QingFeng Li

Plan and audit preprocessing for grouped time-series data, such as matches or patient records. Check missing-value handling, scaling scope, feature meaning, and data leakage, where information unavailable at prediction time enters model inputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning missing-value handling within each group
• Choosing within-group or global scaling
• Auditing preprocessing code for data leakage

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Analyze time to events with incomplete observations
• Compare Cox models and Random Survival Forests
• Select features in datasets with many variables

Build and evaluate machine learning models in Python with scikit-learn. Covers classification, numerical prediction, grouping similar data, preprocessing, model comparison, and pipelines that combine data preparation and prediction into a repeatable workflow.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build classification or numerical prediction models
• Group customers with similar characteristics
• Compare models using cross-validation

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Resolving unclear requirements before implementation planning
• Defining error behavior and measurable completion criteria
• Recording agreed answers directly in a specification

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Restructuring PyTorch code into LightningModules
• Building reusable data preparation pipelines
• Configuring training across multiple GPUs or TPUs

Explore research ideas through conversation, question existing assumptions, and connect methods across disciplines. The skill helps identify research gaps, compare ways to combine papers, and summarize promising directions with practical next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explore new research topics and directions
• Find connections across scientific disciplines
• Question assumptions in existing research

Diagnose file write failures by checking permissions, disk space, paths, and file locks. Get corrective steps before retrying, or check conditions before important writes to catch problems such as missing folders and read-only files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating permission errors when saving files
• Checking disk space before saving large files
• Resolving Windows path length problems

Analyze git history to map contributors to security-sensitive files, identify reliance on too few people, and find stale areas. Export CSV and JSON results for ownership reviews, graph exploration, and comparison with CODEOWNERS.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find stale security-sensitive code with few maintainers
• Check reliance on individual authentication or cryptography maintainers
• Compare CODEOWNERS with actual contribution history

Plan experiments, analyze data, and build and evaluate predictive models. The skill also covers explaining findings to stakeholders and designing production workflows for model deployment, monitoring, and retraining to support business decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing experiments and A/B tests
• Preparing model inputs and evaluating predictions
• Planning model deployment and monitoring

Draft and revise scientific manuscript prose from research notes and results. Organize sections, write abstracts and figure captions, integrate citations, and refine terminology and clarity while following study reporting guidelines and journal requirements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning research notes into manuscript sections
• Writing an abstract within journal word limits
• Refining captions and integrating citations

Explain machine learning predictions with SHAP values, which measure how each input contributes to an output. Create charts for individual predictions and overall feature importance, investigate errors, compare models, and examine differences between groups.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explaining an individual prediction
• Visualizing overall feature importance
• Investigating misclassifications and data leakage

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking and filtering single-cell RNA-seq data
• Visualizing cell groups with UMAP or t-SNE
• Finding marker genes and annotating cell types

Build tests with fast-check for TypeScript and JavaScript or Hypothesis for Python. Generate varied inputs to check rules code should always satisfy, discover edge cases, and simplify failing inputs into small examples for debugging.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking that encoding and decoding restore the original data
• Testing rules that sorting must preserve
• Generating inputs with complex constraints

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a new machine learning pipeline
• Creating reproducible training workflows
• Adding quality checks before model deployment

Connect external services and databases to Claude Code plugins through MCP, a protocol for exposing tools to agents. The skill guides server configuration, connection choices, authentication, tool permissions, and testing so plugins can use those services.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a local MCP server to a Claude Code plugin
• Configuring OAuth or token authentication for a service
• Building a plugin that uses multiple services

Find current official documentation for building with OpenAI products and APIs. Provides source-linked guidance on Codex, APIs, SDKs, and model capabilities or limits, using dedicated documentation tools before searching official OpenAI websites.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check how to use the Responses API
• Look up Codex features and usage
• Research app development with Apps SDK or Agents SDK

Provides a starting structure for work on formal logic and mathematical foundations, with source checks and a review of unresolved gaps. The material is a legacy template awaiting research and validation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing sources for formal logic topics
• Checking unresolved gaps before using topic modules
• Planning validation of mathematical foundations material

Develop healthcare prediction models from electronic health records and physiological signals. Use PyHealth to prepare data, train models for tasks such as mortality or readmission prediction, and evaluate accuracy, fairness, and how predictions can be explained.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building mortality prediction models with MIMIC-IV
• Training and evaluating hospital readmission models
• Developing medication recommendation models with SafeDrug

Reviews machine learning preprocessing, features, and data splits for data leakage: information unavailable when making a real prediction. Identifies risky statistics, future data, and validation setups, then suggests corrections using only appropriate training information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing normalization and missing value imputation
• Checking feature selection and target encoding
• Finding future information in time series features

Supports pathology image analysis in Python, from loading whole-slide images to normalizing stain colors, identifying cell nuclei, mapping spatial relationships, and training machine learning models. It also covers multiplex imaging and marker measurements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preprocessing stained tissue images
• Detecting and segmenting cell nuclei
• Analyzing spatial relationships between cells

Find and retrieve AI prompt templates through prompts.chat, filtering by keywords, categories, tags, or media type. Improve existing prompts for text, image, video, or sound tasks, with an explanation of the changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find prompt templates for code reviews
• Browse prompts for writing tasks
• Retrieve a specific prompt by ID

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing Prowler feature documentation
• Standardizing language in tutorials
• Editing API and SDK references

Calculate defined business, statistical, or quality metrics from available data and test results. It provides formulas and reproducible calculation steps, with checks on units, denominators, and which records are included in each calculation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate a defined business metric from available data
• Convert raw counts into rates, ratios, or differences
• Check formula consistency across multiple outputs

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Keeping older skill requests working
• Reusing documentation references and scripts
• Maintaining continuity in skill catalogs