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The official Agent Skills spec (agentskills.io/specification) whitelists exactly 6 top-level frontmatter keys (name/description/license/compatibility/metadata/ allowed-tools). A top-level `origin` key fails the official validator (anthropics/skills quick_validate.py ALLOWED_PROPERTIES; skills-ref validate). This moves `origin: X` -> `metadata.origin: X` across the canonical skills/ tree, preserving each value verbatim. Frontmatter-only, minimal diff. - 251 SKILL.md updated (242 new metadata block, 9 appended to existing metadata) - origin values preserved verbatim (verified 251/251) - YAML validated on all changed files - scoped to canonical skills/ only (docs/<lang> translations + tool mirrors .cursor/.kiro/.agents left untouched; presumably regenerated from canonical) Addresses #2233
65 lines
2.2 KiB
Markdown
65 lines
2.2 KiB
Markdown
---
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name: ito-data-atlas-agent
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description: 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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metadata:
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origin: ECC
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---
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# Itô Data Atlas Agent
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Use this skill to design an agent that watches data sources, builds candidate
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prediction-market baskets, drafts parameter changes, and hands the result to a
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human for review.
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This skill describes architecture and workflow. It does not run live trading.
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## Guardrails
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- Keep all execution behind explicit human approval.
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- Require `ITO_API_KEY` only for read-only Itô data access unless a separate
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private implementation explicitly adds execution controls.
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- Do not persist private user data unless the target repo already has a storage
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contract and the user asks for it.
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- Do not expose private strategy logic, venue credentials, or local paths in
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public docs.
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## Architecture Pattern
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Use four lanes:
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1. Research collector: public web, X, GitHub, venue docs, API metadata, and
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Itô read endpoints when gated access exists.
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2. Basket drafter: turns sources into candidate underliers, weights, rules, and
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questions.
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3. Risk reviewer: checks data freshness, venue limits, resolution ambiguity,
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compliance notes, and prompt-injection exposure.
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4. Human editor: opens a chat or UI state where the user can approve, reject,
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adjust, or ask for more research.
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## Workflow
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1. Define the user objective and excluded actions.
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2. List data sources and access requirements.
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3. Draft a basket spec with provenance for every underlier.
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4. Produce editable parameters rather than executable orders.
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5. Store an audit trail: inputs, model output, sources, and human decision.
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## Useful Skill Chains
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- `deep-research` for source collection.
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- `x-api` for current social/event signal.
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- `ito-market-intelligence` for venue and underlier context.
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- `ito-basket-compare` for user knowledge-base matching.
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- `prediction-market-risk-review` before any execution-capable integration.
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## Output Contract
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Return an implementation-ready workflow spec with:
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- data sources
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- access gates
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- agent roles
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- human approval points
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- storage/audit boundary
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- non-goals
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