Review papers, research proposals, and literature reviews with the ScholarEval framework. Assess research questions, methods, analysis, writing, and references, then produce evidence-based feedback with prioritized improvements and optional scores for each relevant dimension.
Guides single-cell RNA-seq analysis, which examines gene activity in individual cells. Covers data loading, quality checks, clustering, marker genes, cell type annotation, and plots, with AnnData handling and scVI/scANVI workflow planning.
Build research slides from text sources using Slidev, Marp, Reveal.js, or Quarto. Keep source files manageable, reuse exported figures, and produce reproducible PDF presentations with a clear sequence and one conclusion per slide.
Clarify an existing feature specification before implementation planning. The skill prioritizes consequential gaps, asks focused questions one at a time, updates the specification with accepted answers, and summarizes unresolved decisions and recommended next steps.
Use SAELens to train and analyze sparse autoencoders, which break model activations into a small set of interpretable features. Explore pretrained features, evaluate reconstruction quality, troubleshoot training, and test how individual features influence generated text.
Bridge `/speckit.*` commands to `/vibe` execution guidance in Codex. A PowerShell router preserves the familiar command model and leaves official spec-kit prompts unchanged, helping carry existing workflows into `/vibe-first` execution.
Find reusable Agent Skills by keyword, category, or tag through prompts.chat. Review descriptions and included files, retrieve a chosen skill, and install its instructions, reference documents, scripts, and configuration files for Claude.
Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.
Analyze how long events take to occur, including records where observation ends before the event. Use Python to prepare data, fit and compare survival models, and evaluate predictions with measures suited to incomplete observations.
Structure PyTorch training code with PyTorch Lightning, separating model logic, data preparation, and training settings. Use templates and configuration examples to add checkpoints, experiment logs, and distributed training that runs across multiple GPUs or TPUs.
Create focused checklists to review requirements before implementation. Using project specifications and plans, the skill turns gaps, vague wording, conflicting requirements, and missing scenarios into questions that help authors and reviewers assess specification quality.
Review specifications, plans, and tasks together before implementation. Identify missing task coverage, duplicate requirements, unclear wording, and conflicting decisions, then receive a prioritized report with recommendations without changing project files.
Process physiological recordings in Python, including heart, brain, breathing, and muscle signals. Supports signal cleaning, heart rate variability measurements, stimulus response analysis, and combined analysis of multiple signal types.
Search scholarly literature in OpenAlex by topic, author, or institution. Explore citation counts and publication trends, find freely accessible papers, and export research records to CSV for further analysis.
Keeps existing requests for OpenAI documentation guidance working by directing them to the local openai-docs skill. It reuses the canonical instructions, references, and scripts while preserving compatibility with older callers.
Analyze bulk RNA-seq counts in Python to find genes whose expression differs between conditions. Prepare data, account for batch effects, adjust statistical results for multiple testing, and export tables with volcano or MA plots.
Design and automate machine learning workflows from data preparation through training, validation, deployment, and monitoring. Provides guidance and templates for reproducible experiments, quality checks, gradual model rollouts, and restoring a previous model when problems occur.
Work with DICOM, a standard medical imaging format, in Python. Read and edit scan metadata, extract image pixels, anonymize patient information, convert scans to common image formats, and process compressed files or image series.
Search biomedical literature in PubMed using combined conditions and MeSH, its standardized subject terms. Build search strategies, retrieve abstracts and citation records through the REST API, and export results for reference management or analysis.
Build and train reinforcement learning agents, which learn actions through rewards. The skill guides custom environment creation, parallel simulation tuning, integration with existing environments, and policy development for image or sequential observations.
Use pymoo in Python to optimize competing goals such as cost and performance under constraints. Find and visualize trade-off solutions, compare algorithms, and select candidates according to preferred priorities.
Analyze propositional formulas and arguments using truth tables, deduction rules, and branching proof trees. Classify formulas as always true, always false, or dependent on their inputs, and check whether an argument follows from its premises.
Turn existing logs, measurements, or user reports into a prioritized performance improvement plan. It ranks frontend, backend, database, and infrastructure changes by expected impact and effort, and suggests validation steps for checking whether improvements worked.
Write and edit Prowler documentation using consistent language and formatting. The skill covers feature guides, tutorials, API references, and release notes, with rules for terminology, headings, inclusive wording, search visibility, and reusable MDX documentation components.
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