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Get Started Free →Complete end-to-end pentesting workflow using the Zen-Ai-Pentest platform.
.claude/skills/shadd0wtaka-zen-pentesting-workflow-skill/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 96% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 1% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -33% | 0% |
Complete end-to-end pentesting workflow using the Zen-Ai-Pentest platform.
bash# 1. Start all services docker compose up -d # or: ./scripts/start-zen.sh # 2. Run full audit via CLI deep-audit --target example.com --phases recon,scan,exploit,report # 3. View dashboard at http://localhost:8501 # 4. Export report: deep-report --scan-id wf-abc123 --format pdf
bashdeep-recon --target example.com --output recon.json # What happens: # - Subdomain enumeration (subfinder, amass, dnsx) # - Port scanning (nmap, masscan) # - Technology fingerprinting (whatweb, wappalyzer) # - Screenshot (gowitness, aquatone) # - OSINT (shodan, censys integration)
bashdeep-audit --target example.com --phases scan # Tools engaged: # - nuclei (community + custom templates) # - web vulnerability (sqlmap, nikto, zap) # - network (netexec, crackmapexec) # - cloud (scoutsuite, prowler)
bashdeep-audit --target example.com --phases exploit # Metasploit: auto-match CVEs to modules # Custom payload generation (msfvenom) # Credential testing via netexec
bashdeep-report --scan-id YOUR-ID --format pdf deep-report --scan-id YOUR-ID --format html
bash#!/usr/bin/env bash # custom-audit.sh set -euo pipefail TARGET="$1" WORKSPACE="./workspace/${TARGET}" mkdir -p "$WORKSPACE" echo "=== Phase 1: Network Discovery ===" nmap -sV -sC -oA "$WORKSPACE/nmap" "$TARGET" echo "=== Phase 2: Web Recon ===" gobuster dir -u "https://$TARGET" -w wordlist.txt -o "$WORKSPACE/gobuster.txt" echo "=== Phase 3: Vulnerability Scan ===" nuclei -u "https://$TARGET" -o "$WORKSPACE/nuclei.txt" echo "=== Phase 4: AI Analysis ===" python -c " from agents.react_agent import ReActAgent agent = ReActAgent(max_iterations=5) print(agent.run(target='$TARGET', objective='Analyze findings in $WORKSPACE')) "
┌──────────────────────────────────────────────────┐
│ Zen Network │
│ ┌──────────┐ ┌──────────────┐ ┌───────────┐ │
│ │ Terminal │ │ WhatsApp Bot │ │ OmniRoute │ │
│ │ :23000 │ │ :23001 │ │ :20128 │ │
│ └──────────┘ └──────────────┘ └───────────┘ │
│ ┌──────────┐ ┌──────────────┐ ┌───────────┐ │
│ │ Hermes │ │ VPN Proxy │ │ WG Proxy │ │
│ │ :23002 │ │ :23003 │ │ :23004 │ │
│ └──────────┘ └──────────────┘ └───────────┘ │
└──────────────────────────────────────────────────┘agent_coordinator.create_workflow(target, phases=["recon"])| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→fail | 5,718 | 8,145 | +42% | 1 | 1 | 0% | 564 | 1,548 | +174% | 0 | 0 | — |
case-01 | fail→fail | 25,343 | 10,845 | -57% | 1 | 1 | 0% | 2,306 | 2,216 | -4% | 0 | 0 | — |
case-02 | fail→fail | 30,335 | 12,042 | -60% | 1 | 1 | 0% | 1,639 | 2,147 | +31% | 0 | 0 | — |
case-04 | fail→pass | 8,963 | 2,617 | -71% | 1 | 1 | 0% | 1,471 | 1,501 | +2% | 0 | 0 | — |
case-05 | fail→pass | 18,764 | 21,659 | +15% | 1 | 1 | 0% | 1,040 | 2,036 | +96% | 0 | 0 | — |
case-06 | fail→pass | 17,865 | 7,034 | -61% | 1 | 1 | 0% | 1,368 | 1,376 | +1% | 0 | 0 | — |
case-07 | fail→pass | 5,079 | 5,666 | +12% | 1 | 1 | 0% | 899 | 1,252 | +39% | 0 | 0 | — |
case-08 | pass→pass | 9,207 | 3,474 | -62% | 1 | 1 | 0% | 1,888 | 1,635 | -13% | 0 | 0 | — |
case-09 | pass→pass | 5,752 | 2,152 | -63% | 1 | 1 | 0% | 1,087 | 1,368 | +26% | 0 | 0 | — |
case-10 | pass→pass | 6,616 | 2,083 | -69% | 1 | 1 | 0% | 902 | 1,347 | +49% | 0 | 0 | — |
case-11 | fail→pass | 15,644 | 5,485 | -65% | 1 | 1 | 0% | 2,956 | 1,985 | -33% | 0 | 0 | — |
case-12 | fail→pass | 10,443 | 1,949 | -81% | 1 | 1 | 0% | 1,713 | 1,210 | -29% | 0 | 0 | — |
case-13 | fail→pass | 1,701 | 1,719 | +1% | 1 | 1 | 0% | 175 | 1,214 | +594% | 0 | 0 | — |
case-14 | fail→pass | 3,471 | 1,557 | -55% | 1 | 1 | 0% | 471 | 1,215 | +158% | 0 | 0 | — |
case-15 | fail→pass | 2,946 | 1,971 | -33% | 1 | 1 | 0% | 427 | 1,199 | +181% | 0 | 0 | — |
case-16 | fail→pass | 9,929 | 1,660 | -83% | 1 | 1 | 0% | 1,834 | 1,315 | -28% | 0 | 0 | — |
case-17 | fail→pass | 2,092 | 1,768 | -15% | 1 | 1 | 0% | 267 | 1,314 | +392% | 0 | 0 | — |
case-18 | fail→pass | 11,009 | 3,362 | -69% | 1 | 1 | 0% | 1,850 | 1,206 | -35% | 0 | 0 | — |
case-19 | pass→pass | 11,090 | 5,941 | -46% | 1 | 1 | 0% | 1,770 | 1,125 | -36% | 0 | 0 | — |
case-20 | pass→pass | 12,243 | 9,883 | -19% | 1 | 1 | 0% | 2,449 | 2,794 | +14% | 0 | 0 | — |
case-21 | pass→pass | 8,776 | 4,423 | -50% | 1 | 1 | 0% | 1,729 | 1,727 | -0% | 0 | 0 | — |
case-22 | pass→pass | 12,366 | 7,823 | -37% | 1 | 1 | 0% | 2,405 | 2,447 | +2% | 0 | 0 | — |
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. 22 cases were attempted. The headline lift of +55 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.