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:
- Research collector: public web, X, GitHub, venue docs, API metadata, and
Itô read endpoints when gated access exists.
- Basket drafter: turns sources into candidate underliers, weights, rules, and
questions.
- Risk reviewer: checks data freshness, venue limits, resolution ambiguity,
compliance notes, and prompt-injection exposure.
- Human editor: opens a chat or UI state where the user can approve, reject,
adjust, or ask for more research.
Workflow
- Define the user objective and excluded actions.
- List data sources and access requirements.
- Draft a basket spec with provenance for every underlier.
- Produce editable parameters rather than executable orders.
- 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
---
name: ito-data-atlas-agent
description: Design agents that watch data sources, build candidate prediction-market baskets, draft parameter changes, and hand results to a human for review.
---
# 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
