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 orchestra-hq/orchestra-skills --skill tableau-prefect-to-orchestragit clone --depth 1 https://github.com/orchestra-hq/orchestra-skillsWrote 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/orchestra-hq/orchestra-skills/tableau-prefect-to-orchestra)<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/tableau-prefect-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/tableau-prefect-to-orchestra/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/tableau-prefect-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/tableau-prefect-to-orchestra.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00090 | $0.01259 |
| Opus 5 | $0.00045 | $0.00629 |
| Sonnet 5 | $0.00018 | $0.00252 |
| Haiku 4.5 | $0.00009 | $0.00126 |
Grade A, and why
tableau-prefect-to-orchestra 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 12d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Converts Prefect @task functions that use the tableau-server-client (TSC) Python library to refresh Tableau workbooks or datasource extracts into Orchestra pipeline tasks using the TABLEAU_CLOUD integration. Authentication (Personal Access Token + site ID) moves from environment variables inside the task to a named Orchestra Tableau Cloud connection.
Parameter Mapping
| Prefect construct | Orchestra field | Notes |
|---|---|---|
TSC.Server("https://10ax.online.tableau.com") + site_id |
connection: |
Orchestra Tableau Cloud connection holds server URL, site ID, and PAT |
server.workbooks.filter(name="Sales Dashboard") |
parameters.workbook_name |
String value from filter call |
| project name (from code context or filter chain) | parameters.project_name |
REQUIRED alongside workbook_name |
server.workbooks.refresh(workbook) |
integration_job: TABLEAU_REFRESH_WORKBOOK |
|
server.datasources.refresh(ds) |
integration_job: TABLEAU_REFRESH_EXTRACT + parameters.datasource_name |
Switch integration_job and use datasource_name instead |
treat_failure_as_warning |
treat_failure_as_warning: true |
Optional TaskModel field |
| retries / retry_delay | configuration.retries / configuration.retry_delay |
Under configuration: block — retry_delay is integer MINUTES (not seconds); convert Prefect's retry_delay_seconds by dividing by 60, cap at 120 |
Orchestra YAML Structure
integration: TABLEAU_CLOUD
integration_job: TABLEAU_REFRESH_WORKBOOK # or TABLEAU_REFRESH_EXTRACT
name: refresh_sales_dashboard
connection: tableau_cloud_prod_12345
parameters:
project_name: Sales
workbook_name: Sales Dashboard
depends_on: []
condition: null
tags: []
For a datasource extract refresh:
integration: TABLEAU_CLOUD
integration_job: TABLEAU_REFRESH_EXTRACT
name: refresh_orders_extract
connection: tableau_cloud_prod_12345
parameters:
project_name: Sales
datasource_name: Orders Extract
depends_on: []
condition: null
tags: []
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.
- 12d ago First seen · 126 lines · 90 tokens per session scan A a8abc8ac367b
tableau-prefect-to-orchestra is a skill published in the GitHub repository orchestra-hq/orchestra-skills (9 stars, last pushed 3d ago), licensed MIT. It adds 90 tokens to every session and 1,259 once invoked, about $0.0005 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 2026-08-31.
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