{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-business-science--ai-data-science-team","slug":"business-science--ai-data-science-team","name":"ai-data-science-team","type":"agent","url":"https://github.com/business-science/ai-data-science-team","page_url":"https://unfragile.ai/business-science--ai-data-science-team","categories":["automation"],"tags":["agents","ai","ai-engineer","ai-engineering","copilot","data-science","data-scientist","generative-ai","gpt","machine-learning","ml-engineer","ml-engineering","openai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-business-science--ai-data-science-team__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.supervisor.routing","name":"multi-agent orchestration with supervisor routing","description":"Implements a SupervisorDSTeam agent that routes natural language data science tasks to 10+ specialized agents using a state machine pattern built on LangGraph. The supervisor decomposes user requests, selects appropriate agents (DataLoaderAgent, DataCleaningAgent, FeatureEngineeringAgent, etc.), and chains their outputs together, maintaining dataset lineage across multi-step workflows. Uses CompiledStateGraph with conditional routing logic to dynamically dispatch to domain-specific agents based on task type.","intents":["I want to describe a complex data science workflow in natural language and have agents automatically execute it end-to-end","I need to coordinate multiple specialized agents to work on different parts of my data pipeline sequentially","I want to track which agent performed which transformation and maintain full provenance of my data"],"best_for":["data science teams automating multi-step ETL and analysis workflows","ML engineers building reproducible data pipelines without manual orchestration","organizations wanting to reduce time spent on routine data preparation tasks"],"limitations":["Supervisor routing decisions depend on LLM quality — poor prompts lead to incorrect agent selection","No built-in rollback mechanism if an agent in the chain fails; requires manual intervention or custom error handling","Latency scales with number of agents and chain depth; each routing decision adds LLM inference overhead","Limited to sequential agent chaining; no native support for parallel agent execution or conditional branching based on data properties"],"requires":["Python 3.10+","LangGraph library (state machine orchestration)","LangChain for agent framework integration","API key for OpenAI, Anthropic, or local Ollama installation","Streamlit 1.0+ for UI applications"],"input_types":["natural language task description","dataset references (file paths, database connections)","configuration parameters for specialized agents"],"output_types":["executable Python code","transformed datasets","lineage metadata (parent-child dataset relationships)","execution logs with agent routing decisions"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_1","uri":"capability://code.generation.editing.code.generation.with.sandboxed.execution.and.error.recovery","name":"code generation with sandboxed execution and error recovery","description":"Implements a coding agent pattern where specialized agents generate Python code via LLM, execute it in isolated subprocess sandboxes using run_code_sandboxed_subprocess(), capture errors, and automatically attempt fixes by re-prompting the LLM with error context. The BaseAgent class wraps a CompiledStateGraph with nodes for execution, error fixing, and explanation, enabling autonomous error recovery without user intervention. Supports multiple LLM providers (OpenAI, Anthropic, Ollama) through LangChain abstraction.","intents":["I want to generate data science code from natural language and have it execute automatically with error recovery","I need to sandbox code execution to prevent malicious or buggy code from crashing my system","I want agents to fix their own code when errors occur rather than requiring manual debugging"],"best_for":["data scientists who want to avoid manual coding for routine tasks","teams needing reproducible, auditable code generation with full error logs","organizations with security requirements around code execution isolation"],"limitations":["Sandbox isolation adds ~200-500ms latency per code execution due to subprocess overhead","Error recovery is heuristic-based; complex bugs may require multiple fix attempts or manual intervention","Generated code quality depends entirely on LLM capability; no static analysis or type checking before execution","Sandbox environment has limited access to external resources; network calls and file I/O may be restricted","No support for long-running code; subprocess timeout may interrupt legitimate long-duration computations"],"requires":["Python 3.10+","LangChain for LLM integration","LangGraph for state machine implementation","pandas, numpy for data manipulation in generated code","API key for OpenAI/Anthropic or local Ollama running on localhost:11434"],"input_types":["natural language task description","dataset context (schema, sample rows)","code generation instructions"],"output_types":["executable Python code (as string)","code execution results (stdout, stderr)","error