Agent skill

Dagster

by Kilo-Org in Kilo-Org/kilo-marketplace

Dagster is a data pipeline orchestrator built around the concept of software-defined assets.

Apache-2.0Auto-check passedData & Analytics

Install Dagster

skills CLI
$ npx skills add Kilo-Org/kilo-marketplace --skill dagster -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Kilo-Org/kilo-marketplace dagster --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dagster .claude/skills/dagster && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
dagster
GitHub stars
190
Used in
1 other repo
Token cost
~1.4k tokens
SKILL.md length
43 words
Files
3
Skills in repo
85
Repo updated
First seen
Licence
Apache-2.0

At a glance

Dagster is a data pipeline orchestrator built around the concept of software-defined assets.

  • Tasks that involve Data pipelines and ETL
  • SKILL.md covers Installation, Software-Defined Assets, Resources and Assets with Resources, plus 4 more sections
  • Calls pip

What it does

Dagster is an agent skill from Kilo-Org/kilo-marketplace. Dagster is a data pipeline orchestrator built around the concept of software-defined assets. Learn to define assets, ops, jobs, schedules, sensors, and resources for building maintainable data platforms.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `_scores.json`). Compatibility notes: macos, linux, windows

It sits in Data & Analytics, covering Data pipelines and ETL. It works with Dagster. The repository describes itself as: Kilo Marketplace - A curated collection of Skills, MCP Servers, and Modes for enhancing AI agent capabilities across the Kilo ecosystem—including Kilo Code (VS Code extension)… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Data pipelines and ETL

Example prompts

  • “/dagster”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): macos, linux, windows

What it can do on your machine

Read from SKILL.md and the folder at commit ff51758. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    macos, linux, windows

    From compatibility in the SKILL.md frontmatter.

Context cost

Dagster loads about 1.4k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 43 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Kilo-Org/kilo-marketplace at commit ff51758, republished under its Apache-2.0 licence (© Kilo-Org). 43 words, ~1,447 tokens.

Download SKILL.mdSave it as .claude/skills/dagster/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
dagster
description
Dagster is a data pipeline orchestrator built around the concept of software-defined assets. Learn to define assets, ops, jobs, schedules, sensors, and resources for building maintainable data platforms.
compatibility
macos, linux, windows
metadata.author
terminal-skills
metadata.version
1.0.0
metadata.category
data
metadata.tags
dagster, data-pipeline, orchestration, assets, python

Dagster

Dagster organizes data pipelines around software-defined assets — declarations of the data artifacts your pipeline produces. Assets track lineage, enable incremental computation, and integrate with the Dagster UI.

Installation

bash
# Install Dagster and UI
pip install dagster dagster-webserver

# Create a new project
dagster project scaffold --name my_pipeline
cd my_pipeline
pip install -e ".[dev]"

# Start the dev server
dagster dev
# UI at http://localhost:3000

Software-Defined Assets

python
# my_pipeline/assets.py: Define assets that produce data
from dagster import asset, AssetExecutionContext
import pandas as pd

@asset(group_name="raw")
def raw_users(context: AssetExecutionContext) -> pd.DataFrame:
    """Fetch raw user data from API."""
    import httpx
    response = httpx.get("https://api.example.com/users")
    df = pd.DataFrame(response.json())
    context.log.info(f"Fetched {len(df)} users")
    return df

@asset(group_name="raw")
def raw_orders(context: AssetExecutionContext) -> pd.DataFrame:
    """Fetch raw order data from API."""
    import httpx
    response = httpx.get("https://api.example.com/orders")
    return pd.DataFrame(response.json())

@asset(group_name="analytics", deps=[raw_users, raw_orders])
def revenue_by_user(raw_users: pd.DataFrame, raw_orders: pd.DataFrame) -> pd.DataFrame:
    """Calculate total revenue per user."""
    merged = raw_orders.merge(raw_users, left_on="user_id", right_on="id")
    result = (
        merged.groupby(["user_id", "name"])
        .agg(total_revenue=("amount", "sum"), order_count=("id_x", "count"))
        .reset_index()
    )
    return result

Resources

python
# my_pipeline/resources.py: Configurable resources for external systems
from dagster import resource, ConfigurableResource
import sqlalchemy

class DatabaseResource(ConfigurableResource):
    connection_string: str

    def query(self, sql: str) -> list:
        engine = sqlalchemy.create_engine(self.connection_string)
        with engine.connect() as conn:
            result = conn.execute(sqlalchemy.text(sql))
            return [dict(row._mapping) for row in result]

    def execute(self, sql: str):
        engine = sqlalchemy.create_engine(self.connection_string)
        with engine.connect() as conn:
            conn.execute(sqlalchemy.text(sql))
            conn.commit()

