genpark-agent-deadlock-liveloss-loop-detector-skill
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@genpark-agent-deadlock-liveloss-loop-detector-skillscan this agent trajectory for infinite loops and planning deadlocks"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-agent-deadlock-liveloss-loop-detector-skill
⚡ Overview & Architectural Significance
genpark-agent-deadlock-liveloss-loop-detector-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.
Related MCP server: genpark-agent-bash-command-sandbox-guard-skill
🏗️ 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"]🚀 Quickstart & Standalone Execution
Local Python Client Usage
from client import AgentDeadlockLivelossLoopDetector
# Initialize engine
engine = AgentDeadlockLivelossLoopDetector()
# Execute self-testing benchmark suite
result = engine.run_benchmark_loop_detector()
print("Execution Result:", result)🔌 One-Click MCP Integration (Claude Desktop / Cursor)
Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-agent-deadlock-liveloss-loop-detector-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-agent-deadlock-liveloss-loop-detector-skill/mcp_server.py"]
}
}
}📦 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-deadlock-liveloss-loop-detector-skill.gitThis server cannot be deployed
Maintenance
Related MCP Connectors
Deterministic runtime safety for AI agents: scan PII, gate tool actions, verify LLM output.
The WAF for agents. Pattern-based + heuristic firewall scans prompts, RAG documents, tool argume...
AgentGuard — 20-tool AI safety MCP: policy preflight, risk scoring, audit logging, rate limits.
Pre-execution governance for AI agents. Deterministic PASS/FAIL/REVIEW verdicts, replayable proof.
Related MCP Servers
- AlicenseBqualityBmaintenanceProvides AI governance and action-assurance primitives, enabling trust scoring, policy-based allow/deny decisions, risk assessment, EU AI Act compliance checks, and an emergency kill-switch for autonomous agents.625 npmMIT
- AlicenseNot gradedqualityBmaintenanceEnables autonomous agents to run shell and subprocess commands through a guardrail that blocks destructive system mutations, path traversals, and reverse shells. It also adds prompt-injection detection, spend circuit breakers, loop detection, and privacy sanitization so agent execution stays within safe, budgeted bounds.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables autonomous agents to enforce financial guardrails by tracking real-time token burn and cost velocity, automatically throttling or freezing execution when budgets are exceeded. It also blocks destructive shell commands, prompt injections, and runaway planning loops while sanitizing outputs before they are returned.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables monitoring and interruption of autonomous agent runs by detecting infinite tool loops, livelocks, and planning deadlocks, with safety guardrails for prompt injection, spend limits, and sandboxed command execution.7MIT