Senior Data Engineer
benchflow-ai/skillsbench
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure.
Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer).
Install Managed Airflow Dag Authoring
$ npx skills add google/skills --skill managed-airflow-dag-authoring -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google/skills managed-airflow-dag-authoring --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/managed-airflow-dag-authoring .claude/skills/managed-airflow-dag-authoring && rm -rf skills-srcUse ~/.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/
Install the "managed-airflow-dag-authoring" agent skill from https://github.com/google/skills/tree/main/skills/cloud/managed-airflow-dag-authoring into .claude/skills/managed-airflow-dag-authoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "managed-airflow-dag-authoring", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google/skills/tree/main/skills/cloud/managed-airflow-dag-authoringType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google/skills --skill managed-airflow-dag-authoring -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google/skills managed-airflow-dag-authoring --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cloud/managed-airflow-dag-authoring .agents/skills/managed-airflow-dag-authoring && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "managed-airflow-dag-authoring" agent skill from https://github.com/google/skills/tree/main/skills/cloud/managed-airflow-dag-authoring into .agents/skills/managed-airflow-dag-authoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "managed-airflow-dag-authoring", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill managed-airflow-dag-authoring -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google/skills managed-airflow-dag-authoring --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cloud/managed-airflow-dag-authoring .cursor/skills/managed-airflow-dag-authoring && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "managed-airflow-dag-authoring" agent skill from https://github.com/google/skills/tree/main/skills/cloud/managed-airflow-dag-authoring into .cursor/skills/managed-airflow-dag-authoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "managed-airflow-dag-authoring", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google/skills.git --path skills/cloud/managed-airflow-dag-authoring--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google/skills --skill managed-airflow-dag-authoring -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google/skills managed-airflow-dag-authoring --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cloud/managed-airflow-dag-authoring .gemini/skills/managed-airflow-dag-authoring && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "managed-airflow-dag-authoring" agent skill from https://github.com/google/skills/tree/main/skills/cloud/managed-airflow-dag-authoring into .gemini/skills/managed-airflow-dag-authoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "managed-airflow-dag-authoring", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google/skills managed-airflow-dag-authoringInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google/skills --skill managed-airflow-dag-authoring -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cloud/managed-airflow-dag-authoring .github/skills/managed-airflow-dag-authoring && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "managed-airflow-dag-authoring" agent skill from https://github.com/google/skills/tree/main/skills/cloud/managed-airflow-dag-authoring into .github/skills/managed-airflow-dag-authoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "managed-airflow-dag-authoring", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill managed-airflow-dag-authoring -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google/skills managed-airflow-dag-authoring --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cloud/managed-airflow-dag-authoring .opencode/skills/managed-airflow-dag-authoring && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "managed-airflow-dag-authoring" agent skill from https://github.com/google/skills/tree/main/skills/cloud/managed-airflow-dag-authoring into .opencode/skills/managed-airflow-dag-authoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "managed-airflow-dag-authoring", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
managed-airflow-dag-authoring- GitHub stars
- 21k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 570 words
- Files
- 1
- Skills in repo
- 147
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer).
- Works in 3 steps: Context Discovery → DAG Authoring Best Practices → Validation Process
- Extending an Airflow DAG
- SKILL.md covers Phase 1: Context Discovery, Phase 2: DAG Authoring Best…, Phase 3: Validation Process and Definition of Done
- Calls ruff and gcloud
What it does
Managed Airflow Dag Authoring is an agent skill from google/skills, published by the product's own GitHub organization. Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Data pipelines and ETL. It works with Apache Airflow and Python. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.
When your agent uses it
- Extending an Airflow DAG
- Authoring Python code unrelated to Airflow DAGs
Example prompts
- “Use the managed-airflow-dag-authoring skill to provide guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA…”
- “/managed-airflow-dag-authoring”
Requirements
- Python 3
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 5120a76. 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:
ruffgcloud
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use gcloud, 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.
