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name: multiagent-and-app-workflows description: "Compose ai-data-science-team multi-agent workflows and understand the package's Streamlit application patterns." disable-model-invocation: true metadata: disco-role: operating license: MIT

Multiagent And App Workflows

Use this sub-skill when the task is about composing multiple ai_data_science_team agents, selecting a team workflow, inspecting multi-agent outputs, or understanding the packaged Streamlit app patterns including AI Pipeline Studio.

Best Fit

Use this sub-skill for:

• PandasDataAnalyst: data wrangling followed by optional Plotly visualization.
• SQLDataAnalyst: SQL querying followed by optional Plotly visualization.
• SupervisorDSTeam / make_supervisor_ds_team: supervisor-led routing across data loading, wrangling, cleaning, EDA, visualization, SQL, feature engineering, model evaluation, H2O, and MLflow workers.
• WorkflowPlannerAgent: structured plans for the supervisor-led team.
• Streamlit app workflow understanding, safe launch planning, and AI Pipeline Studio project/pipeline behavior.
• Multi-turn artifacts and active-dataset handoff between team calls.

Route Elsewhere

• For detailed parameters of DataCleaningAgent, DataWranglingAgent, DataVisualizationAgent, or FeatureEngineeringAgent, read ../dataframe-code-agents/SKILL.md.
• For direct data loading, file discovery, EDA-only tools, or optional EDA reports, read ../data-access-and-eda/SKILL.md.
• For SQL safe mode, SQL metadata, schema sampling, and direct SQLDatabaseAgent use, read ../sql-analysis/SKILL.md.
• For H2O AutoML, deterministic model evaluation internals, MLflow tools, and optional ML dependencies, read ../modeling-and-mlflow/SKILL.md.

Required Reading

1. Read references/api-reference.md for constructors, factories, methods, and response getters.
2. Read references/workflows.md for composition patterns and artifact handoff.
3. Read references/apps-and-pipeline-studio.md before advising on any Streamlit app or AI Pipeline Studio behavior.
4. Read references/troubleshooting.md when imports, routing, app startup, state handoff, visualization, or optional services fail.

Operating Rules

• Treat every model argument as a potentially external LLM-backed runtime dependency. Do not invoke agents unless the user explicitly asks to run them and the runtime is configured.
• Do not launch Streamlit apps or other long-running UI services unless the user explicitly asks for an app launch. Prefer explaining setup and verifying imports first.
• Do not place provider settings, local environment names, or user-specific paths in generated code or saved notes.
• Reuse artifacts between team calls: pass team.get_artifacts() into the next team.invoke_agent(..., artifacts=artifacts) when the second request depends on previous data.
• Prefer the smallest composition that satisfies the request. Use PandasDataAnalyst or SQLDataAnalyst for two-step workflows; use SupervisorDSTeam for broad, multi-step, cross-capability tasks.
• For read-only inspection without an LLM call, run scripts/inspect_multiagent_api.py.

Quick Selection

User intent	Start here	Notes
"Wrangle this DataFrame and maybe chart it"	PandasDataAnalyst	Requires prebuilt wrangling and visualization agents.
"Query this database and maybe chart results"	SQLDataAnalyst	Requires prebuilt SQL and visualization agents. SQL safety details belong to sql-analysis.
"Run a data-science team over uploaded data"	SupervisorDSTeam	Build all worker agents; pass artifacts across turns.
"Plan the steps before running"	WorkflowPlannerAgent	Produces a JSON-like plan; it does not execute work.
"Use Pipeline Studio / Streamlit app"	references/apps-and-pipeline-studio.md	Treat app launch as an interactive service operation.