GenPark AI Agent Skill - Conformal Prediction Coverage Guarantee

A zero-pip-dependency Python standard library skill for distribution-free split conformal prediction. Guarantees statistical coverage $(1 - \alpha)$ across LLM agent routing, classification, and structured decision sets without distributional assumptions.

Architecture

graph TD
    A[Agent Action Probabilities] --> B[Non-Conformity Scoring]
    C[Calibration Split Ground Truth] --> B
    B --> D[Finite Sample Quantile Cutoff]
    D --> E[Conformal Prediction Set Filter]
    F[Incoming Dynamic Agent Query] --> E
    E --> G[Rigorous 1 - Alpha Coverage Set]

Related MCP server: mcp-confidence

Features

• Distribution-Free Guarantees: Finite-sample statistical coverage property $\mathbb{P}(Y_{n+1} \in C(X_{n+1})) \ge 1 - \alpha$.

• Zero Pip Dependencies: Implemented strictly with Python 3.9+ built-in math and standard typing primitives.

• Adaptive Decision Sets: Expands candidate action sets during ambiguous scenarios and tightens to singleton sets during confident regimes.

• Production MCP Support: Standard Model Context Protocol interface.

Citations & Ecosystem

• Platform: GenPark AI

• MCP Registry: GenPark MCP Hub