |
| Exploitation | deep-exploit | CVE matching, sandboxed exec |
| Analysis | deep-analyze | FP detection, CVSS scoring |
| Reporting | deep-report | PDF/HTML generation |
test cases passed, vs 5 / 23 without
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