messages and fix attempts","final validated code"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_10","uri":"capability://data.processing.analysis.data.cleaning.agent.with.automated.quality.issue.detection.and.fixing","name":"data cleaning agent with automated quality issue detection and fixing","description":"Implements a DataCleaningAgent that detects data quality issues (missing values, duplicates, outliers, type inconsistencies) and generates code to fix them. The agent analyzes data distributions, identifies anomalies, and applies appropriate cleaning techniques (imputation, deduplication, outlier removal, type conversion). Supports both statistical and domain-specific cleaning rules, with generated code that is transparent and modifiable.","intents":["I want to automatically detect and fix data quality issues without manual inspection","I need to handle missing values, duplicates, and outliers in my dataset","I want to standardize data types and formats across columns"],"best_for":["data scientists automating data cleaning workflows","teams reducing time spent on data quality issues","organizations standardizing data cleaning approaches"],"limitations":["Automated cleaning decisions may not match domain requirements; missing value imputation strategy depends on context","Outlier detection is statistical; may not identify domain-specific anomalies","No handling of semantic inconsistencies (e.g., 'USA' vs 'United States' as country names)","Aggressive cleaning (removing rows with missing values) may lose important data","No validation that cleaned data is suitable for downstream analysis; cleaning is not task-aware"],"requires":["Python 3.10+","pandas for data manipulation","numpy for statistical calculations","scikit-learn for imputation and outlier detection","LLM access for cleaning logic generation"],"input_types":["pandas DataFrame with quality issues","optional data quality rules or constraints","natural language cleaning instructions"],"output_types":["cleaned DataFrame","data cleaning code (Python)","data quality report (issues detected, fixes applied)","cleaning metadata (rows removed, values imputed, etc.)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_11","uri":"capability://data.processing.analysis.data.wrangling.agent.with.transformation.and.reshaping.automation","name":"data wrangling agent with transformation and reshaping automation","description":"Implements a DataWranglingAgent that generates code for complex data transformations (pivoting, melting, grouping, joining, filtering, sorting). The agent understands pandas operations and generates appropriate transformations from natural language descriptions. Supports multi-table operations (merges, concatenation) and complex aggregations, with generated code that is transparent and reusable.","intents":["I want to reshape and transform data (pivot, melt, group, join) without writing pandas code","I need to combine multiple datasets and perform complex aggregations","I want to filter, sort, and reorganize data for analysis or visualization"],"best_for":["data analysts automating data transformation workflows","teams reducing time spent on manual data wrangling","organizations standardizing data transformation approaches"],"limitations":["Generated transformations may not be optimal for large datasets; no automatic performance optimization","Complex multi-step transformations may generate hard-to-read code with nested operations","No automatic handling of data type mismatches in joins or concatenations","Limited to pandas operations; no support for distributed computing (Spark, Dask) for large-scale wrangling","Join logic is not validated; incorrect join keys may produce unexpected results"],"requires":["Python 3.10+","pandas for data transformation","numpy for array operations","LLM access for transformation logic generation"],"input_types":["pandas DataFrame(s)","natural language transformation description","optional join keys, grouping columns, aggregation functions"],"output_types":["transformed DataFrame","wrangling code (Python)","transformation metadata (operations applied, rows/columns affected)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_12","uri":"capability://data.processing.analysis.data.loading.agent.with.multi.source.format.support","name":"data loading agent with multi-source format support","description":"Implements a DataLoaderAgent that loads data from multiple sources (CSV, Excel, JSON, Parquet, SQL databases, APIs) and returns pandas DataFrames. The agent handles format detection, encoding issues, and connection management. Supports both local files and remote data sources, with automatic schema inference and optional data preview.","intents":["I want to load data from various file formats and databases without writing boilerplate code","I need to handle encoding issues and format-specific parameters automatically","I want to preview data and infer schema before loading entire datasets"],"best_for":["data scientists loading data from diverse sources","teams standardizing data loading approaches","organizations reducing boilerplate code for data ingestion"],"limitations":["Large files may cause memory issues