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 SHAdd0WTAka/Zen-Ai-Pentest --skill workflow-orchestratorgit clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/shadd0wtaka/zen-ai-pentest/workflow-orchestrator)<a href="https://agentmods.dev/skills/shadd0wtaka/zen-ai-pentest/workflow-orchestrator"><img src="https://agentmods.dev/badge/skills/shadd0wtaka/zen-ai-pentest/workflow-orchestrator/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/shadd0wtaka/zen-ai-pentest/workflow-orchestrator"><img src="https://agentmods.dev/badge/skills/shadd0wtaka/zen-ai-pentest/workflow-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00085 | $0.01822 |
| Opus 5 | $0.00043 | $0.00911 |
| Sonnet 5 | $0.00017 | $0.00364 |
| Haiku 4.5 | $0.00009 | $0.00182 |
Grade A, and why
workflow-orchestrator scanned grade A with 0 findings 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 13d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow Orchestrator Skill
Manages cyclic testing workflows with dependency chaining, adaptive re-evaluation, and intelligent state management.
Quick Start
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:
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)
What ships with it
10 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.
- assets/workflows/api-testing.yaml 1.6 KB
- assets/workflows/standard-web.yaml 2.2 KB
- references/dependency_patterns.md 2.4 KB
- references/state_machine.md 1.6 KB
- scripts/__init__.py 60 B runs code
- scripts/adaptive_strategy.py 13 KB runs code
- scripts/dependency_chain.py 15 KB runs code
- scripts/parallel_executor.py 12 KB runs code
- scripts/state_manager.py 16 KB runs code
- scripts/workflow_engine.py 17 KB runs code
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.
- 13d ago First seen · 251 lines · 85 tokens per session scan A b447d372bbcb
workflow-orchestrator is a skill published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed yesterday), licensed MIT. It adds 85 tokens to every session and 1,822 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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