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affaan-m/ito-data-atlas-agent

affaan-m

ito-data-atlas-agent

Design background Data Atlas style agents for Itô basket research, market discovery, parameter drafting, and human-in-the-loop editing. Use for architecture and workflow planning, not live order execution.

New~549Updated Jul 14, 2026

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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Overall Score

76/100

Grade

B

Good

Safety

82

Quality

72

Clarity

78

Completeness

68

Summary

This skill guides agents to design human-in-the-loop Data Atlas workflows for Itô basket research and market discovery. It provides a four-lane architecture (research, drafting, risk review, human approval) and workflow patterns for building candidate prediction-market baskets with explicit human approval gates, emphasizing read-only data access and parameter drafting rather than live order execution.

Detected Capabilities

architecture planningworkflow designintegration guidanceread-only API access documentationhuman approval gate specificationaudit trail designrisk checkpoint definition

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

design prediction market agentitô basket workflowmarket discovery architecturehuman approval gatesbasket parameter draftingdata atlas design

Risk Signals

INFO

Conditional credential requirement: ITO_API_KEY for read-only access only, with explicit guardrail against execution without controls

Guardrails section
INFO

Data persistence constraint: explicitly discourages persisting private user data unless target repo has pre-existing storage contract

Guardrails section
WARNING

Operator trust requirement: skill assumes downstream execution controls are external; does not implement them directly

Architecture Pattern and Workflow sections

Use Cases

  • Design a multi-stage agent workflow for researching prediction market baskets
  • Draft candidate underliers and weights with human approval gates before execution
  • Build audit trails for basket parameter decisions with source provenance
  • Integrate risk review and compliance checks into market discovery pipelines
  • Create human-in-the-loop systems for Itô API data collection and strategy drafting

Quality Notes

  • Clear guardrails document credential scoping and data persistence boundaries—appropriate for a design-stage skill
  • Four-lane architecture is well-articulated and separates research, drafting, review, and approval phases clearly
  • Workflow steps are concrete and actionable, moving from objective definition through audit trail creation
  • Useful Skill Chains section effectively signals integration points and prevents scope creep by naming complementary skills
  • Risk review lane (lane 3) explicitly addresses prompt-injection exposure, showing security awareness
  • Non-execution focus is stated upfront, setting correct expectations for design-only usage
  • Missing concrete implementation examples or reference implementations—skill is guidance, not executable code
  • No explicit error handling or fallback patterns for missing data sources or API unavailability
Model: claude-haiku-4-5-20251001Analyzed: Jul 14, 2026

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Version History

  1. v1.1

    Content updated

    ✦ AINo behavioral changes detected.

    2026-07-14

    Latest
  2. v1.0

    2026-05-25

    View This VersionInitial version

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