when loading entire datasets; no streaming or chunked loading","API authentication requires credentials; no built-in secret management","Format detection is heuristic-based; ambiguous formats may be misidentified","No automatic data type inference; numeric columns may be loaded as strings","Limited to pandas-compatible formats; binary or specialized formats require custom loaders"],"requires":["Python 3.10+","pandas for DataFrame creation","openpyxl or xlrd for Excel files","sqlalchemy for database connections","requests for API calls","pyarrow for Parquet files"],"input_types":["file path (local or remote URL)","database connection string","API endpoint and authentication credentials","optional format specification and parameters"],"output_types":["pandas DataFrame","data preview (first N rows)","schema metadata (column names, types, missing value counts)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_13","uri":"capability://automation.workflow.visual.workflow.editor.with.drag.and.drop.agent.composition","name":"visual workflow editor with drag-and-drop agent composition","description":"Implements the AI Pipeline Studio application, a Streamlit-based visual interface for composing multi-agent workflows without code. Users drag-and-drop agent nodes (DataLoader, DataCleaner, FeatureEngineer, etc.), connect them with data flow edges, configure parameters through UI forms, and execute the pipeline. The studio generates the underlying agent orchestration code and provides real-time execution monitoring with error visualization.","intents":["I want to build data science pipelines visually without writing code","I need to configure agent parameters through a user-friendly interface","I want to monitor pipeline execution in real-time and debug failures visually"],"best_for":["non-technical stakeholders building data pipelines","data scientists prototyping workflows quickly","teams collaborating on pipeline design without code expertise"],"limitations":["Visual interface may be limiting for complex conditional logic or dynamic workflows","No support for custom agents or specialized domain logic beyond pre-built agents","Drag-and-drop composition can become cluttered with many agents; no hierarchical workflow grouping","Real-time monitoring adds overhead; large pipelines may have UI responsiveness issues","Generated code from visual workflows may be less optimized than hand-written code"],"requires":["Python 3.10+","Streamlit 1.0+ for UI framework","All dependencies of specialized agents (pandas, plotly, h2o, mlflow, sqlalchemy)","Web browser for accessing Streamlit app"],"input_types":["agent node selection","parameter configuration through UI forms","data flow connections between agents"],"output_types":["executable pipeline code (Python)","pipeline execution results","real-time execution logs and error messages","generated visualizations and reports"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_14","uri":"capability://automation.workflow.pandas.data.analyst.workflow.with.multi.agent.composition","name":"pandas data analyst workflow with multi-agent composition","description":"Implements a PandasDataAnalyst workflow that orchestrates multiple agents (DataLoader, DataCleaner, DataWrangler, EDATools, FeatureEngineer, MLAgent) to perform end-to-end pandas-based data analysis. The workflow accepts a natural language task description, automatically decomposes it into sub-tasks, routes to appropriate agents, and chains results together. Generates a complete, reproducible pandas analysis script as output.","intents":["I want to perform end-to-end data analysis (load, clean, explore, engineer, model) from a single natural language description","I need to automatically decompose complex analysis tasks into agent sub-tasks","I want to generate a reproducible pandas analysis script without manual orchestration"],"best_for":["data scientists performing exploratory analysis workflows","teams automating routine pandas-based analysis","organizations reducing time from raw data to insights"],"limitations":["Workflow decomposition depends on LLM quality; poor task descriptions lead to incorrect agent routing","No automatic validation that agent outputs are suitable for downstream agents","Complex workflows with conditional logic may require manual intervention","Performance scales poorly with large datasets; no distributed computing support","Generated scripts may not be optimized; manual tuning may be needed for production"],"requires":["Python 3.10+","All dependencies of specialized agents (pandas, numpy, plotly, scikit-learn, h2o, mlflow)","LLM access (OpenAI, Anthropic, or Ollama)"],"input_types":["natural language analysis task description","data source (file path, database connection, or DataFrame)"],"output_types":["pandas DataFrame with analysis results","executable analysis script (Python)","visualizations and statistical summaries","lineage metadata (agents used, transformations applied)"],"categories":["automation-workflow","planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_15","uri":"capability://automation.workflow.sql.data.analyst.workflow.with.database.native.operations","name":"sql data analyst workflow