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shadow-analytics

Maintain shadow analytics diagnostics from paper-trading data without altering runtime behavior.

Updated Apr 22, 2026
Search Tags:#analytics#persistence#diagnostics#calibration#shadow#regime-matrix
One-click install
npx skills add https://github.com/EnesMeyzin98/Meridian --skill shadow-analytics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shadow-analytics
Source: https://github.com/EnesMeyzin98/Meridian/tree/main/agents/skills/shadow-analytics
Command: npx skills add https://github.com/EnesMeyzin98/Meridian --skill shadow-analytics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Shadow analytics diagnostics must remain coherent, descriptive, and bounded to passive insights from paper-trading data, never altering runtime behavior.

Core Features & Use Cases

  • Maintain and extend shadow diagnostic dimensions (stability, calibration, regime transitions) in the learning layer.
  • Surface shadow insights in operator panels without changing signal generation or execution.
  • Persist lightweight, local insight history for auditability and trend tracking.

Quick Start

Follow the steps in AGENTS.md to review and extend shadow analytics definitions without modifying runtime paths.

Frequently Asked Questions about shadow-analytics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What are shadow analytics diagnostics in a paper-trading system?▼

Shadow analytics diagnostics are descriptive insights derived from paper-trading data to track stability, calibration, and regime transitions without altering runtime execution or risk management behavior.

How do I extend shadow analytics dimensions without modifying runtime paths?▼

You can extend shadow analytics by updating definitions in app/learning/analytics.py and app/shadow_learning.py, ensuring changes remain purely descriptive and bounded to local storage persistence.

Can I use shadow analytics to change signal generation or AI advisory behavior?▼

No, shadow analytics diagnostics must never alter signal generation, execution, risk management, or AI advisory behavior, remaining limited to passive, descriptive insights from paper-trading data.

How do I surface shadow insights in operator panels for auditability?▼

You surface shadow insights by extending diagnostic dimensions in shadow UI surfaces, persisting lightweight local insight history to enable auditability and trend tracking without affecting execution.

Does shadow analytics persistence support remote or cloud storage?▼

No, shadow analytics persistence is strictly limited to local storage to maintain coherent, bounded diagnostics and prevent unintended runtime changes across the learning layer.

When do I need to maintain shadow analytics coherence?▼

You need to maintain shadow analytics coherence when extending diagnostic dimensions like stability and regime transitions, ensuring paper-trading insights remain descriptive and never trigger execution changes.

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