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everything-claude-code/skills/prediction-market-oracle-research/SKILL.md
BERORINPO db7f2a6fd5 fix(skills): move top-level origin frontmatter key under metadata 
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
2026-06-11 21:12:21 +09:00

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name, description, metadata
name description metadata
prediction-market-oracle-research Research prediction markets as data sources or oracle signals for products, agents, dashboards, and corporate decision intelligence. Use for source-grounded analysis of market-implied probabilities, caveats, and integration patterns without investment advice.
origin
ECC

Prediction Market Oracle Research

Use this skill when prediction markets are being considered as a data source, forecasting input, oracle-like signal, or decision-intelligence layer.

Guardrails

  • Do not treat market prices as objective truth.
  • Do not provide investment advice or trading recommendations.
  • Separate venue mechanics, liquidity, incentives, and resolution rules from the implied signal.
  • Call out manipulation, thin liquidity, stale markets, and ambiguous outcomes.
  • For on-chain or execution-linked systems, run llm-trading-agent-security before granting any write authority.

Research Workflow

  1. Define the decision the signal is meant to inform.
  2. Find relevant markets, events, tags, and venues.
  3. Record market-implied probabilities with timestamps and source links.
  4. Evaluate signal quality:
    • liquidity
    • spread
    • market age
    • trader/incentive concentration if known
    • resolution authority
    • geography or account restrictions
  5. Compare against non-market sources such as filings, news, polls, research, customer data, or internal KPIs.
  6. Recommend whether the signal is usable, weak, or unsuitable for the stated decision.

Integration Patterns

  • Research assistant: source-grounded context for a human analyst.
  • Dashboard signal: market-implied probability alongside internal metrics.
  • Agent memory input: a time-stamped signal that can be retrieved later.
  • Alerting input: notify when probabilities, spreads, or liquidity cross a threshold.
  • Scenario planning: compare multiple event outcomes without automating trades.

Output Contract

Use:

  1. decision context
  2. market sources
  3. signal quality
  4. comparison sources
  5. integration recommendation
  6. caveats

End with:

Prediction-market signals are informational inputs, not investment advice.