with database-native operations","description":"Implements a SQLDataAnalyst workflow that orchestrates SQL-based analysis using the SQLDatabaseAgent, with optional pandas integration for visualization and advanced analysis. The workflow accepts natural language queries, generates SQL code, executes against connected databases, and returns results as DataFrames. Supports exploratory queries, aggregations, and complex joins without requiring manual SQL writing.","intents":["I want to perform SQL-based data analysis using natural language instead of writing SQL","I need to extract data from databases and integrate it with Python analysis workflows","I want to explore database schema and generate summary statistics without manual SQL"],"best_for":["data analysts working primarily with SQL databases","teams automating data extraction from data warehouses","organizations reducing SQL expertise requirements"],"limitations":["Generated SQL may be inefficient or incorrect for complex queries","No query optimization; generated queries may cause performance issues on large tables","Limited to read-only operations; cannot safely generate INSERT/UPDATE/DELETE queries","Database-specific syntax variations are not handled; generated SQL may fail on different database systems","No automatic handling of database-specific features (window functions, CTEs, stored procedures)"],"requires":["Python 3.10+","SQLAlchemy for database abstraction","Database driver (psycopg2, mysql-connector, etc.)","Database connection string with credentials","LLM access (OpenAI, Anthropic, or Ollama)"],"input_types":["natural language query description","database connection string","optional schema hints or table names"],"output_types":["SQL query (as string)","query results (pandas DataFrame)","execution metadata (rows returned, query time)","optional visualizations (if pandas integration enabled)"],"categories":["automation-workflow","data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_2","uri":"capability://memory.knowledge.dataset.registry.with.full.provenance.tracking.and.lineage","name":"dataset registry with full provenance tracking and lineage","description":"Maintains a dataset registry that tracks parent-child relationships between datasets as they flow through the agent pipeline, recording which agent performed which transformation and when. Each dataset is assigned metadata including source, transformations applied, and downstream dependencies. The registry enables reproducible pipelines by allowing users to trace any output dataset back to its original source and understand the exact sequence of operations that produced it.","intents":["I need to understand the full lineage of how a dataset was created and which transformations were applied","I want to reproduce a specific analysis by re-running the exact sequence of agent transformations","I need to audit which agent modified which data and when for compliance or debugging purposes"],"best_for":["regulated industries (finance, healthcare) requiring data provenance for compliance","data science teams debugging unexpected results by tracing transformations","organizations building reproducible ML pipelines with full audit trails"],"limitations":["Lineage tracking adds memory overhead proportional to number of datasets and transformations","No built-in persistence layer; lineage data is lost if process terminates unless explicitly saved","Lineage visualization is limited to the AI Pipeline Studio UI; no programmatic lineage query API","Cannot track lineage for external data sources (databases, APIs) that agents don't directly control"],"requires":["Python 3.10+","In-memory storage (no external database required for basic functionality)","Streamlit for visualization in AI Pipeline Studio"],"input_types":["dataset objects (pandas DataFrames, file paths)","agent execution metadata (agent name, timestamp, parameters)"],"output_types":["lineage graph (parent-child relationships)","transformation history (sequence of operations)","metadata JSON (source, transformations, timestamps)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_3","uri":"capability://code.generation.editing.specialized.agent.factory.for.domain.specific.data.science.tasks","name":"specialized agent factory for domain-specific data science tasks","description":"Provides 10+ pre-built specialized agents (DataLoaderAgent, DataCleaningAgent, DataWranglingAgent, FeatureEngineeringAgent, DataVisualizationAgent, EDAToolsAgent, SQLDatabaseAgent, MLAgent, ExperimentTrackingAgent) that inherit from BaseAgent and implement domain-specific prompts and tool bindings. Each agent is instantiated via create_coding_agent_graph() factory function, which configures the agent's system prompt, available tools, and execution environment. Agents can work independently or be composed by the SupervisorDSTeam for complex workflows.","intents":["I want to load data from various sources (CSV, SQL, APIs) without writing boilerplate code","I need to clean and transform data (handle missing values, outliers, type conversions) automatically","I want to generate exploratory data analysis visualizations and statistical summaries without manual coding","I need