QingFeng Li

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.

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• Comparing gene expression between treatment and control
• Accounting for batch effects or age in expression analysis
• Moving R-based DESeq2 workflows to Python

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.

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• Comparing designs with competing cost and performance goals
• Finding solutions that satisfy constraints
• Comparing algorithms on standard benchmark problems

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.

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• Calculate heart rate variability from ECG recordings
• Analyze EEG power across frequency bands
• Compare skin conductance responses to stimuli

Choose a mathematical approach for a problem you do not yet know how to model. Guided questions clarify change, uncertainty, optimization goals, and required precision, leading to a recommended framework, starting equations, tools, and related skills.

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• Choosing a mathematical approach for a real-world problem
• Deciding how to model discrete or continuous change
• Selecting methods for uncertainty or optimization

Design and implement repeatable workflows that clean, encode, transform, and validate raw data for machine learning. Get a preprocessing plan or implementation outline, with clear steps and guidance on where further checks are needed.

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• Preparing raw data for machine learning
• Automating recurring data cleaning
• Planning data extraction, transformation, and loading

Deploy web projects to Netlify with its command-line tool. The skill checks authentication and project settings, links or creates a site, and publishes a preview or production version with deployment URLs and log links.

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• Publishing a web project on Netlify
• Sharing a preview URL before production
• Linking a project to an existing Netlify site

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.

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

Organize complex tasks using three Markdown files for plans, findings, and progress. The skill keeps decisions, discoveries, errors, and test results in project files that an agent can consult when making decisions or resuming work.

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• Planning tasks with multiple stages
• Recording findings during research
• Resuming work after a break

Screen compound collections for drug discovery using established drug-likeness rules, structural warnings, and molecular complexity measures. Detect relevant chemical groups, apply property limits, and generate filtering results to support compound selection and prioritization.

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• Screening compound collections for drug-like candidates
• Detecting potentially problematic structures
• Prioritizing compounds during lead optimization

Audit Node.js processes started by VCO, identify which are managed and still active, and report stale processes eligible for cleanup. Produce Markdown or JSON reports and simulate cleanup while keeping external and unknown processes outside the candidate set.

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• Auditing accumulated Node.js processes after long VCO runs
• Recording VCO-launched processes through a wrapper
• Producing Markdown or JSON process ownership reports

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.

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

Create interactive charts in Python for exploring scientific, statistical, geographic, and financial data. Choose quick plotting or detailed customization, add hover details and zoom controls, and export results as interactive HTML or static images.

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• Exploring measurement relationships with scatter plots
• Comparing distributions across groups
• Inspecting time series with a range slider

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.

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

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.

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

Implement, customize, and debug legends in G2 charts with guidance on rendering, layout calculations, and user interactions. Understand category and continuous legends, adjust their appearance, and investigate sizing or filtering problems.

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• Customize legend placement and appearance
• Investigate legend sizing problems
• Implement filtering through legend selections

Analyze genomic regions with gtars using Python or command-line tools. Find overlaps, generate coverage tracks showing read density, prepare regions as tokens for machine learning, and split single-cell fragment data for further analysis.

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• Finding overlaps between peaks and promoter regions
• Generating BigWig coverage tracks from sequencing fragments
• Converting genomic regions into tokens for machine learning

Find and download public cancer imaging datasets from NCI Imaging Data Commons. Filter scans and pathology images by metadata, preview them in a browser, check licenses, and generate source citations for research or AI training.

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• Select cancer imaging datasets for AI training
• Find scans by cancer type, imaging modality, or anatomical site
• Preview radiology and pathology images in a browser

Convert documents, spreadsheets, images, and audio into structured Markdown for AI processing. Extract tables, recognize text in images, transcribe audio, and batch convert files using command-line tools or Python.

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• Convert PDF papers into Markdown
• Extract Excel tables for analysis
• Recognize text in scanned documents

Create reports, guides, and project documents in Markdown with Mermaid, a text-based diagram format. Use document templates and style guides to explain workflows, relationships, and timelines while keeping text and diagrams editable and suitable for version control.

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• Writing research reports with explanatory diagrams
• Documenting workflows and system relationships
• Preparing project timelines and status reports

Generate and edit images through the OpenAI Image API using a bundled command-line script. Create product shots, illustrations, and covers, replace backgrounds, remove objects, produce transparent images, or generate batches of variations.

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• Creating product photos and website images
• Replacing backgrounds or removing objects
• Making transparent cutouts

Write Python tests with Hypothesis that generate varied inputs and check properties, rules that should always hold. Discover edge cases, reduce failures to simpler examples, and save failing inputs for replay alongside conventional tests.

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• Finding edge cases in parsers and input handling
• Checking mathematical properties across many inputs
• Testing data encoding and decoding round trips

Develop testable scientific hypotheses from observations, preliminary data, and published evidence. The skill compares competing explanations, defines measurable predictions, proposes experiments, and organizes evidence and decision criteria into a structured research report.

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• Developing hypotheses from preliminary data
• Comparing competing explanations for a phenomenon
• Designing experiments to distinguish hypotheses

Handle review and discussion comments on the open GitHub pull request for the current branch. Collect comments, number and summarize the requested changes, then apply fixes for the comments the user chooses using the GitHub CLI.

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• Reviewing comments on the current branch's pull request
• Listing requested changes with numbered summaries
• Applying fixes for selected review comments

Prepare market research reports covering market size, competitors, entry opportunities, and risks. Organize cited evidence, distinguish assumptions from facts, and present charts and strategic recommendations with implementation plans to support business and investment decisions.

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• Estimating market size for a new product
• Comparing competitors and their market positions
• Evaluating opportunities to enter a new market