Genpark Agent Bash Command Sandbox Guard Skill

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⚡ Overview & Architectural Significance

genpark-agent-bash-command-sandbox-guard-skill provides zero-dependency, deterministic agentic execution safety, sandboxing, and financial circuit breakers engineered strictly using Python 3.9+ standard library.

🌟 Key Architectural Capabilities

• Zero External Dependencies: Operates exclusively via pure Python (math, re, collections, heapq, hashlib, json). Zero pip install overhead, zero C-extension compile errors.
• Enterprise Agent Safety Invariants: Implements formal defenses against destructive shell commands, prompt injections, runaway spend loops, differential privacy data leakage, and planning deadlocks.
• Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.

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🏗️ Architectural Safety State Machine

flowchart TD
    UserPrompt["Incoming User Instruction / External Input"] --> InjectionGuard["Prompt Injection & Jailbreak Sentinel"]
    
    InjectionGuard -->|Malicious Injection| BlockPrompt["Reject Prompt (400 Bad Request)"]
    InjectionGuard -->|Safe Prompt| AgentPlanner["Autonomous Agent Planner / LLM Core"]
    
    AgentPlanner --> CircuitBreaker["Token Spend & Cost Circuit Breaker"]
    CircuitBreaker -->|Budget Exceeded| FreezeSpend["Freeze Execution & Alert Admin"]
    CircuitBreaker -->|Within Budget| LoopDetector["Deadlock & Liveloss Loop Detector"]
    
    LoopDetector -->|Infinite Loop Detected| BreakLoop["Inject Corrective Guidance & Reroute Plan"]
    LoopDetector -->|Healthy Trajectory| CommandSandbox["Bash / Subprocess Sandbox Guard"]
    
    CommandSandbox -->|Destructive / Traversal| BlockCmd["Block Execution (Security Violation)"]
    CommandSandbox -->|Safe Command| ToolExec["Safe Tool Execution"]
    
    ToolExec --> PrivacyGuard["Synthetic Data Differential Privacy Guard"]
    PrivacyGuard --> SanitizedOutput["Sanitized Output & Verified Return"]

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🚀 Quickstart & Standalone Execution

Local Python Client Usage

from client import AgentBashCommandSandboxGuard

# Initialize engine
engine = AgentBashCommandSandboxGuard()

# Execute self-testing benchmark suite
result = engine.run_benchmark_bash_guard()
print("Execution Result:", result)

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🔌 One-Click MCP Integration (Claude Desktop / Cursor)

Add to your claude_desktop_config.json or cursor.json:

{
  "mcpServers": {
    "genpark-agent-bash-command-sandbox-guard-skill": {
      "command": "python",
      "args": ["-u", "/path/to/genpark-agent-bash-command-sandbox-guard-skill/mcp_server.py"]
    }
  }
}

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📦 Smithery.ai & PyPI Deployment

This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:

pip install git+https://github.com/alphaparkinc/genpark-agent-bash-command-sandbox-guard-skill.git

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