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

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

decision context
market sources
signal quality
comparison sources
integration recommendation
caveats

End with:

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

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Overall Score

82/100

Grade

B

Good

Safety

88

Quality

85

Clarity

78

Completeness

72

Summary

This skill guides agents to research prediction markets as data sources for decision intelligence, forecasting, and oracle-like signals. It provides a six-step research workflow for evaluating market signals, comparing them against non-market sources, and assessing signal quality based on liquidity, spreads, and resolution authority. The skill explicitly disclaims investment advice and includes guardrails against manipulation and misuse.

Detected Capabilities

web researchdata synthesiscomparative analysistime-series data retrievalsignal evaluation

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

prediction market researchoracle signal evaluationmarket-implied probabilitiesdecision intelligence sourceforecast signal analysismarket liquidity assessment

Use Cases

Source-grounded context enrichment for human analysts considering market data
Dashboard signal integration combining market-implied probabilities with internal metrics
Agent memory input for time-stamped forecasting signals retrievable in future queries
Threshold-based alerting when prediction market spreads, liquidity, or probabilities cross specified limits
Scenario planning that compares multiple event outcomes without automated trading execution

Quality Notes

Clear security guardrails with explicit prohibition on investment advice
Well-structured research workflow with concrete evaluation criteria (liquidity, spreads, market age, resolution authority)
Strong output contract specifying required sections (decision context, market sources, signal quality, comparison sources, integration recommendation, caveats)
Appropriate disclaimer at end of output distinguishing signals from investment advice
Practical integration patterns section grounds the skill in real use cases
Guardrail requiring security review (`llm-trading-agent-security`) for on-chain or execution-linked systems shows security-aware design

Model: claude-haiku-4-5-20251001Analyzed: May 25, 2026

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