数据与 AI技能

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

Estimate intervention effects from existing data using causal analysis methods. Set up models, compare observed outcomes with estimates of what would have happened without intervention, check robustness, and interpret fitted results.

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• Estimating intervention effects from existing data
• Comparing outcomes with and without intervention
• Checking the robustness of causal estimates

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.

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

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.

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• Train agents in existing reinforcement learning environments
• Create custom simulation environments
• Tune parallel simulations for higher throughput

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

Implement filtering, aggregation, joins, and file processing with Polars for tabular data that fits in memory. Use lazy evaluation, which optimizes operations before running them, and parallel execution to improve pipelines and migrate pandas code.

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• Migrate slow pandas processing to Polars
• Filter and aggregate CSV or Parquet data
• Join multiple tables and reshape results

Create scientific and statistical charts in Python, including line plots, scatter plots, heatmaps, and multi-panel figures. Adjust colors, labels, and layouts, then export figures as PNG, PDF, or SVG for papers and presentations.

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• Visualizing trends and relationships in research data
• Arranging multiple charts in one figure
• Adjusting colors and labels for readable charts

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.

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

Process and compare mass spectra, measurements used to study chemical compounds, with matchms. Clean peak data, standardize metadata, calculate similarity scores, and search reference libraries for candidate compounds in metabolomics research.

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• Match unknown spectra against reference libraries
• Normalize and filter spectral peaks
• Compare spectra using similarity scores

Write MATLAB or GNU Octave scripts for matrix calculations, statistics, signal processing, and scientific plots. Get help with language syntax, import and export data, or convert code between MATLAB and Python for numerical analysis tasks.

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• Analyzing experimental CSV data in MATLAB
• Writing matrix and differential equation calculations
• Processing signals and plotting frequency components

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

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

Keep existing Bayesian modeling requests working through a compatibility alias that routes to the local pymc skill. Reuses the canonical instructions, templates, references, and diagnostic scripts while preserving the name used in existing calls and catalog entries.

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• Keeping existing skill calls working
• Preserving descriptive names in README and catalog entries
• Sharing templates and diagnostic scripts from pymc

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

Work with microscopy images stored in OMERO through its Python API. Retrieve datasets, analyze pixel values and selected image regions, manage annotations and metadata, and save measurements or automate processing across multiple images.

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• Retrieving microscopy images from OMERO
• Measuring pixel intensity in selected regions
• Saving image tags and analysis results

Analyze relationships between people, objects, or places using Python networks. Build connection diagrams, find shortest paths, identify important nodes and closely connected groups, visualize network structure, and save the resulting data and measurements.

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• Visualize social or citation networks
• Find shortest routes through a network
• Identify important nodes and connected groups

Apply NOWAIT to shorten reasoning model outputs by suppressing reflection words during generation. Includes integration examples for HuggingFace Transformers and vLLM, with guidance on model suitability and potential accuracy loss in distilled models.

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• Reducing tokens used in lengthy reasoning
• Introducing NOWAIT into model inference
• Checking model suitability and accuracy risks

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

Prepare pathology image datasets by detecting tissue in whole slide images and extracting smaller image tiles. Use random, grid, or score-based selection, preview extraction locations, and apply preprocessing filters to review and prepare the resulting tiles.

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• Sampling tissue regions for an initial review
• Extracting tiles across tissue in a grid
• Selecting tiles by nuclei density scores

Develop and review machine learning approaches through at least three rounds of critique and refinement. Compare models with simple reference predictions, question data assumptions, and investigate overfitting, feature choices, and evaluation methods before accepting a solution.

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• Comparing model choices with simple reference predictions
• Investigating gaps between training and test performance
• Reviewing feature choices and data leakage risks

Work through optimization problems using gradients, which indicate how a function changes. The skill guides method selection, step size choices, and convergence checks, with SciPy examples for conjugate gradient and BFGS methods.

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• Choosing a method for an optimization problem
• Adjusting gradient descent step sizes
• Investigating oscillation or slow convergence

Check CPU cores, GPUs, available memory, and disk space before demanding computing tasks. Save a JSON report with recommendations for parallel workers, GPU use, and processing data in chunks when memory is limited.

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• Checking memory before analyzing large datasets
• Identifying available GPUs before model training
• Choosing worker counts for parallel file processing

Evaluate code generation models with HumanEval, MBPP, and multilingual benchmarks. The skill guides test execution and model comparisons using pass@k, an estimate of whether at least one of k generated solutions passes the tests.

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• Compare code generation models on shared benchmarks
• Measure code generation performance across languages
• Evaluate instruction-tuned coding models

Work with geographic points, lines, and areas in Python. Read spatial files, combine datasets by location, calculate areas and distances, transform coordinate systems, and export results as maps or converted files.

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• Aggregate point data by geographic area
• Find buildings near roads
• Create maps colored by population