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Explain how input features contribute to machine-learning predictions using SHAP. Select an explanation method and reference data, validate contributions against model outputs, and create charts for individual predictions or patterns across samples, including models with multiple outputs.

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• Explaining the feature contributions behind one prediction
• Visualizing attribution patterns across evaluation samples
• Selecting and explaining a class in a multiclass model

Analyze experimental or survey data with guidance on choosing statistical tests, checking assumptions, estimating effect sizes and uncertainty, planning sample sizes, and writing results in APA style, a standard format for research reporting.

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• Comparing outcomes between experimental groups
• Examining relationships in survey data
• Planning the number of study participants

Reduce data with many features into fewer dimensions using UMAP. Create 2D or 3D views, prepare inputs for clustering, compare parameter settings, and project new samples while checking whether the results remain stable and meaningful.

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• Visualizing data with many features in 2D or 3D
• Preparing reduced features for HDBSCAN clustering
• Guiding dimensionality reduction with partial labels

Analyze single-cell RNA-seq data with Scanpy, from quality checks and normalization to cell grouping, exploratory marker genes, and plots. Bundled scripts also prepare sample-level count summaries and support conversion of R objects to h5ad.

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• Check data quality and normalize single-cell measurements
• Visualize cell groups with UMAP or t-SNE
• Explore marker genes and review cell-type labels

Find biological pathways and functions enriched in gene lists or ranked gene data. Choose an appropriate statistical method, align gene identifiers, define comparison backgrounds, and turn results into interpretable tables and plots with multiple-testing corrections.

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• Finding enriched pathways among differentially expressed genes
• Running GSEA on a complete ranked gene list
• Calculating gene-set scores for individual samples or cells

Process paired-end 16S sequencing reads in QIIME 2 to produce ASVs, distinct sequence variants, and taxonomic assignments. Check input files, primer settings, and read retention while preserving artifacts that record the processing history.

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• Validate paired FASTQ files and sample metadata
• Remove primers and denoise reads with DADA2
• Assign taxonomy using a compatible classifier

Process mass spectrometry data using scripts for format conversion, signal cleanup, peak detection, and quantification across samples. Prepare protein and metabolite analyses with identification filtering, candidate annotations, data tables, and plots.

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• Inspect, convert, and preprocess mass spectrometry files
• Build quantitative tables across multiple samples
• Find metabolite candidates from accurate mass

Analyze biological sequences and microbial communities in Python. Process and align DNA, RNA, or proteins, build evolutionary trees, calculate diversity, visualize sample differences, test group differences, and convert common biological file formats.

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• Calculate and compare microbiome diversity
• Align DNA, RNA, or protein sequences
• Build and compare evolutionary trees

Match scientific labels to ontology identifiers, standardized IDs for concepts such as tissues, cell types, and diseases. Checks existing IDs, labels, obsolete terms, and prefixes to support metadata annotation, submission preparation, and table audits.

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• Annotating tissue, cell type, or disease fields with standard IDs
• Auditing metadata tables for incorrect IDs and labels
• Checking obsolete terms and available replacements

Process next-generation sequencing data with deepTools: convert alignment files into coverage tracks, normalize signals, compare samples, and check quality. Create heatmaps and profiles around genes or peak regions for ChIP-seq, RNA-seq, and ATAC-seq experiments.

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• Converting BAM files to normalized bigWig tracks
• Checking sequencing quality and replicate agreement
• Plotting heatmaps around transcription start sites

Automate lab data tasks in Benchling using its Python SDK and REST API. Import and export sequences, inspect sample inventory, and work with electronic lab notebooks and workflow tasks while checking schemas, permissions, and operation results.

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• Importing DNA sequences from FASTA files
• Auditing sample inventory at a storage location
• Exporting sequences for a specific schema to CSV

Review supplied writing through twelve DHDNA dimensions, a framework for describing reasoning and expression. Receive observations supported by quotations, optional scores, and comparisons between samples, with clear limits separating textual patterns from psychological traits.

