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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.

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v1.0Saved May 25, 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

82/100

Grade

B

Good

Safety

88

Quality

80

Clarity

78

Completeness

76

Summary

This skill guides agents to design architectural workflows for Itô basket research and market discovery with human-in-the-loop approval controls. It structures a four-lane system (research collector, basket drafter, risk reviewer, human editor) to gather public data, synthesize candidate market baskets, perform risk checks, and surface parameters for human review—without executing live orders or trading operations.

Detected Capabilities

Data source discovery and planningAgent workflow architecture designParameter drafting and templatingHuman approval checkpoint designAudit trail and provenance tracking

Trigger Keywords

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

basket research workflowprediction market architecturehuman-in-the-loop approvalagent orchestration designmarket parameter drafting

Use Cases

  • Design agent workflows for prediction market basket research
  • Plan data collection and enrichment pipelines from multiple sources
  • Draft candidate market baskets with provenance tracking
  • Structure human-in-the-loop approval workflows for financial agents
  • Create audit trails linking data sources to risk assessment to human decisions

Quality Notes

  • Well-structured guardrails section explicitly forbids live trading and enforces human approval gates
  • Clear four-lane architecture pattern simplifies complexity and delegates responsibility appropriately
  • Emphasizes audit trails and provenance—good practice for financial decision workflows
  • Thoughtfully scopes credentials: read-only access to Itô data unless execution controls explicitly added
  • Skill chain recommendations integrate with other complementary skills effectively
  • Instructions are prescriptive but somewhat abstract—could include a worked example (e.g., sample basket draft, parameter template, approval UI pseudocode) to increase clarity
  • Output contract is clear about deliverables but could specify format (JSON schema, template, diagram notation)
Model: claude-haiku-4-5-20251001Analyzed: May 25, 2026

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