数据与 AI技能

Inspect supported local scientific files before modeling, summarize structure and missing values, and flag possible data leakage or outlier effects. Produces bounded aggregate reports with identifiers obscured and provides a structured analysis report template.

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• Check local CSV or TSV data before modeling
• Audit missing values and overlap between data splits
• Compare outlier influence and transformation sensitivity

Prepare bulk RNA-seq sequencing files or quantification outputs for gene-level comparisons. Check sample information and data quality, configure processing workflows, and assemble validated count tables and metadata for downstream differential expression analysis with PyDESeq2.

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• Preparing gene count tables from FASTQ files
• Configuring an nf-core/rnaseq workflow
• Combining Salmon, STAR, or featureCounts outputs

Inspect FCS files from flow cytometry, a method for measuring cell properties, using FlowIO. Extract measurement data into NumPy arrays, review channel metadata, export tables, and create FCS 3.1 files for downstream work.

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• Inspect measurement settings and channel labels in FCS files
• Extract measurement data into NumPy arrays or CSV tables
• Read legacy FCS files containing multiple datasets

Prepare genomic interval data for machine learning by checking BED files, reference genome consistency, and model compatibility. Plan Region2Vec or scEmbed workflows and assess shared reference regions using local validation and planning helpers.

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• Check BED coordinates and reference genome consistency
• Plan Region2Vec or scEmbed training
• Inspect model and tokenizer compatibility

Retrieve and combine protein annotations, biological pathways, and chemical identifiers from services such as UniProt, KEGG, and UniChem using Python. Supports cross-database lookups and batch identifier conversion while preserving multiple matches and failed requests.

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• Collect protein sequences and functional annotations
• Retrieve pathway information for genes
• Match compound identifiers across databases

Convert genomic positions between file conventions, standardize variant representations, and check reference genome or chromosome naming conflicts. The skill helps investigate one-base shifts and compare individual variants against a reference sequence before combining analysis files.

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• Converting coordinates between BED and GFF/GTF
• Standardizing and comparing insertion or deletion records
• Checking reference genome and chromosome naming conflicts

Implement and debug astronomical data analysis with Astropy in Python. Work with physical units, celestial coordinates, observation files, tables, precise time conversions, and cosmological models to prepare data and calculate astronomical quantities.

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• Convert celestial coordinates and physical units
• Read and edit FITS observation files
• Match objects across astronomical catalogs

Process DNA, RNA, and protein sequences with Biopython, convert biological files, and retrieve NCBI records. Supports batch workflows for sequence comparison, searches for similar sequences, protein structure analysis, and evolutionary trees.

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• Convert GenBank files to FASTA
• Calculate sequence length and GC content in batches
• Retrieve sequences and publication records from NCBI

Process molecular datasets in Python through a simpler interface to RDKit, a chemistry toolkit. Convert and standardize structures, calculate molecular properties, compare and cluster compounds, generate 3D conformations, and visualize results for drug discovery workflows.

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• Load and standardize molecular datasets
• Calculate molecular properties and similarities
• Cluster compounds and select diverse subsets

Analyze DepMap cancer model data to rank genes required for cell survival, compare groups defined by biological markers, and explore drug responses. Release tracking and statistical checks help organize evidence for research hypotheses.

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• Ranking gene dependencies in cancer models
• Comparing groups defined by biological markers
• Finding genes with similar dependency patterns

Use Dask to process tables, numerical arrays, and file collections in pieces across CPU cores or machines. Helps choose data structures, size memory-friendly chunks, coordinate dependent tasks, and diagnose slow or memory-heavy workflows.

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• Aggregate CSV or Parquet files that exceed available memory
• Process large scientific arrays in chunks
• Clean JSON records and logs before analysis

Use hosted DNA models to predict gene structures, expression levels, and regions that regulate gene activity. Start from a gene symbol, genomic coordinates, or a DNA sequence, without installing model weights or running a local GPU.

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• Finding promoter regions in a DNA sequence
• Predicting splice donor and acceptor sites
• Estimating gene expression for a specified cell type

Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.

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• Estimate metabolic fluxes from carbon-13 labeling
• Validate atom mappings and measurement inputs
• Simulate labeling for proposed reaction rates

Analyze flow cytometry data in Python by correcting fluorescence spillover and applying gates that select cell populations. Produce population counts, percentages, and fluorescence summaries, or reproduce supported FlowJo 10 workspace analyses with recorded analysis settings.

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• Applying the same gating strategy to multiple FCS samples
• Reporting cell-population counts and percentages with denominators
• Reproducing supported FlowJo 10 analyses in Python

Rank possible gene regulatory relationships from bulk or single-cell expression data using GRNBoost2 or GENIE3. The skill guides input preparation, compatible execution, method comparison, and stability checks to help prioritize candidates for further biological investigation.

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• Rank candidate regulator–target gene relationships
• Compare GRNBoost2 and GENIE3 results
• Check network stability across seeds and resampled data

Plan how an AI application should use Hindsight memory. Identify useful information to retain, define user-scoping tags and memory bank settings, and specify how retrieved facts and synthesized knowledge should reach the model.

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• Plan memory that carries conversation context across sessions
• Design tags that separate each user's memories
• Plan summaries of customer preferences and recurring issues

Check a Bot’s connection to Hindsight and set up dedicated and shared memory stores. The skill also creates a user knowledge summary when needed and reports which other memory stores are available to read.

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• Setting up Hindsight for a Bot’s first use
• Checking the connection after a memory tool fails
• Separating project memories from shared knowledge

Consult bundled technical documentation to understand and integrate Hindsight, a memory system for AI agents. Find guidance on storing and retrieving memories, API calls, configuration, deployment, SDKs, troubleshooting, and performance.

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• Learning how memory storage and retrieval work
• Checking API endpoints and request parameters
• Planning server setup and SDK integration

Build Python text analysis with Azure AI Language to detect sentiment, names, key phrases, languages, and personal information. Examples also cover healthcare terminology extraction, combined analyses, authentication, and handling results for multiple documents.

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• Analyzing sentiment about specific aspects of a review
• Detecting and redacting personal information
• Extracting names and key phrases from text

Build Python features that identify a speaker’s intent and extract details such as names from conversation text using Azure. Get guidance on request structure, authentication, client cleanup, and exception handling.

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• Adding intent detection to a Python application
• Extracting names and other details from conversation text
• Structuring requests for ConversationAnalysisClient

Implement .NET workflows for Azure AI Foundry projects, including agent creation, resource connections, dataset uploads, model deployment inspection, search index management, and evaluations. Examples also cover authentication, asynchronous operations, and resource cleanup.

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• Creating and running agents from a .NET application
• Inspecting resource connections and model deployments
• Uploading datasets and managing search indexes

Build Python image analysis with Azure AI Vision to generate captions and tags, locate objects and people, and extract text using OCR, or optical character recognition. It also provides suggested crop regions.

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• Generate image captions and content tags
• Extract text from images
• Locate objects and people in photos

Develop AxonX plugins and manage quantitative finance research through the command line or MCP, an agent connection interface. Track data preparation, training, prediction, and historical strategy tests using execution status, logs, artifacts, and dependencies.

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• Develop research plugins with defined inputs and outputs
• Run data preparation, training, prediction, and backtests
• Investigate failed tasks through status and logs

Helps build Python workflows that extract information from documents, images, audio, and video using Azure AI Content Understanding. Covers document text, timed transcripts, video frames, and invoice fields for search and automated processing.

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• Preparing document content for AI search
• Transcribing audio with timestamps
• Extracting transcripts and key frames from video