Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add TomerMoskov/tableau-ai-skill --skill tableau-twbgit clone --depth 1 https://github.com/TomerMoskov/tableau-ai-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tomermoskov/tableau-ai-skill/tableau-twb)<a href="https://agentmods.dev/skills/tomermoskov/tableau-ai-skill/tableau-twb"><img src="https://agentmods.dev/badge/skills/tomermoskov/tableau-ai-skill/tableau-twb.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00173 | $0.15378 |
| Opus 5 | $0.00086 | $0.07689 |
| Sonnet 5 | $0.00035 | $0.03076 |
| Haiku 4.5 | $0.00017 | $0.01538 |
Grade A, and why
tableau-twb scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 786 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tableau TWB XML & REST API Skill
Create and manipulate Tableau workbook files (.twb/.twbx) and data sources (.tds/.tdsx) programmatically via XML. Publish and manage content on Tableau Server/Cloud via REST API. Supports both published data source connections (sqlproxy) and direct database connections (federated) for production Tableau workbooks.
Two Data Connection Patterns
Pattern 1: Published Data Source (sqlproxy) — Governed
When connecting to a published data source on Tableau Cloud/Server, use the sqlproxy connection class:
<datasource caption='My Data (Published)' name='sqlproxy.1rfw7xb1xv29kt1cv7fne0cre0xx' inline='true' version='18.1'>
<repository-location derived-from='http://server.com/t/site/datasources/MyPublishedDataSource?rev=1.2'
id='MyPublishedDataSource' path='/t/site/datasources'
revision='1.3' site='MySiteName' />
<connection channel='https' class='sqlproxy' dbname='MyPublishedDataSource'
directory='/dataserver' local-dataserver='' port='443'
server='your-pod.online.tableau.com'
server-ds-friendly-name='My Data (Published)'
username='' workgroup-auth-mode='prompt'>
<relation type='collection'>
<relation name='sqlproxy' table='[sqlproxy]' type='table' />
</relation>
</connection>
</datasource>
See references/connections-and-datasources.md for complete examples.
Pattern 2: Direct Database Connection (federated) — Flexible
When connecting directly to a database (Databricks, SQL Server, etc.) without a published data source, use the federated connection wrapper with a named-connection containing the actual database connector. Supports Custom SQL and direct table references:
<datasource caption='My Query' name='federated.02bm58x14zgdy9188vvi70n3vz3a' inline='true' version='18.1'>
<connection class='federated'>
<named-connections>
<named-connection caption='dbc-XXXX.cloud.databricks.com' name='databricks.0fy96ry16x2arq100fydu0fmm2qe'>
<connection authentication='auth-pass' class='databricks'
dbname='your-catalog' schema='marts'
server='dbc-XXXX.cloud.databricks.com'
v-http-path='/sql/1.0/warehouses/WAREHOUSE_ID'
workgroup-auth-mode='prompt'>
<connection-customization class='databricks' enabled='false' version='18.1'>
<vendor name='databricks' /><driver name='databricks' />
<customizations><!-- capability flags --></customizations>
</connection-customization>
</connection>
</named-connection>
</named-connections>
<relation connection='databricks.0fy96ry16x2arq100fydu0fmm2qe'
name='Custom SQL Query' type='text'>SELECT * FROM `catalog`.`schema`.`table`</relation>
<metadata-records><!-- column type mappings --></metadata-records>
</connection>
</datasource>
What ships with it
20 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/calculated-fields-and-lod.md 30 KB
- references/connections-and-datasources.md 36 KB
- references/dashboard-design-best-practices.md 33 KB
- references/dashboard-layout-and-zones.md 26 KB
- references/databricks-connection-config.md 1.5 KB
- references/error-codes-and-pitfalls.md 17 KB
- references/filters-and-parameters.md 24 KB
- references/hyper-api.md 14 KB
- references/kpi-cards-and-trends.md 19 KB
- references/programmatic-twb-learnings.md 202 KB
- references/rest-api.md 15 KB
- references/revenue-view-datasource-example.xml 7.5 KB
- references/tableau-functions-reference.md 26 KB
- references/visualizations-and-charts.md 36 KB
- references/xml-schema-and-structure.md 29 KB
- schemas/twb_2026.1.0.xsd 290 KB
- schemas/user_namespace.xsd 365 B
- scripts/generate_twb.py 16 KB runs code
- scripts/parse_twb.py 6.8 KB runs code
- scripts/rest_api_client.py 24 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 786 lines · 173 tokens per session scan A 8ff77b28a02f
tableau-twb is a skill published in the GitHub repository TomerMoskov/tableau-ai-skill (13 stars, last pushed 4mo ago), licensed MIT. It adds 173 tokens to every session and 15,378 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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