silver-platter

Audit data-readiness by mapping raw sources to processed snapshots and consumer interfaces.

1|Updated Apr 18, 2026

Search Tags:#optimization #audit #maven #data-flow #data-readiness #agentic-os

Ready to run

npx skills add https://github.com/CC90210/CMO-Agent --skill silver-platter-cc90210

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: silver-platter
Source: https://github.com/CC90210/CMO-Agent/tree/main/skills/silver-platter
Command: npx skills add https://github.com/CC90210/CMO-Agent --skill silver-platter-cc90210

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill solves the problem of inefficient data retrieval by identifying where agents are pulling raw, unoptimized data instead of using pre-aggregated snapshots, ensuring high-performance agentic operations.

Core Features & Use Cases

Data-Readiness Audit: Maps raw data sources (Pantry) against processed snapshots (Prep Table) and consumer interfaces (Plate).
Performance Optimization: Identifies anti-patterns where agents bypass the Prep Table, causing latency or redundant API calls.
Quick-Win Prioritization: Ranks data-layer improvements based on potential session time savings and agent impact.

Quick Start

Execute the silver platter audit to generate a data-readiness report for the current Maven agent configuration.

Frequently Asked Questions about silver-platter

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

FAQPage Schema

How do I audit agentic data-readiness for unoptimized data retrieval?▼

To audit agentic data-readiness, map raw data sources against processed snapshots and consumer interfaces to identify where agents pull unoptimized data instead of using pre-aggregated snapshots, ensuring high-performance operations.

Why does my agent bypass the Prep Table and cause redundant API calls?▼

Agents cause redundant API calls by bypassing the Prep Table to pull raw data directly from the Pantry. This anti-pattern is identified by evaluating data flow efficiency within the Maven CMO-Agent architecture to locate latency bottlenecks.

What is data-readiness optimization in an agentic OS data layer?▼

Data-readiness optimization in an agentic OS data layer is the process of validating data taxonomy integrity and mapping raw inputs to processed snapshots, ensuring agents retrieve pre-aggregated data for efficient operations.

How do I prioritize data-layer improvements to reduce agent session time?▼

Prioritize data-layer improvements by ranking identified optimization opportunities based on potential session time savings and agent impact, generating a data-readiness report that highlights quick-win performance fixes.

Do I need access to the capability graph to audit a Maven CMO-Agent architecture?▼

Yes, auditing a Maven CMO-Agent architecture requires access to the capability graph and data taxonomy to validate the integrity of the agentic OS data layer and accurately map data flow efficiency.

When should I not use an automated audit for agentic data flow?▼

You should not use an automated audit when the capability graph or data taxonomy is inaccessible, as validating the integrity of the agentic OS data layer requires these components to accurately map data flow and identify anti-patterns.