to engineer features, train ML models, and track experiments with minimal manual intervention"],"best_for":["data scientists performing routine data preparation and analysis tasks","teams standardizing data science workflows across projects","organizations automating repetitive data science work to free up expert time"],"limitations":["Each agent is specialized for a specific task; complex workflows requiring cross-domain knowledge need supervisor orchestration","Agent quality depends on LLM capability and prompt engineering; poor prompts lead to incorrect transformations","No built-in validation of generated code; agents may produce syntactically correct but semantically incorrect transformations","Limited to Python ecosystem; cannot natively integrate with R, Julia, or other data science languages","Agents have no memory of previous interactions; each invocation is stateless unless explicitly passed context"],"requires":["Python 3.10+","LangChain for LLM integration","LangGraph for state machine implementation","Domain-specific libraries: pandas, numpy, plotly (visualization), h2o (ML), mlflow (experiment tracking), sqlalchemy (SQL)"],"input_types":["natural language task description","dataset references (file paths, database connections, API endpoints)","configuration parameters (column names, thresholds, model hyperparameters)"],"output_types":["transformed datasets (pandas DataFrames)","visualizations (plotly figures, HTML)","statistical summaries (JSON, text)","trained models (pickle, joblib)","experiment metadata (MLflow runs)"],"categories":["code-generation-editing","data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_4","uri":"capability://tool.use.integration.llm.agnostic.provider.abstraction.with.multi.provider.support","name":"llm-agnostic provider abstraction with multi-provider support","description":"Abstracts LLM provider selection through LangChain's language model interface, enabling seamless switching between OpenAI, Anthropic, Ollama, and other providers without code changes. Configuration is handled via environment variables or explicit provider specification at agent instantiation. Supports both cloud-based APIs (OpenAI GPT-4, Claude) and local models (Ollama) for air-gapped or privacy-sensitive deployments.","intents":["I want to switch between OpenAI and Anthropic models without rewriting agent code","I need to run agents locally using Ollama for privacy or cost reasons","I want to compare model performance across different providers on the same task"],"best_for":["organizations evaluating multiple LLM providers","teams with privacy requirements needing local model deployment","cost-conscious teams wanting to use cheaper models (Ollama) for routine tasks"],"limitations":["Model capabilities vary significantly across providers; code generation quality depends on chosen model","Local Ollama models are slower and less capable than cloud-based GPT-4 or Claude","No automatic model selection based on task complexity; users must manually choose appropriate model","Provider-specific features (function calling, vision) may not be uniformly supported across all providers","Switching providers may require re-tuning prompts and system messages for optimal performance"],"requires":["Python 3.10+","LangChain library (provider abstraction)","For OpenAI: OPENAI_API_KEY environment variable","For Anthropic: ANTHROPIC_API_KEY environment variable","For Ollama: Local Ollama installation running on localhost:11434"],"input_types":["provider name (openai, anthropic, ollama)","model identifier (gpt-4, claude-3-opus, llama2)","API credentials (environment variables)"],"output_types":["LLM responses (text)","code generation output","structured data (JSON from function calling)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_5","uri":"capability://code.generation.editing.reproducible.pipeline.generation.with.executable.python.scripts","name":"reproducible pipeline generation with executable python scripts","description":"Generates complete, executable Python scripts that encapsulate the entire data science workflow performed by agents. Each script includes all data loading, transformation, visualization, and ML steps in a single reproducible file that can be version-controlled, shared, and re-executed independently of the agent system. Scripts include error handling, logging, and comments explaining each step, making them suitable for production deployment or team collaboration.","intents":["I want to export the workflow agents created into a standalone Python script I can run independently","I need to version control the exact data science pipeline for reproducibility and audit purposes","I want to hand off the generated code to engineers for production deployment without agent dependencies"],"best_for":["data science teams transitioning from exploration to production","organizations requiring version-controlled, auditable data pipelines","teams collaborating across data scientists and engineers"],"limitations":["Generated scripts may not be optimized for performance; agent-generated code prioritizes correctness over efficiency","Scripts require all dependencies (pandas, numpy, plotly, etc.) to be installed