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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.

OriginalShow the original description

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.

affaan-m/ECC
AI rating100/ 100See how to install

Good for

  • 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

Overview

What it does

Researches prediction markets as information sources for product and business decisions. It defines the decision to inform, finds relevant markets and events, and records probabilities implied by prices with timestamps and source links. It checks trading depth, the gap between buying and selling prices, market age, and who determines the outcome. It then compares the market information with news, research, and internal metrics to assess whether it is suitable for the decision.

When it helps

Use it when adding probabilities to dashboards or considering market information for agent memory, alert conditions, or plans covering several possible outcomes. It organizes reasons for using the information and concerns about it, supported by evidence.

Example requests

  • “Find prediction markets relevant to this business decision and assess their information quality.”
  • “Consider how to present market probabilities alongside internal metrics.”
  • “Compare several event outcomes and organize the evidence for planning.”

Notes

Market prices are not treated as objective truth. The review checks for manipulation, low trading activity, stale information, and ambiguous outcomes. Investment advice and trading recommendations are outside its scope. Before granting write access to systems involving blockchain operations or trade execution, a review using llm-trading-agent-security is required.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodIt clearly evaluates prediction markets as product and decision inputs and excludes investment advice.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodIt specifies six steps from defining the decision to comparing evidence, plus an output structure covering sources, quality, and recommendations.
SafetyAre sending data, deleting files, and running commands disclosed?
AGoodIt excludes trading recommendations and requires llm-trading-agent-security checks before granting write authority.
Easy setupDoes it need few tools or API keys, and are they documented?
AGoodResearch requires no specified tools or API keys; the additional check before granting write authority is explicit.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 19, 2026. Treat it as a guide and read the original before installing.

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Files included (1)

  • SKILL.md2.3 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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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