Prediction Market Oracle Research
- 1.4k installs
- 238k repo stars
- Updated August 5, 2026
- affaan-m/everything-claude-code
prediction-market-oracle-research is a Claude Code skill that researches prediction markets as oracle or forecasting signals for products and agents without giving investment advice.
About
prediction-market-oracle-research is a Claude Code skill for evaluating prediction markets as data sources or oracle-like signals for products, agents, and decision intelligence. It runs source-grounded analysis of market-implied probabilities, records them with timestamps and source links, and scores signal quality on liquidity, spread, market age, incentive concentration, and resolution authority. It explicitly gives no investment advice and never treats market prices as objective truth. Developers use it when designing agents, dashboards, or decision tools that might consume Polymarket-style probability feeds.
- Source-grounded research of prediction markets as oracle/forecasting signals, no investment advice
- Scores signal quality on liquidity, spread, market age, incentive concentration, and resolution authority
- Integration patterns for research assistant, dashboard signal, agent memory, and alerting
Prediction Market Oracle Research by the numbers
- 1,374 all-time installs (skills.sh)
- +245 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #106 of 1,106 Finance & Trading skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
prediction-market-oracle-research capabilities & compatibility
- Capabilities
- research · data analysis · market signal analysis
- Use cases
- research · data analysis
What prediction-market-oracle-research says it does
Do not treat market prices as objective truth.
Do not provide investment advice or trading recommendations.
Prediction-market signals are informational inputs, not investment advice.
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| Installs | 1.4k |
|---|---|
| repo stars | ★ 238k |
| Last updated | August 5, 2026 |
| Repository | affaan-m/everything-claude-code ↗ |
What it does
Assess whether a prediction-market probability feed is a usable oracle/forecasting signal for a product, agent, or dashboard, with caveats and integration patterns.
Who is it for?
Deciding if a market-implied probability feed is usable as a signal for an agent, dashboard, or decision tool.
Skip if: Getting trading recommendations or treating market prices as objective truth.
When should I use this skill?
Prediction markets are being considered as a data source, forecasting input, or oracle-like signal.
What you get
A source-grounded recommendation on whether a market signal is usable, weak, or unsuitable, with integration patterns and caveats.
- signal-quality assessment
- market source list
- integration recommendation
By the numbers
- six-part output contract
- six-step research workflow
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-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.Related skills
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Does this give trading advice?
No. It is source-grounded research only and ends by stating prediction-market signals are informational inputs, not investment advice.
What if the system executes trades on-chain?
For on-chain or execution-linked systems it says to run llm-trading-agent-security before granting any write authority.