airflow
Queries, manages, and troubleshoots Apache Airflow using the af CLI.
Skill by Kilo-Org
Chalk Labs
Need help deploying airflow?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/kilo-org-airflow
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: backend, data, writing, productivity
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Shell scripts
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/airflow
What's inside
- Skill name
airflow- Description
- Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.
- License
- Apache-2.0
- Metadata
- category: data
- Instructions
- 389 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (4)
- LICENSE
- SKILL.md
- api-reference.md
- warm-uvx-cache.sh · hooks/
SKILL.md (first lines)
# Airflow Operations
Use `af` commands to query, manage, and troubleshoot Airflow workflows.
## Astro CLI
The [Astro CLI](https://www.astronomer.io/docs/astro/cli/overview) is the recommended way to run Airflow locally and deploy to production. It provides a containerized Airflow environment that works out of the box:
```bash
# Initialize a new project
astro dev init
# Start local Airflow (webserver at http://localhost:8080)
astro dev start
# Parse DAGs to catch errors quickly (no need to start Airflow)
astro dev parse
# Run pytest against your DAGs
astro dev pytest
# Deploy to production
astro deploy # Full deploy (image + DAGs)
astro deploy --dags # DAG-only deploy (fast, no image build)
```
For more details:
- **New project?** See the **setting-up-astro-project** skill
- **Local environment?** See the **managing-astro-local-env** skill
- **Deploying?** See the **deploying-airflow** skill
---
## Running the CLI
These commands assume `af` is on PATH. Run via `astro otto` to get it automatically, or install standalone with `uv tool install astro-airflow-mcp`.
## Instance Configuration
Manage multiple Airflow instances with persistent configuration:
```bash
# Add a new instance
af instance add prod --url https://airflow.example.com --token "$API_TOKEN"
af instance add staging --url https://staging.example.com --username admin --password admin
# List and switch instances
af instance list # Shows all instances in a table
af instance use prod # Switch to prod instance
af instance current # Show current instance
af instance delete old-instance
# Auto-discover instances (use --dry-run to preview first)
af instance discover --dry-run # Preview all discoverable instances
af instance discover # Discover from all backends (astro, local)
af instance discover astro # Discover Astro deployments only
af instance discover astro --all-workspaces # Include all accessible workspaces
af instance discover local # Scan common local Airflow ports
af instance discover local --scan # Deep scan all ports 1024-65535
# IMPORTANT: Always run with --dry-run first and ask for user consent before
# running discover without it. The non-dry-run mode creates API tokens in
# Astro Cloud, which is a sensitive action that requires explicit approval.
# Show where an instance came from (file path + scope)
af instance show prod
# Override instance for a single command via env vars
AIRFLOW_API_URL=https://staging.example.com AIRFLOW_AUTH_TOKEN=$STG af dags list
# Or switch persistently
af instance use staging
```
Config layout (mirrors `git config` system/global/local):
| Scope | File | Committed? |
|---|---|---|
| Global | `~/.astro/config.yaml` | n/a (per-user) |
| Project shared | `<root>/.astro/config.yaml` | yes |
| Project local | `<root>/.astro/config.local.yaml` | no (gitignored) |
`<root>` is found by walking up from cwd looking for `.astro/`. Default write routing inside a project: `add`/`discover` → project-shared, `use` → project-local. Override with `--global` / `--project` / `--local`. Set `AF_CONFIG=<path>` to bypass layering and use a single file.
Migrate from the legacy `~/.af/config.yaml` with `af migrate` (idempotent; renames the old file to `.bak`).
Tokens in config can reference environment variables using `${VAR}` syntax:
```yaml
instances:
- name: prod
url: https://airflow.example.com
auth:
token: ${AIRFLOW_API_TOKEN}
```
Or use environment variables directly (no config file needed):
```bash
export AIRFLOW_API_URL=http://localhost:8080
export AIRFLOW_AUTH_TOKEN=your-token-here
# Or username/password:
export AIRFLOW_USERNAME=admin
export AIRFLOW_PASSWORD=admin
```
Or CLI flags: `af --airflow-url http://localhost:8080 --token "$TOKEN" <command>`
## Quick Reference
| Command | Description |
|---------|-------------|
| `af health` | System health check |
| `af dags list` | List all DAGs |
| `af dags get <dag_id>` | Get DAG details |
| `af dags explore <dag_id>` | Full DAG investigation |
| `af dags source <dag_id>` | Get DAG source code |
| `af dags pause <dag_id>` | Pause DAG scheduling |
| `af dags unpause <dag_id>` | Resume DAG scheduling |
| `af dags errors` | List import errors |From Kilo-Org/kilo-marketplace, Apache-2.0.
