---
id: gh-ito-data-atlas-agent
name: "ito-data-atlas-agent"
url: https://skills.yangsir.net/skill/gh-ito-data-atlas-agent
author: affaan-m
domain: investment
tags: ["prediction-market", "data-atlas", "research", "workflow", "risk-review"]
install_count: 1500
rating: 4.30 (120 reviews)
github: https://github.com/affaan-m/ecc/tree/main/skills/ito-data-atlas-agent
---

# ito-data-atlas-agent

> 设计基于Itô数据的数据图谱代理，自动收集市场数据、构建预测市场篮子、草拟参数变更并交付人工审核。适用于预测市场研究、架构规划与工作流设计，不执行实时交易。

**Stats**: 1,500 installs · 4.3/5 (120 reviews)

## Before / After 对比

### 手动研究到初稿生成时间

**Before**:

用户需要手动从多个数据源（社交媒体、新闻、Itô API）收集信息，逐一分析并起草篮子参数，再自行检查风险，整个过程耗时且容易遗漏关键信号。

**After**:

此技能自动聚合数据源、生成篮子草案和风险提示，用户只需审批或微调，大幅缩短从研究到初稿的周期。

| Metric | Before | After | Change |
|---|---|---|---|
| 研究到初稿时间 | 480分钟 | 60分钟 | -87.5% |

## Readme

# 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


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*Source: https://skills.yangsir.net/skill/gh-ito-data-atlas-agent*
*Markdown mirror: https://skills.yangsir.net/api/skill/gh-ito-data-atlas-agent/markdown*