| name | query-tableau-data |
| description | A skill to query Tableau data sources, the "last mile" of analytics in an organization. When business users think about company data they often think of a visualization or data set on the BI platform, curated to their needs with useful semantics instead of raw data in a warehouse. |
| metadata | {"category":"data","source":{"repository":"https://github.com/Action-Co/skills","path":"skills/tableau/query-tableau-data","license_path":"LICENSE","commit":"1e9d4403f120d1950aafedecf594d39eec876686"}} |
Why Tableau?
Tableau is a repository of data sources and visualizations that represent the "last mile" of analytics in an organization. When business users think about company data they often think of a visualization or data set on the BI platform, curated to their needs with useful semantics instead of raw data in a warehouse.
The visual context built on top of data sources shapes this otherwise raw resource into something more consumable and actionable for people. Tableau is good at enabling all kinds of users to be productive with data and it thus contains a diversity of perspectives you will not find elsewhere in the data stack.
To query Tableau, you must first explore the data catalog ideally in a Read-Evaluate-Print Loop (REPL) such that you can quickly iterate over different approaches to find the datasources and views you need. You can query data from views directly but querying datasources gives you more flexibility and access to the full dataset, at the cost of having to understand the schema and construct your own query logic. This information can be derived from the view layer and then formalized as a more robust query against the datasource with additional filters, aggregations, or calculations as needed.
Workflow (REPL-first)
Explore the data catalog and reason through your task using a REPL tool. This workflow is inspired by research on Recursive Language Models, which shows that holding large inputs as REPL variables — rather than loading them into the context window — scales beyond context limits. Recursion (e.g., sub-agent delegation) is implemented by individual harnesses, not by this skill itself.
RULE: Do not write files for exploration. Run uv run python -c "..." directly.
Writing exploration code to disk is a exploration workflow anti-pattern. Use scripts/ only to
formalize a reusable workflow after REPL exploration is complete.
Hold catalog, schema, and query results as Python variables inside a Session, and surface only printed summaries to your context window. This keeps information-dense payloads out of the context window, where they degrade reasoning quality on linear-complexity tasks. Only print counts, filtered lists, and small row samples — never full payloads. This loop lets you iterate until you have a clear strategy for next steps in your task.
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Understand the Problem Space. What question is the user trying to answer? What are the fundamental components of your task? You will advise on which datasources or views serve their needs after mapping the catalog.
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Authenticate first — run this before anything else.
from query_tableau_data_py.config import SdkConfig
from query_tableau_data_py.session import Session
with Session(SdkConfig()) as session:
si = session.server_info
print(f"Server {si.product_version} (API {si.rest_api_version} — auto-negotiated), VDS tier: {si.vds_feature_tier}")
print("AUTH OK")
If this raises:
ValidationError → .env is missing required fields (TABLEAU_SERVER_URL, PAT_NAME, PAT_VALUE)AuthenticationError → credentials are wrong or PAT is expiredOSError / ConnectionError → server URL is wrong or network is down
If credentials are missing or invalid, stop and ask the user to complete the setup steps in README.md § HITL. Creating a PAT and configuring .env requires human access to the Tableau UI.
Do not manually check for .env — place it in the skill root directory (next to .env.template and pyproject.toml). SdkConfig looks for .env in cwd first, then the skill root, and raises immediately if anything is missing. Let it fail loudly. See AUTH.md to troubleshoot.
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Scope the Site. Before calling any inventory function, take a fast site-scale snapshot. This makes exactly 4 HTTP requests regardless of site size — no timeout risk.
scope = session.scope_site()
print()
()
p scope.projects[:]:
()
Using the Code
This skill contains a Python package (query_tableau_data_py) with a modular script suite (auth.py, catalog.py, inventory.py, lineage.py, introspect_datasource.py, introspect_workbook.py, query.py, query_view.py) and a demo orchestrator, main.py. The demo is an example, not a reusable entry point — for your own workflows, use Session directly in a REPL or write a new script that imports it. See credentials setup.
Start here — REPL exploration: REPL.md — a complete REPL session demonstrating the full workflow: auth check → inventory → lineage → introspection → VDS query.
Building a reusable script? Only write to scripts/ after validating your approach in the REPL. See the SDK Reference for module documentation, import patterns, and complete examples.
Need to persist results to disk? See TEMP_DATA.md for file-based persistence conventions.
Output conventions:
- Reusable scripts you write → save to
scripts/ (committed to git)
- Temp exploration data (JSON, CSV, Markdown) → save to
temp/ (gitignored; clean up when done)
Skill Structure
skills/query-tableau-data/
├── SKILL.md
├── README.md
├── pyproject.toml
├── .env.template
│
├── docs/
│ ├── README.md
│ ├── REPL.md
│ ├── DDD.md
│ ├── sdk/
│ ├── vds/
│ └── api/
│
├── scripts/
│
├── temp/
│
└── src/
├── schemas/
│ └── vds.20261.0.openapi.json
│
└── query_tableau_data_py/
└── main.py
Note: This skill was last updated as of Tableau version 2026.1.0.
