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ito-data-atlas-agent

Design data atlas agent architectures with guardrails and auditable output contracts.

Updated Apr 13, 2026
Search Tags:#workflow#design#research#human-in-the-loop#basket#data-atlas
One-click install
npx skills add https://github.com/sakamoto-family-smile/agent_monorepo --skill ito-data-atlas-agent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ito-data-atlas-agent
Source: https://github.com/sakamoto-family-smile/agent_monorepo/tree/main/.claude/skills/ecc/ito-data-atlas-agent
Command: npx skills add https://github.com/sakamoto-family-smile/agent_monorepo --skill ito-data-atlas-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The skill helps architect data-atlas style agents for research planning and human-in-the-loop editing, enabling structured workflow design without live trading execution.

Core Features & Use Cases

  • Define data sources and access requirements for basket research.
  • Draft candidate underliers, weights, rules, and questions for decision-making.
  • Establish guardrails, audit trails, and human approval points to ensure safe, compliant workflows.

Quick Start

Outline a basket spec for a hypothetical data sources set and prepare an editable parameter draft for human review.

Frequently Asked Questions about ito-data-atlas-agent

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

FAQPage Schema
How do I design a human-in-the-loop workflow for basket research?▼

A data-atlas architecture pattern structures basket research workflows by specifying guardrails, four processing lanes, and an auditable output contract, enabling structured parameter drafting and human review without live trading execution.

What is a data-atlas style agent for research planning?▼

A data-atlas style agent is an architecture pattern for research planning that observes data sources, drafts candidate underliers and weights, and routes outputs through human approval points to ensure compliant, auditable workflows.

How do I draft basket parameters and underliers for human review?▼

Draft basket parameters by outlining a basket spec with candidate underliers, weights, and rules, then prepare the parameter draft as an editable output contract routed through designated human approval points.

Can I use this architecture pattern for live trading execution?▼

No, this architecture pattern applies specifically to research planning, scenario exploration, and workflow design where data sources are observed and human review is required, excluding live trading execution.

How do you establish guardrails and audit trails for research workflows?▼

Establish guardrails and audit trails by applying an architecture pattern with four lanes and an auditable output contract, defining data source access requirements and setting explicit human approval points for production workflows.

What's the best way to explore scenarios for basket research without live execution?▼

The best way to explore scenarios without live execution is using a data-atlas architecture that observes data sources, drafts parameters, and enforces human-in-the-loop review points for safe, structured scenario exploration.

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