shadd0wtaka/zen-ai-pentest/ai-delegation

Claude Code and agent skill by shadd0wtaka/zen-ai-pentest

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Install

npx vskill@latest install shadd0wtaka/zen-ai-pentest/ai-delegation

SKILL.md

AI↔AI Delegation & Multi-Agent Orchestrierung Skill

Workflows für Agent-to-Agent Delegation, Workflow-Chaining, Sub-Agent-Spawning, Result-Merging.

Architektur

User Request
│
▼
Orchestrator Agent (Coordinator)
│
├── ▶ Specialist Agent A ──▶ Result
├── ▶ Specialist Agent B ──▶ Result
└── ▶ Specialist Agent C ──▶ Result
│
▼
Merge & Final Response

Delegation Patterns

1. Sequential Chain

Jeder Agent baut auf dem vorherigen auf:

deep-recon --target example.com → recon.json
deep-exploit --target-file recon.json → exploit.log
deep-report --scan-id <id> --format pdf → report.pdf
2. Parallel Fan-Out

Mehrere Agenten gleichzeitig, Ergebnisse mergen:

python
import asyncio
async def fan_out(target: str):
tasks = [
agent_run("recon", target),
agent_run("network", target),
agent_run("cloud", target),
agent_run("social", target),
]
results = await asyncio.gather(*tasks, return_exceptions=True)
return merge_results(results)
3. Hierarchisch (Manager + Worker)
python
manager_prompt = """
Analyze the task and break it into sub-tasks.
Assign each to the right specialist agent.
Track dependencies and merge results.
"""
4. ReAct mit Sub-Agent-Calls
python
class DelegatingReActAgent(ReActAgent):
async def _act(self, plan: str) -> ToolResult:
if plan.get("delegate_to"):
# Spawn sub-agent
sub = create_agent(plan["delegate_to"])
return await sub.run(plan["task"])
return await super()._act(plan)

Workflow Chaining

Dependency Graph
python
workflow = coordinator.create_workflow(
target="example.com",
phases=["recon", "scan", "exploit", "report"],
)
# Automatisch: recon-output → scan-input, scan-output → exploit-input
Conditional Branching
python
if result["critical_findings"] > 0:
# Escalate to red team
workflow.add_phase("redteam")
else:
# Skip to report
workflow.add_phase("report")

Context Passing

Zwischen Agenten wird Kontext über JSON weitergegeben:

json
{
"task_id": "wf-abc123",
"target": "example.com",
"phase": "recon",
"results": {
"subdomains": ["admin.example.com", "api.example.com"],
"ports": {"22": "open", "80": "open", "443": "open"},
"technologies": ["nginx", "react", "postgresql"]
},
"artifacts": ["recon.json", "screenshots/"]
}

Hermes Integration (via MCP)

python
# Agent delegiert an anderen Agent
zen_agents_agent_run(agent_type="recon", target="example.com")
# → Result enthält findings für nächsten Agent
# Workflow orchestrieren
zen_agents_workflow_create(target="example.com", phases="recon,scan,exploit,report")

Fehlerbehandlung

python
class AgentOrchestrator:
def handle_failure(self, agent: str, error: str):
if "timeout" in error.lower():
return self.retry(agent, timeout=timeout * 2)
elif "rate_limit" in error.lower():
ks_rotate_now() # VPN-Killswitch
return self.retry(agent)
else:
return self.escalate(agent, error)

Security

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  • Model reviewPassed

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Source

Popularity

Repository stars
433
Repository forks
76
7-day trend score
30

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