Skill UI

Toolkit for Python-based survival and time-to-event modeling with scikit-survival, supporting Cox models, ensemble learners, SVMs, and evaluation metrics for censored data.

Probabilistic deep-learning framework built on PyTorch for single-cell omics; handles batch correction, integration, multimodal and spatial data, differential expression, and transfer learning with unified APIs.

Automates iterative use of the plugin-dev skill-reviewer agent to diagnose and fix Claude Code SKILL.md issues, driving repeated review/fix cycles until standards are met before emitting the completion marker.

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

Guides hypothesis testing, regression, correlation, and Bayesian analyses with automated assumption checks, diagnostics, power planning, and APA-style reporting so researchers can choose proper tests, interpret effect sizes, and document academic findings.

Statsmodels is a Python library for detailed regression and time-series models, giving econometrics-grade inference, diagnostics, residual analysis, model comparison, and forecasting tools when you need publication-ready tables and rigorous testing.

Wraps Google's TimesFM foundation model for zero-shot univariate time series forecasting, returning point forecasts with quantile intervals while enforcing a preflight RAM/GPU/disk checker before loading weights, ideal for sales, sensors, and energy data.

Detects missing zeroization of secrets and flags compiler-optimized removals with mandatory assembly/IR evidence, helping auditors verify C/C++/Rust cryptographic code through dataflow, CFG, and runtime validation.

Audits and improves local SEO for law firms and legal pros, focusing on GBP, directories, E-E-A-T trust signals, practice/location pages, and reputation to boost visibility in local pack and organic search.

Transforms a single idea or asset into platform-native social posts, threads, scripts, calendars, and repurposed content for X, LinkedIn, TikTok, YouTube, and newsletters while keeping hooks, CTAs, and tone tailored to each destination.

Apify Actorization is a process that transforms existing, standalone software or scripts (like CLI tools) into standardized, reusable serverless applications called Actors. Actors are packaged as Docker images, accepting structured JSON inputs and producing structured JSON outputs. This skill guides developers on migrating projects to the Apify platform, ensuring compatibility, and enabling scalable, event-driven data processing pipelines.

A comprehensive tool designed to analyze and understand audience demographics, engagement patterns, and behavior across major social media platforms including Facebook, Instagram, YouTube, and TikTok. By utilizing specialized Apify Actors, it extracts structured data on follower metrics, post engagement, and user behavior, providing deep insights essential for targeted marketing and content strategy development. Ideal for market researchers and digital marketers needing comprehensive user profiling.