prediction-market-oracle-research
affaan-m/ecc
Research prediction markets as decision-intelligence signals for products, agents, and dashboards—without investment advice.
What is prediction-market-oracle-research?
This skill helps you evaluate prediction markets as data sources for forecasting and decision-making. Use it to analyze market-implied probabilities, assess signal quality, compare against non-market sources, and integrate market signals into dashboards, agents, or alerting systems while maintaining clear guardrails against manipulation and thin liquidity.
- Research relevant prediction markets, venues, and events for a specific decision
- Record market-implied probabilities with timestamps, spreads, and source links
- Evaluate signal quality by liquidity, resolution authority, trader concentration, and geography
- Compare market signals against non-market sources (filings, news, polls, internal KPIs)
- Recommend integration patterns: dashboards, agent memory, alerting, or scenario planning
- Identify and call out manipulation risks, stale markets, and ambiguous outcomes
How to install prediction-market-oracle-research
npx skills add null --skill prediction-market-oracle-research- Access to prediction market data (Polymarket, Manifold, or similar venues)
- Understanding of the business decision the signal is meant to inform
- Familiarity with market mechanics, liquidity, and resolution rules
How to use prediction-market-oracle-research
- 1.Define the specific decision or outcome the market signal should inform
- 2.Identify relevant prediction markets, venues, and event tags across available platforms
- 3.Record current market-implied probabilities, spreads, and timestamps with source links
- 4.Evaluate signal quality: check liquidity depth, bid-ask spread, market age, and resolution authority
- 5.Compare the market signal against non-market sources (news, filings, polls, internal KPIs)
- 6.Recommend whether the signal is usable, weak, or unsuitable for your stated decision
- 7.Document caveats: manipulation risks, thin liquidity, trader concentration, or geographic restrictions
- 8.Choose an integration pattern: research assistant, dashboard metric, agent input, or alerting threshold
Use cases
- Inform corporate decision intelligence by comparing market-implied probabilities to internal forecasts
- Feed time-stamped market signals into agent memory for retrieval in future decisions
- Build dashboard alerts that notify when prediction market spreads or liquidity cross thresholds
- Evaluate geopolitical or business outcomes through multiple prediction venues before scenario planning
- Assess the reliability of a market signal by comparing it against research, customer data, and filings
- Product managers evaluating external signals for decision dashboards
- Agents and AI systems that need to incorporate market-based forecasts
- Corporate strategists and analysts building decision-intelligence layers
- Risk and scenario-planning teams comparing multiple outcome probabilities
- Researchers studying prediction markets as data sources
prediction-market-oracle-research FAQ
No. Treat market prices as informational inputs reflecting trader beliefs, not ground truth. Always compare against non-market sources and evaluate liquidity, incentives, and resolution rules.
Deep liquidity, tight spreads, established resolution authority, diverse trader base, and alignment with non-market sources (news, filings, internal data). Thin markets, stale prices, and concentrated traders reduce signal quality.
No. This skill is for research and decision intelligence only. If you are considering on-chain or execution-linked systems, run the llm-trading-agent-security check before granting any write authority.
Record both the market-implied probability and your internal forecast (from KPIs, customer data, or research) side-by-side with timestamps. Document where they agree, diverge, and why—this reveals blind spots.
Ignoring liquidity and spreads, treating prices as certainty, missing resolution ambiguity, overlooking geographic or account restrictions, and failing to check for manipulation or concentrated traders.
Full instructions (SKILL.md)
Source of truth, from affaan-m/ecc.
name: prediction-market-oracle-research 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. metadata: origin: ECC
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.
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:
- decision context
- market sources
- signal quality
- comparison sources
- integration recommendation
- caveats
End with:
Prediction-market signals are informational inputs, not investment advice.
Related skills
More from affaan-m/ecc and the wider catalog.
prediction-market-risk-review
Review prediction-market workflows for compliance, safety, data quality, privacy, and execution risk before handling auth or portfolio data.
prisma-patterns
Production Prisma ORM patterns, query optimization, and critical traps for TypeScript backends.
product-capability
Translate product intent into implementation-ready capability plans with explicit constraints and interfaces.
product-lens
Validate product direction and diagnose the "why" before building—run founder-level reviews and pressure-test ideas.
production-audit
Local production-readiness audit for shipped apps—no external data sharing, just risk assessment.
production-scheduling
Expert production scheduling for discrete/batch manufacturing: sequencing, bottleneck resolution, changeover optimization, and disruption response.