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SamuelNDCESamuelNDCE/claude-super-skill-library/skills/agent-ai-engineering/ito-data-atlas-agent/SKILL.md

ito-data-atlas-agent

Skill1 starsChanged 3 months agoMITView on GitHub

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.

  • Reads credentials

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill to design an agent that watches data sources, builds candidate prediction-market baskets, drafts parameter changes, and …

    line 9
  2. Distinctive:

    Keep all execution behind explicit human approval.

    line 17
  3. Distinctive:

    Do not persist private user data unless the target repo already has a storage contract and the user asks for it.

    line 20
  4. Distinctive:

    Do not expose private strategy logic, venue credentials, or local paths in public docs.

    line 22

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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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