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name: ai-data-science-team description: "Operate the ai-data-science-team package for AI-assisted data loading, EDA, pandas transformations, SQL analysis, H2O/MLflow modeling, multi-agent teams, and Streamlit app workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

AI Data Science Team repo skill

Use this repo skill when a task names ai-data-science-team, ai_data_science_team, AI Data Science Team, AI Pipeline Studio, or asks for AI-assisted data-science agents that load data, clean/wrangle/visualize DataFrames, query SQL databases, run H2O AutoML/MLflow tools, or compose data-science agent teams.

This skill is self-contained: read the bundled references and scripts here instead of reopening the original repository checkout, notebooks, or app source.

Quick package context

• Distribution name: ai-data-science-team.
• Import package: ai_data_science_team.
• Verified version baseline: 0.0.0.9017.
• Public install floor from package metadata: Python 3.9+; Python 3.10+ is recommended for app workflows.
• Common LLM backends: OpenAI chat models through langchain_openai.ChatOpenAI, or local Ollama models through langchain_ollama.ChatOllama when an Ollama service/model is available.
• Optional extras: machine_learning installs h2o and mlflow; data_science installs pytimetk, missingno, and sweetviz; D-Tale report support needs dtale separately.

Minimal import check:

from ai_data_science_team import DataCleaningAgent, SQLDatabaseAgent, PandasDataAnalyst

If that import fails with ModuleNotFoundError: IPython, install ipython; the source imports IPython.display but the package metadata may not declare it.

Route by task

Task signal	Read
Discover files, load CSV/Excel/JSON/Parquet, summarize DataFrames, use file tools, run basic EDA summaries, or opt into Sweetviz/D-Tale/missingno/correlation-funnel reports	sub-skills/data-access-and-eda/SKILL.md
Use DataCleaningAgent, DataWranglingAgent, DataVisualizationAgent, or FeatureEngineeringAgent; retrieve generated code/functions; handle retries/logs/human review	sub-skills/dataframe-code-agents/SKILL.md
Query SQL databases, inspect database metadata, protect read-only SQL, use SQLDatabaseAgent, or troubleshoot schema/token pressure	sub-skills/sql-analysis/SKILL.md
Train or inspect H2O AutoML workflows, evaluate models, manage MLflow experiments/runs/artifacts/UI/registry, or check optional ML dependencies	sub-skills/modeling-and-mlflow/SKILL.md
Compose agents with PandasDataAnalyst, SQLDataAnalyst, SupervisorDSTeam, WorkflowPlannerAgent, or understand AI Pipeline Studio / Streamlit app patterns	sub-skills/multiagent-and-app-workflows/SKILL.md

First actions for future agents

1. Identify whether the user wants deterministic helper functions, LLM-backed agents, optional report/model services, or Streamlit apps.
2. Check installed prerequisites before invoking agents. Use scripts/check_env.py for package import/signature and optional dependency visibility.
3. For deterministic data/SQL helper tasks, prefer bundled smoke scripts from the owning sub-skill before any LLM call.
4. For LLM-backed agents, confirm the user has configured a LangChain-compatible model object and any provider credentials/service.
5. For optional H2O/MLflow/EDA reports or app workflows, read the owning troubleshooting reference before installing extras, launching services, or starting long-running training/UI processes.

Shared references and helpers

• references/package-overview.md explains package modules, public objects, optional dependency groups, and common response/getter patterns.
• references/troubleshooting.md covers cross-cutting install/import, model-provider, optional dependency, service, generated-code, and app issues.
• references/repo-provenance.md records the source baseline used to create this skill; read it before deciding whether to refresh the skill.
• references/repo-routing-metadata.json is structured metadata for managed repo-skill routing.
• scripts/check_env.py is a safe no-credential environment/import checker.

Safety and verification boundaries

• Do not run notebook workflows, LLM-backed agents, H2O training, MLflow UI, or Streamlit apps unless the user explicitly wants that side effect and has provided credentials/services/budget.
• Treat generated code from cleaning/wrangling/visualization/feature-engineering agents as code to review, log, and validate on small data before using on production data.
• Keep SQLDatabaseAgent(safe_mode=True) unless the user intentionally authorizes non-read-only SQL. The bundled SQL sub-skill describes what the validator does and does not guarantee.
• Optional extras are not required for base imports. Install only the extra needed by the selected workflow, not all by default.
• Runtime guidance here must remain independent of the original checkout; if you need an example, use the bundled references/scripts.