workflow-orchestrator
Module Loop and Iteration Skill for orchestrating multi-phase penetration testing workflows.
Skill by SHAdd0WTAka
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/shadd0wtaka-workflow-orchestrator
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About
Use cases: testing, productivity
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/workflow-orchestrator
What's inside
- Skill name
workflow-orchestrator- Description
- Module Loop and Iteration Skill for orchestrating multi-phase penetration testing workflows. Use when coordinating sequential tool execution, managing dependencies between reconnaissance and vulnerability scanning modules, implementing adaptive fallback strategies, or managing workflow state across iterations. Triggers on tasks requiring dependency chaining (e.g., Nmap results feeding into Nuclei), adaptive workflow adjustments based on results, or stateful iteration management to avoid redundant scans.
- License
- MIT
- Instructions
- 247 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Agent Skills spec issues
- files: referenced but missing: references/optimization_guide.md
Files (11)
- SKILL.md
- api-testing.yaml · assets/workflows/
- standard-web.yaml · assets/workflows/
- dependency_patterns.md · references/
- state_machine.md · references/
- __init__.py · scripts/
- adaptive_strategy.py · scripts/
- dependency_chain.py · scripts/
- parallel_executor.py · scripts/
- state_manager.py · scripts/
- workflow_engine.py · scripts/
SKILL.md (first lines)
# Workflow Orchestrator Skill
Manages cyclic testing workflows with dependency chaining, adaptive re-evaluation, and intelligent state management.
## Quick Start
```python
from scripts.workflow_engine import WorkflowEngine
from scripts.dependency_chain import DependencyChain
from scripts.state_manager import StateManager
# Initialize workflow
state = StateManager(target="example.com")
engine = WorkflowEngine(state)
# Define dependency chain
chain = DependencyChain()
chain.add_step("recon", NmapModule(), required=True)
chain.add_step("vuln_scan", NucleiModule(),
depends_on=["recon"],
input_mapper=lambda r: {"ports": r.open_ports})
chain.add_step("exploit", ExploitModule(),
depends_on=["vuln_scan"],
fallback=EnumModule()) # Fallback if no vulns
# Execute with iterations
results = await engine.execute(chain, max_iterations=3)
```
## Workflow Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ WORKFLOW ITERATION │
├─────────────────────────────────────────────────────────────┤
│ 1. CHECK STATE → Skip if already tested │
│ 2. EXECUTE MODULE → Run with dependencies │
│ 3. EVALUATE RESULTS → Success / Empty / Error │
│ 4. ADAPT STRATEGY → Fallback if empty │
│ 5. CHAIN OUTPUTS → Feed into next modules │
│ 6. UPDATE STATE → Mark tested, store findings │
│ 7. ITERATE → Continue until max_iterations or complete │
└─────────────────────────────────────────────────────────────┘
```
## Core Components
### 1. Dependency Chaining (`scripts/dependency_chain.py`)
Manages execution order and data flow between modules:
```python
chain = DependencyChain()
# Reconnaissance phase
chain.add_step("port_scan", NmapScanner(), required=True)
chain.add_step("service_enum", ServiceEnumerator(),
depends_on=["port_scan"])
# Vulnerability phase
chain.add_step("web_scan", NucleiScanner(),
depends_on=["port_scan"],
condition=lambda ctx: 80 in ctx.get("port_scan", {}).ports or
443 in ctx.get("port_scan", {}).ports)
# Exploitation phase
chain.add_step("sql_exploit", SQLMapModule(),
depends_on=["web_scan"],
condition=lambda ctx: "sql_injection" in ctx.findings)
```
See [references/dependency_patterns.md](references/dependency_patterns.md) for complete patterns.
### 2. Adaptive Re-evaluation (`scripts/adaptive_strategy.py`)
Switches strategies based on results:
| Result | Action |
|--------|--------|
| Success | Continue to dependent modules |
| Empty | Trigger fallback module |
| Error | Retry with reduced intensity |
| Timeout | Queue for later retry |
```python
# Define fallback strategies
fallbacks = {
"web_scan": {
"empty_result": "service_deep_scan",
"error": "reduced_web_scan",
"timeout": "queue_retry"
},
"vuln_scan": {
"empty_result": "full_port_scan",
"blocked": "stealth_scan"
}
}
strategy = AdaptiveStrategy(fallbacks)
new_module = strategy.decide("web_scan", result)
```
### 3. State Management (`scripts/state_manager.py`)
Prevents redundant scans using Smart Memory:
```python
state = StateManager(target="example.com")
# Check before scan
if state.is_tested("nmap", ports="80,443"):
return state.get_result("nmap")
# Execute and store
result = await nmap.scan("example.com", ports="80,443")
state.store("nmap", result, metadata={"ports": "80,443", "timestamp": now()})
# Track tested paths
state.add_tested_path("/admin", method="GET", params={"id": "1"})From SHAdd0WTAka/Zen-Ai-Pentest, MIT.
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Signals
- Stars
- 480
- Forks
- 85
- Open issues
- 1
- Last commit (this folder)
- Jun 25, 2026 (4 months ago)
- Last release
- Feb 19, 2026 (8 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/SHAdd0WTAka/Zen-Ai-Pentest
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
workflow-orchestrator skill. Agent Mart. https://agentmart.dev/skills/shadd0wtaka-workflow-orchestrator (last updated 2026-10-24)