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prediction-market-oracle-research

Evaluate prediction markets as information sources for products, agents, dashboards, and business decisions. Record probabilities implied by market prices with sources and timestamps, assess signal quality, and compare other evidence to recommend suitable uses and explain limitations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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. Use when evaluating prediction markets as a data source or oracle signal for a product, agent, or dashboard.

설치 방법 보기

이럴 때 유용합니다

  • Evaluating prediction-market data for a product
  • Comparing market probabilities with internal dashboard metrics
  • Assessing probability signals for agent memory or alerts
  • Comparing event outcomes for scenario planning

포함된 파일(1)

  • SKILL.md2.3 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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.

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