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 dbt-core-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/dbt-core-prefect-to-orchestra)<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/dbt-core-prefect-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/dbt-core-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/dbt-core-prefect-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/dbt-core-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 | $0.00103 | $0.01785 |
| Opus 5 | $0.00051 | $0.00892 |
| Sonnet 5 | $0.00021 | $0.00357 |
| Haiku 4.5 | $0.00010 | $0.00178 |
Grade A, and why
dbt-core-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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Converts Prefect dbt tasks into Orchestra pipeline YAML. Two dbt variants exist:
- dbt Core (
DbtCoreOperation,ShellOperationrunningdbtCLI):integration: DBT_CORE,integration_job: DBT_CORE_EXECUTE - dbt Cloud (
DbtCloudJob):integration: DBT,integration_job: DBT_RUNJOB
The commands list from Prefect is joined into a single semicolon-delimited string. Warehouse credentials and Git repo details go on the Orchestra dbt Core connection, not in YAML.
Parameter Mapping
| Prefect construct | Orchestra field | Notes |
|---|---|---|
DbtCoreOperation(commands=[...]) |
parameters.commands |
Join list items with ; — e.g. 'dbt seed; dbt build;' |
| Python version | parameters.python_version |
'3.11' or '3.12' |
| pip / poetry / uv | parameters.package_manager |
PIP, POETRY, or UV — infer from visible project files (poetry.lock/pyproject.toml → POETRY, uv.lock → UV, requirements.txt/Pipfile → PIP). If none of that is visible, don't silently guess PIP — see Gotchas |
| Git repo + profiles.yml / warehouse creds | connection: |
Set up in Connectors → dbt Core |
--select, --exclude, --target flags |
inline in parameters.commands |
e.g. dbt build --select tag:daily --target prod; |
project_dir |
parameters.project_dir |
A real task parameter — the subdirectory holding dbt_project.yml. Carry the value over; null if the project is at the repo root |
DbtCloudJob(job_id=..., account_id=...) |
integration: DBT, integration_job: DBT_RUNJOB, parameters.job_id |
account_id goes on the Orchestra dbt Cloud connection |
ShellOperation(commands=["dbt build"]) |
same as DbtCoreOperation |
Treat identically |
Orchestra YAML Structure
dbt Core:
version: v1
name: dbt-flow
pipeline:
stage-001:
tasks:
task-001:
integration: DBT_CORE
integration_job: DBT_CORE_EXECUTE
name: dbt_daily_build
connection: data_dbt_bigquery_prod_12345
parameters:
commands: 'dbt seed; dbt build --select tag:daily --target prod;'
package_manager: PIP
python_version: '3.12'
project_dir: null # optional — subdirectory holding dbt_project.yml, e.g. 'dbt' in a monorepo
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 · 148 lines · 103 tokens per session scan A ac545a524f17
dbt-core-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 103 tokens to every session and 1,785 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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