in target environment","No automatic conversion to other languages (R, SQL, Spark); scripts are Python-only","Complex workflows with conditional logic may generate hard-to-read scripts with nested if-else statements","Scripts don't include the agent's reasoning or alternative approaches; only the final chosen path is captured"],"requires":["Python 3.10+","All data science libraries used by agents (pandas, numpy, plotly, h2o, mlflow, sqlalchemy)","Access to data sources referenced in the pipeline"],"input_types":["agent execution history","generated code from specialized agents","dataset references and transformations"],"output_types":["executable Python script (.py file)","script with embedded documentation and comments","requirements.txt with dependencies"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_6","uri":"capability://data.processing.analysis.sql.database.agent.with.query.generation.and.execution","name":"sql database agent with query generation and execution","description":"Implements a specialized SQLDatabaseAgent that generates SQL queries from natural language descriptions, executes them against connected databases, and returns results as pandas DataFrames. The agent understands database schema, handles connection management, and can perform exploratory queries, data extraction, and aggregations. Supports multiple database backends (PostgreSQL, MySQL, SQLite, etc.) through SQLAlchemy abstraction.","intents":["I want to query databases using natural language instead of writing SQL","I need to extract data from SQL databases and integrate it with Python data science workflows","I want to explore database schema and generate summary statistics without manual SQL writing"],"best_for":["data analysts and scientists working with SQL databases","teams automating data extraction from data warehouses","organizations reducing SQL expertise requirements for data access"],"limitations":["Generated SQL may be inefficient or incorrect for complex queries; LLM understanding of SQL is limited","No query optimization; generated queries may cause performance issues on large tables","Cannot handle database-specific syntax variations (PostgreSQL vs MySQL vs Oracle)","No built-in query validation; malformed SQL is only caught at execution time","Limited to read-only operations; cannot safely generate INSERT/UPDATE/DELETE queries without additional safeguards"],"requires":["Python 3.10+","SQLAlchemy for database abstraction","Database driver (psycopg2 for PostgreSQL, mysql-connector for MySQL, etc.)","Database connection string with credentials","LLM access (OpenAI, Anthropic, or Ollama)"],"input_types":["natural language query description","database connection string","optional schema hints or table names"],"output_types":["SQL query (as string)","query results (pandas DataFrame)","execution metadata (rows affected, query time)"],"categories":["data-processing-analysis","code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_7","uri":"capability://data.processing.analysis.exploratory.data.analysis.eda.automation.with.visualization.generation","name":"exploratory data analysis (eda) automation with visualization generation","description":"Implements an EDAToolsAgent that automatically generates exploratory visualizations, statistical summaries, and data quality reports from datasets. The agent analyzes column types, distributions, correlations, and missing values, then generates appropriate visualizations (histograms, scatter plots, heatmaps, box plots) using Plotly. Results are returned as interactive HTML visualizations and JSON summaries suitable for stakeholder communication.","intents":["I want to quickly understand a new dataset's structure, distributions, and quality without manual exploration","I need to generate professional EDA reports and visualizations for stakeholder presentations","I want to identify data quality issues (missing values, outliers, skewness) automatically"],"best_for":["data scientists performing initial data exploration","analysts generating reports for non-technical stakeholders","teams standardizing EDA processes across projects"],"limitations":["Automated visualization selection may not match domain-specific analysis needs","Large datasets (>1M rows) may cause performance issues in visualization generation","No interactive filtering or drill-down in generated reports; visualizations are static","Statistical tests are limited to basic descriptive statistics; no advanced statistical inference","Correlation analysis assumes numeric columns; categorical relationships are not analyzed"],"requires":["Python 3.10+","pandas for data manipulation","plotly for interactive visualization generation","numpy for statistical calculations","LLM access for analysis interpretation"],"input_types":["pandas DataFrame","optional analysis parameters (target column, grouping columns)"],"output_types":["interactive Plotly visualizations (HTML)","statistical summary (JSON, text)","data quality report (missing values, duplicates, outliers)","correlation matrix and heatmaps"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_8","uri":"capability://data.processing.analysis.feature.engineering.agent.with.automated.transformation.generation","name":"feature