QingFeng Li

Build searches that find documents by meaning and support recommendation features using vector databases. Includes implementation patterns for storing and retrieving vectors, combining meaning with keyword matches, and tuning search speed and result quality.

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• Build document search based on meaning
• Implement retrieval for RAG, AI answers grounded in retrieved documents
• Combine keyword and meaning-based search

Analyze biological data in Python, from DNA, RNA, and protein sequences to evolutionary trees and microbial communities. Read and convert files, compare diversity within and between samples, and test differences between groups.

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• Processing DNA, RNA, and protein sequences
• Building and comparing evolutionary trees
• Comparing microbial community diversity

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.

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• Using existing `/speckit.*` commands with Codex
• Getting `/vibe` guidance for an implementation command
• Checking the router's command list

Find academic papers, statistics, and technical documentation with citations for writing and fact checking. The skill selects a search or reasoning model based on the question, supporting both straightforward lookups and comparisons across studies.

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• Finding papers to cite in a manuscript
• Checking claims and statistics against research
• Comparing findings across multiple studies

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.

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• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

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.

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

Capture desktop screenshots on macOS, Linux, and Windows. Save the full screen, a selected window, or a region defined by coordinates, with support for a specified file path, the default location, or temporary storage.

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• Saving a screenshot of the entire desktop
• Capturing a specific app or window
• Saving only a region defined by coordinates

Build image and video AI systems for object detection and segmentation, which identifies separate image regions. The skill covers custom model training, dataset pipelines, inference optimization, real-time processing, and deployment and monitoring in production.

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• Developing object detection and image segmentation systems
• Training custom vision models with project data
• Optimizing inference for real-time video processing

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.

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

Create scientific plots for manuscripts with consistent layouts, accessible colors, and error bars showing uncertainty. Use matplotlib, seaborn, or plotly to prepare multi-panel figures and export files with publication-specific sizes and resolution.

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• Prepare figures for journal submission
• Arrange multiple plots with consistent styling
• Improve color and grayscale readability

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.

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• Finding a code review skill
• Searching for testing skills
• Retrieving all files for a specific skill

Prepare a complete scholarly publication package, from manuscript sources and submission attachments to reviewer responses and final files. Organize journal or conference requirements, figure specifications, revision tracking, and repeatable build steps around one authoritative manuscript source.

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• Preparing a journal or conference submission package
• Organizing point-by-point responses to reviewers
• Checking final files before publication

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.

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• Manage research slide versions as text
• Reuse original figures without screenshots
• Connect reports and slides through Quarto

Manage repeated work on a task in Codex with iteration limits, completion conditions, and saved progress. Advance iterations manually with the default engine, or use the optional open-ralph-wiggum command-line tool for automatic iteration.

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• Starting repeated work with an iteration limit
• Advancing to the next iteration manually
• Checking the current loop state

Create or improve Claude skills that package specialized knowledge, repeatable workflows, and tool instructions. The guide covers concise instructions, reusable scripts and resources, template initialization, validation, and packaging into a distributable .skill file.

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• Turning recurring work into a reusable Claude skill
• Organizing company knowledge and API references
• Improving an existing skill after practical use

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.

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

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.

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

Create scientific diagrams from written descriptions using Nano Banana Pro. Gemini 3 Pro reviews accuracy, labels, and layout, triggering regeneration when scores fall below the chosen document threshold, up to the iteration limit.

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• Creating neural network architecture diagrams for papers
• Illustrating biological signaling pathways
• Preparing study participant flowcharts

Use RDKit in Python to read molecular structures, calculate properties such as molecular weight, find similar compounds or specific structural features, and generate molecular drawings and 3D coordinates for chemistry research.

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• Calculating properties for a list of molecules
• Finding compounds similar to a reference molecule
• Filtering molecules by a specific structural feature

Review code for security risks in authentication, APIs, input handling, and data access. Identify issues such as exposed secrets, injection, and missing access checks, then report severity, file evidence, proposed fixes, and verification steps.

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• Reviewing authentication and access controls
• Checking for exposed secrets and unsafe logs
• Auditing input handling and file uploads

Evaluate existing code review comments against the actual codebase before making changes. The skill guides clarification, technical responses, prioritized fixes, and individual testing, helping assess whether suggestions fit current behavior and compatibility requirements.

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• Assessing CodeRabbit or GitHub review comments
• Clarifying ambiguous requests before making changes
• Explaining why a suggested change would break compatibility

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.

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• Review a paper before journal or conference submission
• Assess research questions and methodology
• Evaluate the coverage and synthesis of a literature review

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.

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

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.

中文简介正在准备中。当前显示英语简介。

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