Skill UI

Enables Claude to analyze historical time series patterns, select the best forecasting model, and predict future values with confidence intervals for sales, traffic, or other time-dependent metrics.

Geniml builds machine learning workflows around BED and scATAC datasets to learn embeddings for regions, metadata, and cells, enabling similarity searches, clustering, consensus peaks, and downstream analysis of genomic intervals.

High-performance Rust toolkit with Python bindings for genomic interval analysis, covering overlap detection, coverage track generation, ML tokenization, reference sequence handling, fragment processing, and scoring workflows for sequencing datasets.

This skill provides zero-shot time series forecasting using Google's TimesFM foundation model. It forecasts any univariate time series (like sales, sensor data, or weather) without requiring custom model training. The model delivers point forecasts along with calibrated prediction intervals (quantiles), offering a robust and probabilistic view of future trends. It is ideal for rapid, reliable forecasting across diverse time-based datasets.

This skill manages and schedules recurring, autonomous experiments. It allows users to set an experiment to run continuously at predefined intervals (e.g., every 10 minutes, daily, or weekly) using cron scheduling logic. It handles the full lifecycle, including execution, result review, strategy refinement, and state persistence.

This skill provides comprehensive methods to detect Cobalt Strike beacon command-and-control (C2) traffic. It uses advanced network forensics techniques, including analyzing default TLS certificates (e.g., serial 8BB00EE), JA3/JA3S fingerprints, HTTP malleable profiles, and beacon interval jitter, leveraging Zeek logs, Suricata rules, and Python PCAP analysis. It is essential for threat hunters and SOC analysts investigating sophisticated C2 communications.

This tool provides comprehensive network traffic analysis capabilities by querying the Arkime API. Users can search sessions, download PCAPs for forensics, detect sophisticated threats like C2 beaconing (via jitter/interval analysis), spot DNS tunneling patterns, and flag connections to known-bad TLS certificate issuers. Ideal for security assessment and incident investigation.

polars-bio accelerates genomic interval math and bioinformatics I/O by combining Polars, Arrow, and DataFusion to handle BED/VCF/BAM/GFF/FASTA streams, SQL queries, and cloud storage with overlap, coverage, and nearest APIs plus streaming fallbacks.

Use the cron tool to schedule reminders, recurring tasks, or one-time events with simple expressions for intervals, cron syntax, or ISO timestamps; supports listing and removing jobs.

Manage scheduled or recurring tasks using qwenpaw cron commands. Supports listing, creating, updating, pausing, and deleting jobs. Specify execution times, schedules, and agent IDs explicitly. Ideal for daily reminders, periodic checks, or automated agent queries at specific intervals.

Geniml validates genomic interval workflows, BED files, and model compatibility for ML/statistical analysis. Plan Region2Vec/scEmbed runs, inspect tokenizers, and assess consensus universes locally without network requests. Requires Python 3.10+ and uv for reproducible bioinformatics ML pipelines.

Gtars offers local genomic interval models, set algebra, overlap/count operations, consensus and coverage, tokenization, fragment processing, and refget/BEDbase planning across Python, Rust, and CLI.