Prediction Market Oracle Research
This walks you through using prediction markets as data inputs for dashboards, agents, or decision support without crossing into trading advice. It structures research around liquidity checks, resolution mechanics, venue comparisons, and cross-referencing non-market sources like filings or polls. The output format keeps signal quality separate from the probability number itself, which matters when you're feeding this into automated systems or executive reporting. The guardrails are sensible: it won't pretend thin markets are reliable, and it explicitly calls out manipulation risk and stale data. Useful if you're building intelligence tooling that needs probabilistic signals but can't afford to treat market prices as ground truth.
- skills.sh installs
- 796
- GitHub stars
- 229.1K
- last updated
- Jul 14, 2026
Install
$ npx -y skills add affaan-m/everything-claude-code --skill prediction-market-oracle-research --agent claude-codeInstalls into .claude/skills of the current project.
Files
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-securitybefore 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.
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- Repository
- github.com/affaan-m/everything-claude-code
- Category
- Data Science & ML
- First seen
- Jul 14, 2026
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