Results for “aeo”

Tracks DeFi positions across DEX pools, lending markets, and vaults, alerting only on meaningful changes like APR drops, utilization spikes, or health factor approaching liquidation.

Monitors EVM blockchain addresses and contracts for large transfers, new approvals, contract upgrades, unusual gas spends, MEV interactions, and first-time interactions with new contracts.

Monitors a watchlist of Polymarket markets and surfaces meaningful shifts in price, volume, comments, or resolution proximity, with position context and optional Bankr integration.

Monitors a watchlist of Kalshi prediction markets for price moves, volume spikes, resolution proximity, and kill-criterion triggers, with cross-venue arbitrage detection against paired Polymarket markets adjusted for fees and slippage.

Generates at most one token recommendation and one prediction-market pick per run, each with a falsifiable thesis, entry, sizing, and kill criterion. Returns NO_PICK when no candidate meets the bar.

Ranks weekly token unlocks by Absorption Ratio (unlock value / 7d avg volume) instead of supply percentage, with per-event cliff vs linear classification, recipient category, and a one-line market read.

Delivers a daily DeFi regime verdict (RISK-ON/NEUTRAL/RISK-OFF) from five named inputs, top TVL movers with causal reasoning, and a sustainable-vs-incentive yield split to distinguish real product-market fit from emissions-pumped APY.

Logic (acao-logic)

Paseo reference for managing agents and worktrees. Load whenever you need to create agents, send them prompts, or manage worktrees.

Detects outbound HTTP clients constructed without explicit timeouts in AEM Cloud Service code and applies mechanical fixes with CSO-aligned defaults to prevent request-thread pool saturation.

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Filters the last 24 hours of Hugging Face Papers to surface one must-read paper with a structured brief covering the central claim, why it's worth an hour, where it might be wrong, and a time-budgeted read order.

Logic (aeao-logic)

Discover, compare, and research autonomous AI agents, frameworks, and tools using the AgentFolio directory at agentfolio.io.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Esta skill deve ser usada para tarefas de machine learning em séries temporais, incluindo classificação, regressão, clustering, forecasting, detecção de anomalias, segmentação e busca de similaridade. Use quando trabalhar com dados temporais, padrões sequenciais ou observações indexadas por tempo que requerem algoritmos especializados além de abordagens padrão de ML. Particularmente adequada para análise univariada e multivariada de séries temporais com APIs compatíveis com scikit-learn.