genpark-agent-budget-token-spend-circuit-breaker-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-budget-token-spend-circuit-breaker-skillset my token budget to $10 per hour and freeze if exceeded"
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-budget-token-spend-circuit-breaker-skill
⚡ Overview & Architectural Significance
genpark-agent-budget-token-spend-circuit-breaker-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: agentguard
🏗️ 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 AgentBudgetTokenSpendCircuitBreaker
# Initialize engine
engine = AgentBudgetTokenSpendCircuitBreaker()
# Execute self-testing benchmark suite
result = engine.run_benchmark_circuit_breaker()
print("Execution Result:", result)🔌 One-Click MCP Integration (Claude Desktop / Cursor)
Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-agent-budget-token-spend-circuit-breaker-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-agent-budget-token-spend-circuit-breaker-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-budget-token-spend-circuit-breaker-skill.gitThis server cannot be deployed
Maintenance
Related MCP Connectors
Budget & cost control for AI agents — per-agent spend caps + rate limits before each call.
Deterministic runtime safety for AI agents: scan PII, gate tool actions, verify LLM output.
Free spend guardrails for AI agents: approve/deny/ask_user, caps, dupes.
Meter, cap, and block AI agent spend before the provider is charged.
Related MCP Servers
- AlicenseAqualityAmaintenanceRuntime governance and budget guardrails for Claude Code, Cursor, and autonomous AI agents. Enforces per-session spend caps, verifier safety gates, and runaway loop prevention.24673 npm364Apache 2.0

agentguardofficial
AlicenseNot gradedqualityCmaintenanceEnforces policy controls for AI agents, including spend limits, action approvals, kill switch, scoped credentials, dry-run diffs, loop prevention, and auditable hash-chained logs.MIT- 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 agents to track real-time token spend and cost velocity, automatically throttle or freeze execution when budgets are exceeded, and enforce sandbox and safety guardrails.7MIT