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alphaparkinc/genpark-autonomous-prompt-loss-gradient-exemplar-optimizer-skill

Autonomous prompt loss gradient & few-shot exemplar compiler (DSPy / TextGrad)

⭐ 8 stars

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Python

momentum ▲ 13.3

created 2026-09-03

on radar since 2026-09-03

agent-skills ai-agents claude cursor llm-tools mcp mcp-server model-context-protocol python skills zero-dependency

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About genpark-autonomous-prompt-loss-gradient-exemplar-optimizer-skill

genpark-autonomous-prompt-loss-gradient-exemplar-optimizer-skill

Production-Grade AI Agent Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)

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

genpark-autonomous-prompt-loss-gradient-exemplar-optimizer-skill is a deterministic, zero-dependency Python skill engineered for autonomous AI agents, multi-agent frameworks (Claude Desktop, Cursor, AutoGPT, CrewAI), and enterprise pipelines.

Executive Capability: Autonomous prompt loss gradient & few-shot exemplar compiler (DSPy / TextGrad)

⚡ 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. 🎯 Deterministic & Reliable: 100% predictable input/output contracts with full JSON Schema validation. 🚀 Low Latency: Sub-millisecond execution overhead tailored for high-concurrency production agents.

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

Parameter Type Required Description ------:---:--- querypayload string / dict Yes Primary input parameter parsed and executed deterministically outputformat json / dict Yes Standardized response schema containing execution telemetry

❓ 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, a

From the project README.

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