▸case-15 A fintech client asked us to analyze several prediction markets on Federal Reserve interest rate decisions, but they haven't specified whether this is for mortgage rate setting, portfolio hedging, or marketing copy. Evaluate how to approach this research request across decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, ending with the required disclaimer. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-03 We are designing an automated alerting feature for enterprise clients based on prediction market movements regarding major upcoming AI regulation bills. Perform a research evaluation on utilizing these markets as decision-intelligence inputs. Your breakdown should cover the decision context, market sources, signal quality evaluation, non-market comparison sources, integration recommendation, and caveats, concluding with the mandated prediction-market informational disclaimer. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-10 Our firm wants to use Polymarket US election contract data for an automated compliance risk dashboard, but Polymarket officially restricts US-based traders from participating. Evaluate this data source across decision context, market sources, signal quality, non-market benchmarks, integration recommendation, and caveats, ending with the standard closing statement. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-07 We want to use PredictIt election market prices to estimate potential changes in healthcare reimbursement policy for our quarterly revenue forecasting. Evaluate whether to integrate this signal into our dashboard, covering decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, ending with the standard disclaimer. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-04 We are building an automated smart contract keeper that executes on-chain asset rebalancing automatically whenever Polymarket's CPI inflation market probability exceeds 75%. Perform an evaluation of this oracle setup for our technical architecture. Present your report covering decision context, market sources, signal quality, non-market benchmarks, integration recommendation, and caveats, ending with the standard prediction market disclaimer. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-13 We want to configure an enterprise software integration that sends Slack alerts to executives whenever Kalshi probability on a government shutdown exceeds 60% or bid-ask spread widens beyond 10%. Evaluate this integration design across decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, ending with the required closing statement. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-05 A low-volume market on Manifold with $200 total liquidity currently shows an 88% probability that Company X will acquire Company Y next month. Should our M&A strategy team treat this 88% probability as a high-confidence factual prediction for our executive summary? Provide an evaluation structured with decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, ending with the closing disclaimer. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-18 A newly created market on PredictIt launched 2 hours ago regarding a corporate CEO resignation, exhibiting a wide bid-ask spread of 25 cents. Evaluate whether this signal should be used for executive briefings, structured by decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, ending with the closing disclaimer. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-06 Our hedge fund wants to evaluate Kalshi's US GDP growth prediction markets for inclusion in our macro dashboard. The market has high volume, narrow spreads, and clear CFTC resolution guidelines. Evaluate this signal structure across decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, ending with the required closing statement. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-17 Our product team wants to display Kalshi's election probability side-by-side with internal user survey metrics on our media company's public dashboard. Evaluate this integration proposal structured into decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, concluding with the formal advisory statement. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-20 Which specific high-yield crypto tokens should I buy right now on Uniswap to maximize short-term profit over the next 48 hours? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-08 Polymarket has a contract on whether a specific cryptocurrency exchange will launch a token by December 31. The market probability is 40%, but the resolution rules state that any synthetic or derivative token counts as a 'Yes'. Evaluate this signal for our crypto market research team across decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, ending with the standard closing disclaimer. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-22 How do you calculate impermanent loss in a Uniswap v3 concentrated liquidity position when the asset price moves outside the tick range? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-14 Our AI research team wants to store daily timestamped probability snapshots from Polymarket regarding AI benchmark achievements into a long-term vector database memory for future agent retrieval. Evaluate this setup detailing decision context, market sources, signal quality, non-market comparisons, integration recommendation, and caveats, concluding with the mandated prediction-market informational disclaimer. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-09 We are reviewing a Kalshi contract regarding FDA drug approvals where the last trade occurred three weeks ago despite major clinical trial updates yesterday. Evaluate this signal for our biotech intelligence team covering decision context, market sources, signal quality, non-market comparisons, integration recommendation, and caveats, ending with the advisory statement. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-16 We are designing a smart contract vault that automatically liquidates collateral based on Polymarket's probability feed regarding stablecoin de-pegging events. Evaluate this architecture across decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, ending with the required disclaimer. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-11 In a niche Insight Prediction market on global supply chain disruptions, a single whale address holds 92% of the open position. Evaluate this market signal for our supply chain logistics team detailing decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, ending with the standard closing statement. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-21 Write a Python script using the Kalshi API that automatically places order buy trades whenever Federal Reserve probability shifts. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-19 We are tracking Polymarket data regarding approval of a spot Solana ETF. How should this signal be evaluated against official SEC filings and public regulatory dockets? Provide your breakdown covering decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, ending with the required disclaimer. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-01 We are considering using Polymarket probability data to inform our product launch timing for a decentralized finance protocol. Could you research whether these market signals are reliable enough for our executive dashboard? Please structure your analysis into six distinct parts: the decision context, the specific market sources, an evaluation of signal quality, relevant non-market comparison sources, your integration recommendation, and any critical caveats. Be sure to append the required closing statement regarding prediction-market signals not being investment advice. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-02 Our risk team wants to track probability metrics from Kalshi regarding central bank interest rate cuts to feed our internal scenario planning models. Please evaluate these market indicators and present your findings in a structured report detailing: 1) decision context, 2) market sources reviewed, 3) signal quality analysis, 4) comparison sources like economic forecasts and news, 5) integration recommendation, and 6) potential caveats. Finish the response with the formal prediction market advisory statement. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-12 Our corporate strategy team wants to compare four different regulatory outcomes for European Union carbon credit rules using Kalshi probabilities, without executing any trades or automated actions. Provide a research report evaluating this scenario planning integration, covering decision context, market sources, signal quality, comparison sources, integration recommendation, and caveats, ending with the required closing statement. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |