“agent-skill”的搜索结果

Check physical units, propagate measurement uncertainty through calculations, and format results with meaningful precision. Local tools also inspect Python code for unit and uncertainty mistakes and assess whether calculated magnitudes are physically plausible.

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• Convert units while carrying measurement uncertainty
• Build an uncertainty budget from calibration data
• Audit Python calculations for unit and uncertainty errors

Work with large scientific arrays by storing data in smaller blocks and reading selected portions. Guides compression, cloud storage, format migration, and NumPy, Dask, or Xarray integration while preserving dimensions, units, and other metadata.

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• Reading selected regions of large scientific arrays
• Choosing data blocks and compression settings
• Working with scientific arrays stored on S3 or GCS

Build graph neural networks, models that learn from connected nodes, with PyTorch Geometric. Supports node and graph classification, link prediction, custom datasets, and training approaches for large graphs or graphs containing multiple types of nodes and connections.

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• Build node and graph classification models
• Predict links between nodes
• Train models on graphs with multiple node and connection types

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

Run protein structure prediction, molecular design, and docking tools on Tamarind Bio without a local GPU. Guides tool selection, input validation, job submission, batch processing, and result retrieval through its REST API or MCP server.

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• Predicting protein structures in the cloud
• Running binder or antibody design tools
• Submitting batches of sequences for analysis

Build and evaluate time-to-event models with scikit-survival, including records whose events remain unobserved when follow-up ends. Keep preprocessing separate from evaluation data, compare models, predict survival probabilities, and summarize censoring-aware metrics.

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• Validate event indicators and follow-up times in local CSV data
• Train survival models without leaking evaluation data
• Evaluate survival probabilities with censoring-aware metrics

Store genomic variant records in TileDB-VCF, add samples over time, and retrieve selected samples or genomic regions. Supports batch reading, allele statistics, quality checks, and VCF/BCF export, with workflows for local storage and TileDB Cloud.

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• Build a variant dataset and add samples incrementally
• Extract variants for selected samples and genomic regions
• Review allele statistics and sample quality

Estimate RNA velocity, the modeled direction of gene expression change, from single-cell spliced and unspliced counts. Fit models, inspect gene-level plots, visualize trajectories, and explore relative timing and associated genes while checking model limitations.

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• Explore hypotheses about directions of cell-state change
• Inspect gene-level RNA velocity model fits
• Estimate relative cell ordering with a dynamical model

Prepare papers, research posters, and grant documents for a specific journal, conference, or funder. The skill guides official template selection, checks current submission requirements, adapts writing, and inspects PDF page counts and embedded fonts.

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• Preparing a paper for a journal or conference
• Checking page limits and anonymous review requirements
• Drafting grant proposal components

Fit probabilistic models to single-cell measurements for integration across experiments, cell annotation, combined RNA and protein or ATAC analysis, and gene expression comparisons. The skill covers input requirements, training checks, reference mapping, and interpretation of model uncertainty.

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• Integrating single-cell RNA data across experimental batches
• Assigning cell labels using partial annotations or a reference
• Analyzing RNA with protein or ATAC measurements

Create static statistical charts from pandas tables to explore distributions, relationships, and group differences. Supports scatter plots, regression displays, pair plots, and heatmaps, with explicit choices for aggregation, uncertainty, missing data, and visual styling.

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• Exploring distributions and relationships in tabular data
• Comparing group differences with box or violin plots
• Visualizing correlation matrices as heatmaps

Use SymPy to simplify expressions, solve equations, and calculate derivatives, integrals, and matrices with exact symbolic results. Specify assumptions, verify solutions, and turn mathematical expressions into numerical functions, source code, or LaTeX output.

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• Solving equations and checking their solutions
• Calculating symbolic derivatives, integrals, and limits
• Finding matrix eigenvalues with exact arithmetic

Stress-test research plans by comparing possible scenarios, such as sample losses, unavailable materials, or methods failing to transfer. Record assumptions and evidence, compare response options, and define contingency plans and observable triggers for changing course.

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• Plan responses to higher-than-expected sample losses
• Prepare backups for unavailable reagents or datasets
• Compare research options under resource constraints

Research current web information with Parallel CLI and organize findings with supporting sources. The skill covers search, page extraction, detailed reports, adding sourced fields to existing lists, finding matching entities, and recurring monitoring of web changes.

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• Research current information and locate academic sources
• Extract content from public webpages and PDFs
• Add sourced information to company or product lists

Solve optimization problems in Python with one or several competing goals. Define constraints, compare search algorithms, visualize trade-offs, and assess candidate solutions before choosing an option that fits your priorities.

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• Comparing designs with competing cost and performance goals
• Searching for solutions under explicit constraints
• Benchmarking optimization algorithms

Read, analyze, modify, and visualize molecular structures with RDKit in Python. Calculate molecular properties, search for similar structures or specific structural patterns, apply reaction rules, and generate 2D or 3D coordinates with detailed control over processing.

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• Calculating properties for a collection of molecules
• Searching for molecules with similar structures
• Filtering molecules by specific structural patterns

Prepare constructive review drafts for scientific manuscripts, protocols, preprints, and proposals. Organize checks of claims, evidence, methods, statistics, reproducibility, ethics, figures, and citations into specific comments that a responsible human reviewer can verify and revise.

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• Draft evidence-based comments on a scientific manuscript
• Check methods, statistics, and reproducibility reporting
• Choose reporting guidelines and identify missing information

Work with genomic regions in Polars tables to find overlaps, nearby regions, covered bases, and gaps. Read and write bioinformatics files, query data with SQL, and check coordinate conventions when joining datasets or migrating from bioframe.

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• Finding variants that overlap genomic regions
• Merging regions and measuring covered bases
• Summarizing sequencing read depth

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

Design and review PCR and RT-qPCR primers, short DNA sequences used to start amplification. Assess predicted structures and unintended products under explicit reaction conditions, with reports that track reference sequences, coordinates, and search limits.

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• Design new PCR or RT-qPCR primer pairs
• Review existing primers for structure and unintended products
• Plan exon-junction or isoform-specific assays

Plan and review PufferLib reinforcement learning workflows, where agents learn through rewards. Covers version-specific environment checks, parallel execution, training and evaluation plans, plus checkpoint inspection before loading saved models.

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• Choose APIs for a specific PufferLib version
• Check environment behavior before training
• Create training and evaluation plans with separate seeds

Calculate equilibrium phases, their molar fractions, and compositions from a local thermodynamic database using pycalphad. Compare temperatures, check material balance and sampling stability, and export results with recorded conditions and database identifiers.

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• Calculate equilibrium phase fractions for an alloy composition
• Compare stable phases across specified temperatures
• Check material balance and sampling stability

Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.

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• Filtering and summarizing large CSV files
• Migrating pandas processing code to Polars
• Joining and reshaping multiple tables

Retrieve, validate, and export protocols.io experimental procedures while preserving version and attribution details. Check saved JSON offline, save protocol PDFs, or prepare reviewable plans for updates and publication; bundled scripts do not execute changes.

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• Retrieve a specific version of an experimental protocol
• Validate saved protocol JSON offline
• Export a protocol as a PDF