Context cost
Managed Airflow Dag Authoring loads about 1.5k tokens when it runs. Until then it costs about 99 tokens; SKILL.md has 570 words of instructions outside code blocks.
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 google/skills at commit 5120a76, republished under its Apache-2.0 licence (© google). 570 words, ~1,460 tokens.
Download SKILL.mdSave it as
.claude/skills/managed-airflow-dag-authoring/SKILL.md (or your agent's skills folder).- name
- managed-airflow-dag-authoring
- description
- Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.
- metadata.version
- 1.0.0
- metadata.category
- BigDataAndAnalytics
GCP Managed Airflow DAG Authoring Guide
This skill guides you through authoring and validating Apache Airflow DAGs for Managed Service for Apache Airflow (MSAA; formerly Cloud Composer) environments.
Phase 1: Context Discovery
Before writing any DAG code, you MUST understand the constraints (e.g. version of Airflow) and capabilities of your target environment if user is willing to provide them.
1.1 Identify Target Environment & Access
Determine if you have direct access to the target Managed Airflow environment, local development environment or if you are working offline (only changing local files without validation).
- If environment access is available: Use
gcloudto inspect the environment (see Section 1.3). - If offline: Rely on user provided details.
1.2 Identify Development Environment
Determine if a local development environment is available.
- Check if
composer-devCLI is installed. - Check if a local Python environment with
airflowis available.
1.3 Inspect Target Environment (if available and requested)
Run the following commands to discover version constraints:
Get Airflow/Image Version:
bash gcloud composer environments describe {env_name} \ --location {region} \ --format="value(config.softwareConfig.imageVersion)"Get Installed Packages (Versions):
bash gcloud composer environments describe {env_name} \ --location {region} \ --format="value(config.softwareConfig.pypiPackages)"Get DAGs GCS Bucket:
bash gcloud composer environments describe {env_name} \ --location {region} \ --format="value(config.dagGcsPrefix)"
Phase 2: DAG Authoring Best Practices
2.1 General Airflow Best Practices
- Idempotency: Every task SHOULD be idempotent. Running it multiple times with the same inputs (e.g., execution date) SHOULD produce the same result and not duplicate data.
- No Top-Level Code Execution: Do NOT execute database queries, external API calls, or heavy computations at the top level of the DAG file (outside of tasks/operators). This code runs every few seconds during DAG parsing and will degrade performance.
- Explicit Catchup: Always set
catchup=Falsein the DAG definition unless historical backfilling is explicitly required. - Use Airflow Variables/Connections: Never hardcode credentials or
environment-specific configs. Use
Variable.get()(withdeserialize_json=Trueif applicable) andBaseHook.get_connection(). Access variables via Jinja templates (e.g.,{{ var.value.my_var }}) to avoid database calls during DAG parsing.
2.2 Airflow 2 vs Airflow 3 Compatibility
Use managed-airflow-migrations skill to navigate adjusting the code to specific target Airflow version.
Show full SKILL.md (231 more words)Show less
Phase 3: Validation Process
You MUST validate DAGs before concluding your task.
3.1 Local Validation (Offline/Pre-deployment)
3.1.1 Static Analysis & Linting
Use ruff or pylint if available.
ruff check path/to/dag.py- If targeting Airflow 3, check with Airflow 3 rules if rulesets are available.
3.1.2 Local Dev Environment (composer-dev)
If the user has composer-dev configured:
Copy the DAG to the local directory with DAGs:
bash cp path/to/dag.py $(composer-dev describe {local_env} --format="value(dags_directory)")Verify parsing:
bash composer-dev run-airflow-cmd {local_env} dags list-import-errors
3.2: Target Environment Validation
Only perform these steps if you have GCP access and are authorized to deploy to a target environment.
3.2.1 Deploy to GCS
Upload the DAG to the target environment's GCS bucket:
gcloud storage cp path/to/dag.py gs://{target_bucket}/dags/3.2.2 Verify via Airflow CLI
Wait 1-2 minutes for the scheduler to parse the file, then run:
Check for Import Errors:
bash gcloud composer environments run {env_name} \ --location {region} \ dags list-import-errors
Pass Criteria: Output should be "No data found" or empty.