engineering agent with automated transformation generation","description":"Implements a FeatureEngineeringAgent that generates feature transformations (scaling, encoding, polynomial features, interactions, domain-specific features) from natural language descriptions. The agent analyzes the target variable and existing features, then generates code to create new features that improve model predictability. Supports both numeric and categorical feature engineering, with automatic selection of appropriate techniques (StandardScaler, OneHotEncoder, PolynomialFeatures, etc.).","intents":["I want to automatically generate new features from existing columns without manual feature engineering","I need to scale, encode, and transform features for machine learning models","I want to create interaction terms and polynomial features to improve model performance"],"best_for":["data scientists automating feature engineering for ML pipelines","teams reducing manual feature engineering effort","organizations standardizing feature engineering approaches"],"limitations":["Generated features may not be domain-relevant; LLM lacks domain expertise for specialized features","No automatic feature selection; all generated features are included without importance ranking","Polynomial and interaction features can cause dimensionality explosion on high-dimensional datasets","No handling of temporal features or time-series specific transformations","Feature engineering is not validated against model performance; generated features may not improve predictions"],"requires":["Python 3.10+","scikit-learn for feature transformation (scaling, encoding, polynomial features)","pandas for feature manipulation","LLM access for feature generation logic"],"input_types":["pandas DataFrame with features","target variable (optional, for supervised feature engineering)","natural language feature engineering instructions"],"output_types":["transformed DataFrame with new features","feature engineering code (Python)","feature metadata (names, types, transformations applied)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-business-science--ai-data-science-team__cap_9","uri":"capability://code.generation.editing.ml.model.training.and.experiment.tracking.integration","name":"ml model training and experiment tracking integration","description":"Implements MLAgent and ExperimentTrackingAgent that generate model training code, execute training pipelines, and automatically log experiments to MLflow. The agent supports multiple model types (linear regression, decision trees, random forests, gradient boosting, neural networks), hyperparameter tuning, and cross-validation. Experiment metadata (parameters, metrics, artifacts) is logged to MLflow for tracking model performance across iterations.","intents":["I want to train machine learning models from natural language descriptions without manual code","I need to track model experiments, hyperparameters, and performance metrics across multiple runs","I want to compare model performance and select the best model for deployment"],"best_for":["data scientists automating model training workflows","teams tracking ML experiments for reproducibility","organizations standardizing ML model development processes"],"limitations":["Generated models may not be optimal; LLM lacks expertise in hyperparameter tuning","No automatic model selection; all generated models must be manually compared","Limited to scikit-learn and basic deep learning models; no support for specialized architectures (transformers, graph neural networks)","Hyperparameter tuning is limited to grid search or random search; no Bayesian optimization","No automatic handling of class imbalance, missing values, or other data quality issues in training"],"requires":["Python 3.10+","scikit-learn for traditional ML models","tensorflow or pytorch for deep learning (optional)","mlflow for experiment tracking","LLM access for model generation logic"],"input_types":["training dataset (pandas DataFrame)","target variable","natural language model specification (model type, hyperparameters)","optional validation/test datasets"],"output_types":["trained model (pickle, joblib, or framework-specific format)","model predictions (numpy array or pandas Series)","MLflow experiment metadata (parameters, metrics, artifacts)","model evaluation report (accuracy, precision, recall, F1, etc.)"],"categories":["code-generation-editing","data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","LangGraph library (state machine orchestration)","LangChain for agent framework integration","API key for OpenAI, Anthropic, or local Ollama installation","Streamlit 1.0+ for UI applications","LangChain for LLM integration","LangGraph for state machine implementation","pandas, numpy for data manipulation in generated code","API key for OpenAI/Anthropic or local Ollama running on localhost:11434","pandas for data manipulation"],"failure_modes":["Supervisor routing decisions depend on LLM quality — poor prompts lead to incorrect agent selection","No built-in rollback mechanism if an agent in the chain fails; 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