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dbt-core-dagster-to-orchestra

dbt-core-dagster-to-orchestra is a skill for Claude Code from orchestra-hq/orchestra-skills. It costs 107 tokens per session (2,793 once invoked), scanned A, original, MIT.

A guide for converting Dagster's dbt Core integration into an Orchestra pipeline task. dbt Core is a command-line tool for transforming data, and Dagster and Orchestra are systems for scheduling and running data workflows.

In plain words
What is it for?
Use it when rewriting Dagster code that runs dbt commands or reads dbt project assets as an Orchestra DBT_CORE task.
Why use it?
It explains how to translate dbt commands, project locations, Python versions, package managers, repository connections, and dependencies into Orchestra settings.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the migrate-to-orchestra plugin — 52 skills shipped together

not rated 9repo changed 7d ago A scan 107 tokens original MIT

Good fit Use it when rewriting Dagster code that runs dbt commands or reads dbt project assets as an Orchestra DBT_CORE task.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/orchestra-hq/orchestra-skills/dbt-core-dagster-to-orchestra
View source ↗ orchestra-hq/orchestra-skills
Install

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.

Any agent
npx skills add orchestra-hq/orchestra-skills --skill dbt-core-dagster-to-orchestra
Clone the repo
git clone --depth 1 https://github.com/orchestra-hq/orchestra-skills

Made for: Claude Code.

Or install migrate-to-orchestra, the plugin that ships this one along with the rest of its 52 skills.

Wrote 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.

agentmods badge for dbt-core-dagster-to-orchestra

README.md
[![agentmods](https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/dbt-core-dagster-to-orchestra/github.svg)](https://agentmods.dev/skills/orchestra-hq/orchestra-skills/dbt-core-dagster-to-orchestra)
Your own site
<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/dbt-core-dagster-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/dbt-core-dagster-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.

agentmods 80×15 button for dbt-core-dagster-to-orchestra

Your own site · 80×15
<a href="https://agentmods.dev/skills/orchestra-hq/orchestra-skills/dbt-core-dagster-to-orchestra"><img src="https://agentmods.dev/badge/skills/orchestra-hq/orchestra-skills/dbt-core-dagster-to-orchestra.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 107 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,793 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00107 $0.02793
Opus 5.5 $0.00043 $0.01117
Sonnet 5.5 $0.00021 $0.00559
Haiku 4.5 $0.00011 $0.00279

Measured 7d ago against content hash cacc14e09f7e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

dbt-core-dagster-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 7d 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.

skills/migrate-to-orchestra/skills/dbt-core-dagster-to-orchestra/SKILL.md · 177 lines

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.

dbt Core: Dagster -> Orchestra Conversion

Overview

In Dagster, dbt Core is a flagship integration via dagster-dbt: a DbtCliResource runs the dbt CLI and @dbt_assets parses the dbt manifest.json to produce asset-level lineage. The decorated function streams dbt.cli([...]) commands.

In Orchestra, dbt Core is a first-class integration. A single Execute task under DBT_CORE replaces one or more @dbt_assets. Orchestra pulls the dbt project from a connected Git repository and runs the commands you specify.

Parameter Mapping

Dagster concept Orchestra YAML field Notes
dbt.cli(["build", "--select", ...]) parameters.commands Semicolon-separated dbt CLI string
Python environment parameters.python_version e.g. '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
DbtProject(project_dir=...) (Git repo + project) connection: for the repo/credentials, parameters.project_dir for the path The connection stores the repo URL; project_dir is a real task parameter — set it to the same subdirectory value, null if the project is at the repo root
--project-dir parameters.project_dir Same as above — carry the path over, don't drop it
profiles_dir / profiles.yml Configured on the connection Warehouse creds live on the connection
--select / --exclude Include in commands e.g. dbt build --select models/marts
--target Include in commands or on connection e.g. dbt run --target prod
asset key / op name name: Human-readable task name

Orchestra YAML Structure

version: v1
name: <pipeline-name>
pipeline:
  <stage-uuid>:
    tasks:
      <task-uuid>:
        integration: DBT_CORE
        integration_job: DBT_CORE_EXECUTE
        name: <descriptive name>
        connection: <orchestra-dbt-core-connection-name>
        parameters:
          commands: 'dbt seed; dbt build --select tag:daily;'
          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: []

Read the full file on GitHub · 177 lines

Changes

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.

  1. 7d ago Changed · +1 lines cacc14e09f7e
  2. 25d ago Changed · +24 lines dbce84f6147f
  3. 2mo ago First seen · 152 lines · 107 tokens per session scan A 09b1930f0367

Subscribe to this mod's changes

dbt-core-dagster-to-orchestra is a skill published in the GitHub repository orchestra-hq/orchestra-skills (9 stars, last pushed yesterday), licensed MIT. It adds 107 tokens to every session and 2,793 once invoked, about $0.0004 per session on Opus 5.5. 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.

Data and AIDevOps and cloud
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