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• Reflect on reasoning patterns in your writing
• Compare argumentation across selected texts
• Review how a text expresses assumptions and uncertainty

Organize experiment photos, audio, and notes into Markdown logs with structured metadata. Assign experiment and sample batch IDs, archive original attachments, and optionally connect records to Feishu group messages or an Obsidian notebook.

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• Turning experiment photos and voice notes into consistent logs
• Tracking the same sample batch across multiple experiments
• Archiving original files with links from experiment logs

Review and improve statistical reporting in manuscript methods, results, and figure captions. It checks sample definitions, repeated measurements, tests, and uncertainty, then provides revised wording and identifies missing information or issues reviewers may raise.

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• Reviewing a manuscript’s statistical methods section
• Checking sample sizes and repeated measurements
• Aligning figure captions with reported statistics

Plan A/B tests to compare page or messaging changes. Helps define hypotheses, choose success metrics, estimate sample sizes, allocate traffic, and interpret results, while also organizing a repeatable process for prioritizing and documenting experiments.

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• Compare headline or button-copy variants
• Plan sample sizes and test duration around available traffic
• Analyze results alongside metrics that must not worsen

Turn a supplied PowerPoint template into a reusable agent skill. Analyze layouts and content boundaries, bundle the template with self-contained instructions, validate it with a sample presentation, and package the skill for later presentation creation.

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• Turning a company PowerPoint template into a reusable skill
• Documenting layouts and safe content areas
• Bundling a template with presentation creation instructions

Observe malware in an isolated Cuckoo Sandbox environment and review its processes, network traffic, and system changes. Use the findings to trace infection stages, identify persistence mechanisms, and extract indicators of compromise for behavioral reports.

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• Investigating suspicious files after static analysis
• Tracing multi-stage infections and persistence mechanisms
• Extracting indicators of compromise from observed behavior

Review Cuckoo or AnyRun behavior reports for signs that malware is avoiding an analysis environment. Identify timing checks, virtual machine detection, and user activity checks, then produce a JSON report with evidence, technique mappings, and an evasion sophistication score.

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• Investigating malware that stays inactive in a sandbox
• Reviewing behavior reports for analysis evasion
• Developing detections for anti-analysis behavior

Extract Cobalt Strike Beacon settings from executable files and memory dumps to identify command-and-control servers, communication patterns, and identifying markers. Use the findings to compare samples, investigate incidents, and develop file and network detection rules.

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• Extract Beacon settings from suspicious files
• Identify command-and-control servers and traffic patterns
• Compare samples using watermark identifiers

Analyze Go malware binaries in Ghidra by extracting build metadata, function names, dependencies, and relevant strings. Use GoResolver to help recover obscured names and organize functions so analysts can focus their reverse engineering work.

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• Investigating the structure of a Go malware sample
• Recovering function names from stripped or obfuscated binaries
• Identifying embedded Go modules and dependencies

Plan A/B tests that compare page or product changes, with clear hypotheses, sample sizes, success metrics, and traffic allocation. Get a structured test plan and guidance for interpreting results and deciding what to try next.

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• Comparing signup button copy
• Planning a pricing page experiment
• Estimating sample size and test duration

Set brand styling from a website link or a plain-language description, preview it on a sample page, and save preferred delivery formats so future plugin pages and documents follow the chosen look and format.

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• Set brand styling from a website link
• Update colors and logos after rebranding
• Preview report styling before saving

Run nf-core workflows on local sequencing files or public GEO/SRA datasets to analyze gene expression, genetic variants, or chromatin accessibility. The skill guides environment checks, sample table preparation, workflow configuration, execution, and output verification.

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• Analyze gene expression from RNA-seq data
• Identify genetic variants in WGS/WES data
• Analyze chromatin accessibility from ATAC-seq data

Turn CSV files, pasted data, or query results into an HTML dashboard that opens in a browser. Combine key metrics, interactive charts, filters, and sortable tables in one report, with sample data available when real data is missing.

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• Create a dashboard of key metrics for executives
• Turn query results into a shareable report
• Review team metrics with filters