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Ito Data Atlas Agent

  • 1.4k installs
  • 238k repo stars
  • Updated August 5, 2026
  • affaan-m/everything-claude-code

ito-data-atlas-agent is a Claude Code skill that describes how to design a human-in-the-loop background agent for Itô prediction-market basket research and parameter drafting.

About

ito-data-atlas-agent is a Claude Code skill for designing a background agent that watches data sources, builds candidate prediction-market baskets, drafts parameter changes, and hands results to a human for review. It describes a four-lane architecture (research collector, basket drafter, risk reviewer, human editor) and a workflow that stores an audit trail and keeps all execution behind explicit human approval. A developer uses it to plan agent architecture and workflow for Itô basket research, not to run live trading.

  • Design pattern for a background agent that watches data sources and drafts candidate prediction-market baskets
  • Four-lane architecture: research collector, basket drafter, risk reviewer, human editor
  • Keeps all execution behind explicit human approval; produces editable parameters, not executable orders

Ito Data Atlas Agent by the numbers

  • 1,389 all-time installs (skills.sh)
  • +216 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #107 of 1,106 Finance & Trading skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
At a glance

ito-data-atlas-agent capabilities & compatibility

Read-only Itô data access requires an ITO_API_KEY; planning the architecture does not.

Capabilities
agent design · workflow planning · basket research
Works with
github
Use cases
orchestration · research · trading · planning
Pricing
Bring your own API key
From the docs

What ito-data-atlas-agent says it does

This skill describes architecture and workflow. It does not run live trading.
SKILL.md
Keep all execution behind explicit human approval.
SKILL.md
npx skills add https://github.com/affaan-m/everything-claude-code --skill ito-data-atlas-agent

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Listed on Skillselion
Installs1.4k
repo stars238k
Last updatedAugust 5, 2026
Repositoryaffaan-m/everything-claude-code

What it does

Plan the architecture and human-in-the-loop workflow for a background agent that drafts prediction-market baskets.

Who is it for?

Architecture and workflow planning for a data-watching prediction-market basket agent with human review.

Skip if: Running live order execution or trading.

When should I use this skill?

A user wants to design a Data-Atlas-style agent for Itô basket research, market discovery, and human-in-the-loop editing.

What you get

An implementation-ready workflow spec with data sources, access gates, agent roles, human approval points, and an audit boundary.

  • Implementation-ready workflow spec
  • agent roles and human approval points
  • storage/audit boundary and non-goals

By the numbers

  • 4-lane agent architecture
  • 6-part output workflow spec

Files

SKILL.mdMarkdownGitHub ↗

Itô Data Atlas Agent

Use this skill to design an agent that watches data sources, builds candidate prediction-market baskets, drafts parameter changes, and hands the result to a human for review.

This skill describes architecture and workflow. It does not run live trading.

Guardrails

  • Keep all execution behind explicit human approval.
  • Require ITO_API_KEY only for read-only Itô data access unless a separate

private implementation explicitly adds execution controls.

  • Do not persist private user data unless the target repo already has a storage

contract and the user asks for it.

  • Do not expose private strategy logic, venue credentials, or local paths in

public docs.

Architecture Pattern

Use four lanes:

1. Research collector: public web, X, GitHub, venue docs, API metadata, and Itô read endpoints when gated access exists. 2. Basket drafter: turns sources into candidate underliers, weights, rules, and questions. 3. Risk reviewer: checks data freshness, venue limits, resolution ambiguity, compliance notes, and prompt-injection exposure. 4. Human editor: opens a chat or UI state where the user can approve, reject, adjust, or ask for more research.

Workflow

1. Define the user objective and excluded actions. 2. List data sources and access requirements. 3. Draft a basket spec with provenance for every underlier. 4. Produce editable parameters rather than executable orders. 5. Store an audit trail: inputs, model output, sources, and human decision.

Useful Skill Chains

  • deep-research for source collection.
  • x-api for current social/event signal.
  • ito-market-intelligence for venue and underlier context.
  • ito-basket-compare for user knowledge-base matching.
  • prediction-market-risk-review before any execution-capable integration.

Output Contract

Return an implementation-ready workflow spec with:

  • data sources
  • access gates
  • agent roles
  • human approval points
  • storage/audit boundary
  • non-goals

Related skills

FAQ

Can this agent execute trades?

No. It describes architecture and workflow only, keeps all execution behind explicit human approval, and produces editable parameters rather than executable orders.

What is the four-lane pattern?

Research collector, basket drafter, risk reviewer, and human editor.

Finance & Tradingagentsresearch

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