Prediction Market Oracle Research

SkillAI & models

This skill guides an agent through researching prediction markets as forecasting data sources. It helps find relevant markets, record market-implied probabilities with sources, and judge signal quality by checking liquidity, spreads, market age, and resolution rules. The agent then compares those signals against news, polls, or internal metrics and recommends whether they suit the decision at hand.

Use Prediction Market Oracle Research in Claude, ChatGPT or Ahel Desktop

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Then ask your AI: use the Prediction Market Oracle Research skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Have an AI agent that can load and run skills.

Prediction Market Oracle ResearchStart free

What your AI can do with it

  • Find prediction markets relevant to a product, agent, or decision
  • Record market-implied probabilities together with their sources
  • Judge signal quality using liquidity, spreads, market age, and rules
  • Compare market signals against news, polls, or internal metrics
  • Recommend whether a market signal suits the decision at hand

Getting started

  1. Have an AI agent that can load and run skills.
  2. Add the prediction-market-oracle-research skill to that agent.
  3. Give the agent the decision or product question you want researched.
  4. Ask the agent to find relevant markets and record probabilities with sources.
  5. Review the agent's signal-quality checks and its recommendation.

What this skill tells your AI

The instructions your AI receives, as published by affaan-m/ecc in skills/prediction-market-oracle-research/SKILL.md and read by ahel’s review.

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.

Signals

GitHub stars
270k
Forks
40k
Last commit
Sep 2026

Questions

Does this skill give investment advice?
No. It supports source-grounded analysis of market-implied probabilities, caveats, and integration patterns without investment advice.
What does it check to judge signal quality?
It checks liquidity, spreads, market age, and resolution rules, then compares the signal against other sources like news, polls, or internal metrics.
Can it be used for dashboards and corporate decision intelligence?
Yes. It is meant for researching prediction markets as data sources or oracle signals for products, agents, dashboards, and corporate decision intelligence.
Advanced
Item type
skill
Key
prediction-market-oracle-research
Source
github.com/affaan-m/ecc