Assets with Resources

python
# my_pipeline/db_assets.py: Assets that use database resources
from dagster import asset, AssetExecutionContext
from .resources import DatabaseResource

@asset(group_name="warehouse")
def dim_users(context: AssetExecutionContext, database: DatabaseResource):
    """Load cleaned user dimension table into warehouse."""
    users = database.query("SELECT id, name, email, created_at FROM raw_users")
    context.log.info(f"Loaded {len(users)} users into warehouse")
    return users

Definitions

python
# my_pipeline/__init__.py: Wire everything together
from dagster import Definitions, load_assets_from_modules
from . import assets, db_assets
from .resources import DatabaseResource

all_assets = load_assets_from_modules([assets, db_assets])

defs = Definitions(
    assets=all_assets,
    resources={
        "database": DatabaseResource(
            connection_string="postgresql://user:pass@localhost:5432/analytics"
        ),
    },
)

Schedules and Sensors

python
# my_pipeline/schedules.py: Time-based and event-based triggers
from dagster import (
    ScheduleDefinition,
    define_asset_job,
    sensor,
    RunRequest,
    SensorEvaluationContext,
    AssetSelection,
)

# Job that materializes specific assets
analytics_job = define_asset_job(
    name="analytics_job",
    selection=AssetSelection.groups("analytics"),
)

# Cron schedule
daily_analytics = ScheduleDefinition(
    job=analytics_job,
    cron_schedule="0 6 * * *",  # 6 AM daily
)

# Sensor — trigger on external event
@sensor(job=analytics_job, minimum_interval_seconds=60)
def new_file_sensor(context: SensorEvaluationContext):
    import os
    files = os.listdir("/data/incoming")
    new_files = [f for f in files if f.endswith(".csv")]
    if new_files:
        context.log.info(f"Found {len(new_files)} new files")
        yield RunRequest(run_key=new_files[0])

Partitioned Assets

python
# my_pipeline/partitioned.py: Time-partitioned assets for incremental processing
from dagster import asset, DailyPartitionsDefinition

daily_partitions = DailyPartitionsDefinition(start_date="2026-01-01")

@asset(partitions_def=daily_partitions, group_name="raw")
def daily_events(context):
    """Fetch events for a specific date partition."""
    date = context.partition_key  # e.g., "2026-02-19"
    context.log.info(f"Processing events for {date}")
    # Fetch only this date's data
    return fetch_events(date)

CLI Reference

bash
# cli.sh: Common Dagster CLI commands
# Development server
dagster dev

# Materialize assets
dagster asset materialize --select raw_users,raw_orders

# List assets
dagster asset list

# Run a job
dagster job execute -j analytics_job

# Check definitions
dagster definitions validate

© Kilo-Org, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in skills/dagster of Kilo-Org/kilo-marketplace.

  • SKILL.md
  • LICENSE
  • _scores.json

Open the folder on GitHubat commit ff51758

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in Kilo-Org/kilo-marketplace, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Dagster next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Dagster compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Dagster this skillKilo-Org/kilo-marketplace1901 repos~1.4kAutomated safety check: PassApache-2.0
Migrate To Riversion-elgreco/rivers102—~1.2kAutomated safety check: PassApache-2.0
Migrating Dagster To Airflowastronomer/agents451—~3.8kAutomated safety check: PassApache-2.0
Dagster Expertdagster-io/skills211—~2kAutomated safety check: PassApache-2.0
Dagster Expertdagster-io/skills211—~321Automated safety check: PassApache-2.0
AI Data Engineeringancoleman/ai-design-components5261 repos~3.5kAutomated safety check: PassMIT

Similar skills

  • Migrate To Rivers

    ion-elgreco/rivers

    Translate a Dagster or Prefect project to rivers, the Rust-powered asset orchestrator.

    102 GitHub stars~1.2k tokensUpdated yesterday
    Data & AnalyticsAuto-check passed
  • Guide for migrating Dagster projects to Apache Airflow 3 on Astro.

    451 GitHub stars~3.8k tokensUpdated yesterday
    Data & AnalyticsAuto-check passed
  • Dagster Expert

    dagster-io/skills

    Official

    Expert guidance for working with Dagster and the dg CLI. An agent skill from dagster-io/skills.

    211 GitHub stars~2k tokensUpdated yesterday
    Data & AnalyticsAuto-check passed
  • Dagster Expert

    dagster-io/skills

    Official

    A skill your agent uses for any task involving Dagster, the dg CLI, assets, materialization, components, data tools or data pipelines, matching the situations the Dagster skill handles.