Verify DAG is Listed:
bash gcloud composer environments run {env_name} \ --location {region} \ dags list | grep {dag_id}
3.2.3 Monitor Cloud Logging
Check for runtime parsing errors in Cloud Logging:
resource.type="cloud_composer_environment"
resource.labels.environment_name="{env_name}"
log_id("airflow-scheduler")
severity>=ERROR
textPayload:"{dag_file_name}"Definition of Done
- DAG code adheres to Airflow version constraints of the target environment.
- DAG code follows best practices (no top-level execution, idempotent if possible).
- DAG parses locally without import errors.
- (If environment is available) DAG is deployed to the target environment and verified to have no import errors.
© google, 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
Just SKILL.md in skills/cloud/managed-airflow-dag-authoring of google/skills.
Open the folder on GitHubat commit 5120a76
Compare with similar skills
Managed Airflow Dag Authoring 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Managed Airflow Dag Authoring this skillgoogle/skills | 21k | — | ~1.5k | Automated safety check: Pass | Apache-2.0 | |
| Senior Data Engineerbenchflow-ai/skillsbench | 1.8k | — | ~5.9k | Automated safety check: Pass | MIT | |
| Airflow DAG Patternswshobson/agents | 40k | 9 repos | ~784 | Automated safety check: Pass | MIT | |
| Version Bumpergodatadriven/whirl | 205 | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Senior Data Engineeralirezarezvani/claude-skills | 28k | 3 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Authoring Go SDK Tasksastronomer/agents | 451 | — | ~2.1k | Automated safety check: Pass | Apache-2.0 |
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Questions about Managed Airflow Dag Authoring
What does Managed Airflow Dag Authoring do?
Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Managed Airflow Dag Authoring is an agent skill from google/skills, published by the product's own GitHub organization. Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer).
When should I use Managed Airflow Dag Authoring?
Managed Airflow Dag Authoring fits situations like: extending an Airflow DAG; authoring Python code unrelated to Airflow DAGs.
How do I install Managed Airflow Dag Authoring in Claude Code?
Run `npx skills add google/skills --skill managed-airflow-dag-authoring -a claude-code`. Or copy the skill folder (skills/cloud/managed-airflow-dag-authoring in google/skills) into .claude/skills/managed-airflow-dag-authoring in your project. Claude Code loads it when a task matches its description.
How do I install Managed Airflow Dag Authoring in Codex?
Run `npx skills add google/skills --skill managed-airflow-dag-authoring -a codex`. Or copy the skill folder (skills/cloud/managed-airflow-dag-authoring in google/skills) into .agents/skills/managed-airflow-dag-authoring in your project. Codex loads it when a task matches its description.
Can I use Managed Airflow Dag Authoring 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 google/skills --skill managed-airflow-dag-authoring -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/managed-airflow-dag-authoring, .gemini/skills/managed-airflow-dag-authoring, .github/skills/managed-airflow-dag-authoring and .opencode/skills/managed-airflow-dag-authoring in your project.
What does Managed Airflow Dag Authoring need to run?
Going by SKILL.md and its folder, Managed Airflow Dag Authoring needs the command-line tools its instructions call (ruff and gcloud). Our summary lists: Python 3.
Does Managed Airflow Dag Authoring access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Managed Airflow Dag Authoring 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 Managed Airflow Dag Authoring use?
Managed Airflow Dag Authoring is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Managed Airflow Dag Authoring use?
About 1.5k 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 Managed Airflow Dag Authoring?
Skills that share tags, products or a category with Managed Airflow Dag Authoring: Senior Data Engineer (benchflow-ai/skillsbench, 1.8k stars), Airflow DAG Patterns (wshobson/agents, 40k stars), Version Bumper (godatadriven/whirl, 205 stars) and Senior Data Engineer (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Managed Airflow Dag Authoring?
google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,069 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on October 9, 2026.
Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.