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affaan-m/prediction-market-oracle-research

affaan-m

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

NewUpdated Sep 9, 2026

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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Overall Score

78/100

Grade

B

Good

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

85

Quality

75

Clarity

82

Completeness

68

Summary

A research skill for using prediction markets as data sources, oracle signals, and decision-intelligence inputs. It provides a structured workflow for evaluating market-implied probabilities, assessing signal quality, comparing against alternative sources, and integrating into products, agents, or dashboards without providing investment advice.

Detected Capabilities

market research and analysisprobability evaluationdata source comparisonintegration pattern guidancesignal quality assessmentcross-reference with alternative data sources

Trigger Keywords

Phrases that agents use to match this skill to user intent.

prediction market researchoracle signal evaluationmarket-implied probabilityforecast data sourcedecision intelligence signalmarket signal integration

Risk Signals

INFO

No automated trading or execution enabled; guardrail explicitly prevents write authority without security review

Guardrails section
INFO

Instruction explicitly excludes investment advice and trading recommendations

Guardrails section
INFO

Cross-references security skill for on-chain/execution-linked systems

Guardrails section

Use Cases

  • >Evaluating prediction market signals as inputs to corporate decision intelligence or risk assessment tools
  • Sourcing market-implied probabilities for research dashboards or agent memory systems
  • Assessing the reliability of prediction market data before integrating it into automated systems
  • Comparing market-implied probabilities against alternative data sources (filings, news, internal KPIs) to validate forecasting quality
  • Building alerting systems that trigger on prediction market threshold changes (probability, spread, liquidity)
  • Scenario planning that uses market probabilities as context without automating trades or investment decisions

Quality Notes

  • Clear guardrails establish boundaries between research and investment advice; prevents misuse
  • Well-structured research workflow with six concrete steps from decision definition through recommendation
  • Explicit output contract ensures agent delivers source-grounded analysis with required context
  • Integration patterns section covers realistic use cases (research assistant, dashboard, alerting, scenario planning)
  • Signal quality evaluation criteria comprehensively addresses liquidity, spreads, resolution authority, and trader concentration
  • Appropriate disclaimer mandated at end of output prevents legal liability
  • Cross-references external security skill for on-chain scenarios, showing awareness of execution risk
  • No supporting examples, market venue references, or sample queries provided — agent must generate from scratch
  • No error handling guidance for unavailable markets, stale data, or ambiguous resolution scenarios
  • Lacks specific guidance on comparing market signals with internal KPIs or customer data
Model: claude-haiku-4-5-20251001Analyzed: Sep 9, 2026

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Version History

  1. v2.0

    Contract changed: description

    ✦ AIClarifies activation condition: skill now explicitly triggers when evaluating prediction markets as data source or oracle signal.

    triggering2026-09-09

    LATEST
  2. v1.1

    Content updated

    ✦ AINo behavioral changes detected.

    2026-07-14

    View This Version
  3. v1.0

    2026-05-25

    View This VersionInitial version

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