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 prefect-testing-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/prefect-testing-to-orchestra)<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/prefect-testing-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/prefect-testing-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/prefect-testing-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/prefect-testing-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 | $0.00098 | $0.02074 |
| Opus 5 | $0.00049 | $0.01037 |
| Sonnet 5 | $0.00020 | $0.00415 |
| Haiku 4.5 | $0.00010 | $0.00207 |
Grade A, and why
prefect-testing-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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Prefect data quality tests are typically @task functions that query a database and assert on the result, raising exceptions on failure. Orchestra provides native test integration jobs (*_RUN_TEST) that run SQL and evaluate threshold expressions. The critical difference: Prefect tests PASS when the assertion is TRUE; Orchestra *_RUN_TEST FAILS when the SQL result MATCHES the threshold expression. Always invert the condition.
Great Expectations and Soda have no native Orchestra integration jobs — wrap them in PYTHON_EXECUTE_SCRIPT tasks.
Parameter Mapping
| Prefect construct | Orchestra field | Notes |
|---|---|---|
@task running SQL check via SnowflakeConnector |
integration_job: SNOWFLAKE_RUN_TEST |
invert assertion |
@task running BigQuery check |
integration_job: GCP_BQ_RUN_TEST |
invert assertion |
@task running Postgres check |
integration_job: POSTGRES_RUN_TEST |
invert assertion |
@task running Databricks SQL check |
integration_job: DATABRICKS_RUN_TEST |
invert assertion |
@task running SQL Server check |
integration_job: SQL_SERVER_RUN_QUERY |
for testing via SQL Server |
assert count == 0 (nulls check) |
error_threshold_expression: '> 0' |
inverted: fail when count IS > 0 |
assert count >= 1000 (min rows) |
error_threshold_expression: '< 1000' |
inverted: fail when count IS < 1000 |
raise ValueError(...) on failure |
error_threshold_expression matching failing condition |
|
log.warning(...) soft failure |
treat_failure_as_warning: true |
pipeline continues with WARNING status |
| Snowflake schema/column type check | integration_job: SNOWFLAKE_SCHEMA_VALIDATION |
no SQL needed |
| Great Expectations checkpoint | integration_job: PYTHON_EXECUTE_SCRIPT |
no native GE integration |
| Soda checks | integration_job: PYTHON_EXECUTE_SCRIPT |
no native Soda integration |
Valid test integration_jobs: SNOWFLAKE_RUN_TEST, GCP_BQ_RUN_TEST, POSTGRES_RUN_TEST, DATABRICKS_RUN_TEST, SQL_SERVER_RUN_QUERY
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 · 177 lines · 98 tokens per session scan A eca821b7ccf8
prefect-testing-to-orchestra is a skill published in the GitHub repository orchestra-hq/orchestra-skills (9 stars, last pushed 3d ago), licensed MIT. It adds 98 tokens to every session and 2,074 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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