
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)
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
What ito-data-atlas-agent says it does
This skill describes architecture and workflow. It does not run live trading.
Keep all execution behind explicit human approval.
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| Installs | 1.4k |
|---|---|
| repo stars | ★ 238k |
| Last updated | August 5, 2026 |
| Repository | affaan-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
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_KEYonly 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-researchfor source collection.x-apifor current social/event signal.ito-market-intelligencefor venue and underlier context.ito-basket-comparefor user knowledge-base matching.prediction-market-risk-reviewbefore 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.