Aeon Time Series Machine Learning
Skill Verifiziert AktivThis 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.
To empower AI agents with specialized algorithms for sophisticated time series analysis beyond standard machine learning approaches.
Funktionen
- Time series classification and regression
- Forecasting and anomaly detection
- Clustering and segmentation
- Similarity search and pattern discovery
- Scikit-learn compatible APIs
Anwendungsfälle
- Analyzing temporal data with specialized algorithms
- Detecting anomalies or change points in sequential patterns
- Clustering or forecasting time-indexed observations
- Performing univariate and multivariate time series analysis
Nicht-Ziele
- Handling general machine learning tasks outside of time series
- Providing basic data manipulation beyond what aeon offers
- Replacing standard scikit-learn for non-temporal data
Documentation
- info:Configuration & parameter referenceThe SKILL.md details algorithms and provides quick start examples, but does not explicitly document all parameters or configuration precedence for those algorithms.
Versioning
- info:Release ManagementThe SKILL.md frontmatter specifies a license, but no explicit version number is present. The installation command `uv pip install aeon` implies a specific version is installed, but it's not clearly declared in the skill's metadata.
Execution
- info:Pinned dependenciesThe installation instruction uses `uv pip install aeon`, which typically pins dependencies by default. However, a lockfile is not explicitly mentioned as part of the skill's bundle.
- info:Pinned dependenciesThe installation instruction uses `uv pip install aeon`, which typically pins dependencies by default. However, a lockfile is not explicitly mentioned as part of the skill's bundle, and versioning is not explicitly declared.
Practical Utility
- info:Edge casesThe SKILL.md documentation mentions data preparation best practices like normalization and handling missing values, but doesn't explicitly list failure modes with symptoms and recovery steps.
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
Ähnliche Erweiterungen
TimesFM Forecasting
100Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
Alterlab Aeon
98Part of the AlterLab Academic Skills suite. 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.
Fit Drift Diffusion Model
100Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.
Pipeline Forecasting
100Generate predictive pipeline forecasts with confidence intervals and scenario modeling for revenue planning
Scrum Master Expert
100Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation, backlog grooming, story points, burndown charts, blocker resolution, or agile team health. Runs Python scripts to analyse sprint JSON exports from Jira or similar tools: velocity_analyzer.py for Monte Carlo sprint forecasting, sprint_health_scorer.py for multi-dimension health scoring, and retrospective_analyzer.py for action-item and theme tracking. Produces confidence-interval forecasts, health grade reports, and improvement-velocity trends for high-performing Scrum teams.
OraClaw Forecast
100Zeitreihenprognose für KI-Agenten. ARIMA- und Holt-Winters-Vorhersagen mit Konfidenzintervallen. Prognostizieren Sie Umsatz, Traffic, Preise oder beliebige sequentielle Daten. Inferenz unter 5 ms.