    211 GitHub stars~321 tokensUpdated yesterday
    Data & AnalyticsAuto-check passed
  • AI Data Engineering

    ancoleman/ai-design-components

    Data pipelines, feature stores, and embedding generation for AI/ML systems.

    526 GitHub starsUsed in 1 repo~3.5k tokens
    Data & AnalyticsAuto-check passed
  • AI Pipeline Orchestration

    sickn33/agentic-awesome-skills

    Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Data & AnalyticsAuto-check passed

More from Kilo-Org/kilo-marketplace

All 85 skills in this repo
  • AzureML Project Scaffolding

    Kilo-Org/kilo-marketplace

    Sets up and maintains AzureML-ready Python projects as uv workspaces with devcontainers, a Makefile and job YAML, so local runs match cloud jobs and experiments stay reproducible.

    190 GitHub stars~3.1k tokensUpdated 10 days ago
    Auto-check: notes
  • Jupyter Notebook Builder

    Kilo-Org/kilo-marketplace

    Creates, inspects, edits and runs Jupyter notebooks, scaffolding experiment or tutorial notebooks from templates and preferring a Jupyter MCP server over raw JSON edits.

    190 GitHub stars~1.3k tokensUpdated 10 days ago
    Auto-check passed
  • Tableau Dashboard Creator

    Kilo-Org/kilo-marketplace

    Takes a plain-language dashboard request through brand setup, data exploration, planning, an interactive HTML mock and a Tableau implementation spec.

    190 GitHub stars~3.8k tokensUpdated 10 days ago
    Auto-check: notes
  • Elasticsearch File Ingest

    Kilo-Org/kilo-marketplace

    Ingest and transform data files (CSV/JSON/Parquet/Arrow IPC) into Elasticsearch with stream processing and custom transforms.

    190 GitHub stars~2.8k tokensUpdated 10 days ago
    Auto-check passed
  • Nifi Flow Layout

    Kilo-Org/kilo-marketplace

    A skill your agent uses when arranging Apache NiFi processors, process groups, ports, comments, numbering, crossing connections, dense fan-in/fan-out, or reusable readable canvas layouts.

    190 GitHub stars~1.5k tokensUpdated 10 days ago
    Auto-check passed
  • Splunk Ingest Processor Setup

    Kilo-Org/kilo-marketplace

    Render Cisco Data Fabric ingest-time routing workflows and Splunk Cloud Platform Ingest Processor setup plans with SPL2 pipelines, source types, destinations, lifecycle handoffs, queue and…

    190 GitHub stars~1.2k tokensUpdated 10 days ago
    Auto-check passed

Works with

Questions about Dagster

What does Dagster do?

Dagster is a data pipeline orchestrator built around the concept of software-defined assets. Dagster is an agent skill from Kilo-Org/kilo-marketplace. Dagster is a data pipeline orchestrator built around the concept of software-defined assets.

When should I use Dagster?

Dagster fits situations like: tasks that involve Data pipelines and ETL.

How do I install Dagster in Claude Code?

Run `npx skills add Kilo-Org/kilo-marketplace --skill dagster -a claude-code`. Or copy the skill folder (skills/dagster in Kilo-Org/kilo-marketplace) into .claude/skills/dagster in your project. Claude Code loads it when a task matches its description.

How do I install Dagster in Codex?

Run `npx skills add Kilo-Org/kilo-marketplace --skill dagster -a codex`. Or copy the skill folder (skills/dagster in Kilo-Org/kilo-marketplace) into .agents/skills/dagster in your project. Codex loads it when a task matches its description.

Can I use Dagster in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Kilo-Org/kilo-marketplace --skill dagster -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dagster, .gemini/skills/dagster, .github/skills/dagster and .opencode/skills/dagster in your project.

What does Dagster need to run?

Going by SKILL.md and its folder, Dagster needs the command-line tools its instructions call (pip). Our summary lists: Python 3. Compatibility (from SKILL.md): macos, linux, windows.

Does Dagster access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Dagster safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Dagster use?

Dagster is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dagster use?

About 1.4k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Dagster?

Skills that share tags, products or a category with Dagster: Migrate To Rivers (ion-elgreco/rivers, 102 stars), Migrating Dagster To Airflow (astronomer/agents, 451 stars), Dagster Expert (dagster-io/skills, 211 stars) and Dagster Expert (dagster-io/skills, 211 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dagster?

Kilo-Org (a GitHub organization) maintains it in Kilo-Org/kilo-marketplace, which has 190 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on September 28, 2026.

Source: Kilo-Org/kilo-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.