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name: data-access-and-eda description: "Use ai-data-science-team data/file loading helpers, direct DataFrame summaries, DataLoaderToolsAgent, EDAToolsAgent, and optional EDA report tools." disable-model-invocation: true metadata: disco-role: operating license: MIT

data-access-and-eda

Use this sub-skill when the task is about discovering local data files, loading tabular files, summarizing DataFrames, or using the package's tool-calling data loader / EDA agents.

Best-fit tasks

• List, search, or inspect files and folders before analysis.
• Load CSV, TSV, Excel, JSON/JSONL/NDJSON, Parquet, or explicitly trusted pickle files into pandas.
• Summarize one or more pandas DataFrames without asking an LLM to write pandas code.
• Use DataLoaderToolsAgent to let a chat model choose data-loader tools.
• Use EDAToolsAgent for explain/describe/missingness/correlation/report tool selection.
• Decide whether optional EDA report tooling is appropriate for a safe, bounded run.

Route away from this sub-skill

• Cleaning, wrangling, feature engineering, or visualization code generation: use dataframe-code-agents.
• SQL database metadata, query generation, or SQL safety: use sql-analysis.
• Streamlit apps, Pipeline Studio, supervisor workflows, or multi-agent orchestration: use multiagent-and-app-workflows.
• H2O AutoML, model evaluation, MLflow tools, or ML service troubleshooting: use modeling-and-mlflow.

Fast operating path

1. For deterministic data loading and summaries, prefer direct tool functions and DataFrame helpers from references/api-reference.md.
2. For user-facing file discovery or EDA requests that genuinely need model-based tool selection, use the agent workflows in references/workflows.md. These require a LangChain-compatible model and may call that model when invoked.
3. Treat optional EDA report tools as opt-in. Check references/optional-eda-reports.md before using missingno, pytimetk, sweetviz, or dtale features.
4. If a symptom appears, use references/troubleshooting.md before retrying.
5. For a no-network, no-LLM smoke check, run scripts/smoke_data_access.py in the target Python environment.

Safety defaults

• Do not call external LLM providers unless the user explicitly provides a model configuration and expects agent invocation.
• Use listing/search tools before loading file contents when the user only asks what files exist.
• Do not load pickle files from untrusted sources. Pickle loading is disabled by default and requires an explicit ALLOW_UNSAFE_PICKLE opt-in in the process environment.
• Use small sample sizes for summaries of wide or large DataFrames.
• Optional report tools may write HTML files or launch local services; get explicit approval before browser, service, or long-running report workflows.