Prediction Market Oracle Research
- 1.3k installs
- 238k repo stars
- Updated August 5, 2026
- affaan-m/ecc
Prediction-market-oracle-research is an agent research skill that systematically evaluates prediction markets as grounded data sources or oracle signals for developers building agents, dashboards, and decision systems.
About
Prediction-market-oracle-research is an affaan-m ECC origin skill for source-grounded analysis of prediction markets as forecasting inputs, oracle-like signals, or decision-intelligence layers. It guides developers through evaluating market-implied probabilities while separating venue mechanics, liquidity constraints, and integration caveats from treating prices as objective truth. Explicit guardrails prohibit investment advice and trading recommendations. Reach for this skill when building agents, dashboards, or corporate decision systems that might ingest prediction-market data and need rigorous due diligence on signal quality, not speculative trading guidance. The research output covers integration patterns and documented limitations for engineering teams.
- 6-step research workflow that produces timestamped probability records with source links
- Evaluates liquidity, spread, market age, trader concentration, resolution authority and restrictions
- Explicitly compares market signals against filings, news, polls, customer data and internal KPIs
- Includes hard guardrails against treating prices as truth or giving investment advice
- Requires running llm-trading-agent-security before any on-chain write authority
Prediction Market Oracle Research by the numbers
- 1,263 all-time installs (skills.sh)
- +84 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #903 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 1.3k |
|---|---|
| repo stars | ★ 238k |
| Last updated | August 5, 2026 |
| Repository | affaan-m/ecc ↗ |
How do you evaluate prediction markets as data sources?
Systematically evaluate prediction markets as grounded data sources or oracle signals for agents, dashboards, and decision systems.
Who is it for?
Developers building agents or dashboards who need rigorous research on prediction-market data quality before wiring oracle signals.
Skip if: Skip prediction-market-oracle-research when you need trading advice or treat market prices as ground truth without caveats.
When should I use this skill?
A developer considers prediction markets as forecasting inputs, oracle signals, or decision-intelligence layers for a product.
What you get
Source-grounded research report on market-implied probabilities, venue mechanics, caveats, and integration patterns.
- Oracle signal research report
- Integration pattern recommendations
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 prediction-market-oracle-research provide trading advice?
Prediction-market-oracle-research does not provide investment advice or trading recommendations; it researches venue mechanics, liquidity, caveats, and integration patterns for engineering teams considering market-implied probabilities as data inputs.
What systems can use prediction-market-oracle-research output?
Prediction-market-oracle-research output supports developers building agents, dashboards, and corporate decision-intelligence systems that need source-grounded evaluation before ingesting prediction-market probabilities as oracle signals.