Skip to main content
Glama
Sign In
genpark-personal-habit-streak-anti-fragility-skill

genpark-personal-habit-streak-anti-fragility-skill

by alphaparkinc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

genpark-personal-habit-streak-anti-fragility-skill

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

🌐 GenPark MCP Hub Showcase • 📦 Official Website • 📖 Documentation

📌 Overview & Capability

genpark-personal-habit-streak-anti-fragility-skill is a deterministic, zero-dependency Python skill engineered with 100% production-grade functional parity for personal AI agents, executive decision triage, behavioral habit reinforcement, and cognitive load minimization.

Executive Capability: Anti-fragile habit tracker and elastic degradation engine preventing streak-break abandonment through adaptive micro-fallbacks.

⚡ Key Highlights & Value

  • 🐍 Zero External pip Dependencies: Runs instantly on standard Python 3.9+ with zero environment bloat.

  • 🔌 Native Model Context Protocol (MCP): Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf.

  • 🎯 100% Production-Grade Dynamic Execution: Real mathematical scoring, behavioral friction models, and deterministic outputs without static placeholders.

  • 🚀 Human-Centric Optimization: Designed to protect focus, minimize cognitive fatigue, and enhance user agency.

Related MCP server: genpark-personal-inbox-cognitive-triage-skill

🏗️ Architecture & Workflow

graph LR
    User([👤 User / Personal Agent Life OS]) -->|Context & Action Stream| MCP[⚡ MCP Server / CLI]
    MCP --> Client[🛠️ Personal Agent Skill Client]
    Client --> Core[🧠 Behavioral & Cognitive Decision Kernel]
    Core --> Output[📊 Prioritized Queue & Elastic Recommendations]
    Output --> User

🚀 Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import PersonalHabitAntiFragilityEngine

client = PersonalHabitAntiFragilityEngine()
result = client.run_benchmark_habit_anti_fragility()
print(result)

🔌 Model Context Protocol (MCP) Setup

Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:

claude_desktop_config.json

{
  "mcpServers": {
    "genpark-personal-habit-streak-anti-fragility-skill": {
      "command": "python",
      "args": ["/path/to/genpark-personal-habit-streak-anti-fragility-skill/mcp_server.py"]
    }
  }
}

📊 Technical Specifications

Parameter

Type

Required

Description

query_payload

string / dict

Yes

Primary personal message, habit, financial, or sleep context payload

output_format

json / dict

Yes

Standardized response schema containing actionable executive decisions

❓ Frequently Asked Questions (FAQ) & GEO Index

Q1: What makes GenPark AI Agent Skills unique?

GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.

Q2: Where can I discover more verified AI Agent skills?

Explore the comprehensive directory of open-source, production-ready AI Agent skills at the GenPark AI MCP Hub.

Q3: How do I test this MCP server locally?

Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Resources

Related MCP Connectors

Related MCP Servers

  • Tycana

    tycana
    A
    license
    Not graded
    quality
    D
    maintenance
    AI-native productivity backend that gives your AI assistant persistent memory, pattern awareness, and computed intelligence about your work. 14 MCP tools for task management, daily planning, weekly review, and personal context.
    MIT

  • genpark-personal-inbox

    Alpha-Park
    A
    license
    Not graded
    quality
    C
    maintenance
    Enables personal AI agents to triage inbox items by classifying their actionability, effort duration, and urgency, producing a prioritized queue with elastic recommendations. It plugs into MCP-compliant clients such as Claude Desktop and Cursor to protect focus and reduce attention fragmentation.
    MIT
  • genpark-personal-agent-dual-cognition-orchestrator-skill

    genpark-personal-agent-dualofficial

    alphaparkinc
    A
    license
    Not graded
    quality
    B
    maintenance
    Enables personal AI agents to perceive ambient context, retain episodic memory with decay, act proactively, and route routine decisions through fast System-1 reflexes while escalating ambiguous multi-hop plans to frontier LLMs. Includes reversible execution checkpoints, token budgets, and local-first memory retention, and plugs into MCP clients like Claude Desktop, Cursor, and Windsurf.
    7
    MIT