More from Kilo-Org/kilo-marketplace
- 0.795score
dynamic-tables-tutorial
Snowflake
Interactive tutorial that teaches Snowflake Dynamic Tables hands-on.
SkillAFreshness: A★ 195Claude Code +9 - 0.795score
cortex-code
Snowflake Integration Team
Routes Snowflake-related operations to Cortex Code CLI for specialized Snowflake expertise.
SkillAFreshness: A★ 195Claude Code +9 - 0.795score
dbt-analytics-engineering
dbt-labs
Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show.
SkillAFreshness: A★ 195Claude Code +9 - 0.795score
database-redshift
christopher huffman
Amazon Redshift expert.
SkillAFreshness: A★ 195Claude Code +9 - 0.791score
jupyter-notebook
Kilo
Use whenever the user works with Jupyter notebooks (`.ipynb`), including creating, inspecting, editing, executing, or visualizing notebook content for…
SkillAFreshness: A★ 195Claude Code +9 - 0.791score
gcp-event-driven-architecture-review
github: Raishin
Review GCP Pub/Sub, Eventarc, Cloud Tasks, Cloud Scheduler, and Workflows designs — dead-letter topics, message ordering, idempotency, fan-out blast radius…
SkillAFreshness: A★ 195Claude Code +9 - 0.789score
dbt
dbt-labs
Skills for analytics engineering with dbt - building models, writing tests, querying the semantic layer, troubleshooting jobs, and more.
SkillAFreshness: A★ 195Claude Code +9 - 0.786score
data-investigation
Pedro / Kilo Code
A workflow for conducting rigorous, reproducible data investigations and ad-hoc analyses.
SkillAFreshness: A★ 195Claude Code +9 - 0.786score
apache-arrow
terminal-skills
Expert guidance for Apache Arrow, the cross-language columnar memory format for analytics workloads.
SkillAFreshness: A★ 195Claude Code +9 - 0.786score
dagster
terminal-skills
Dagster is a data pipeline orchestrator built around the concept of software-defined assets.
SkillAFreshness: A★ 195Claude Code +9 - 0.786score
cassandra
terminal-skills
Apache Cassandra is a distributed NoSQL database designed for high availability and linear scalability.
SkillAFreshness: A★ 195Claude Code +9 - 0.786score
prefect
terminal-skills
Prefect is a modern workflow orchestration framework for Python data pipelines.
SkillAFreshness: A★ 195Claude Code +9
Signals
- Stars
- 195
- Forks
- 328
- Open issues
- 18
- Last commit (this folder)
- Jun 24, 2026 (4 months ago)
- Last release
- Sep 28, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/Kilo-Org/kilo-marketplace
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
Same skill elsewhere
Copies of this SKILL.md published by others; search shows only one of them.
More from Kilo-Org
- 0.661score
angular-tooling
Kilo-Org
Use Angular CLI and development tools effectively in Angular v20+ projects.
SkillAFreshness: A★ 195Claude Code +9 - 0.661score
angular-http
Kilo-Org
Implement HTTP data fetching in Angular v20+ using resource(), httpResource(), and HttpClient.
SkillAFreshness: A★ 195Claude Code +9 - 0.661score
elasticsearch-file-ingest
Kilo-Org
Ingest and transform data files (CSV/JSON/Parquet/Arrow IPC) into Elasticsearch with stream processing and custom transforms.
SkillAFreshness: A★ 195Claude Code +9 - 0.661score
senior-data-engineer
Kilo-Org
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure.
SkillAFreshness: A★ 195Claude Code +9 - 0.661score
sql-server-table-reconciliation
Kilo-Org
Use when: comparing SQL Server tables across instances, data migration validation, ETL verification, row mismatch detection, schema drift, reconciliation…
SkillAFreshness: A★ 195Claude Code +9 - 0.661score
dataset-transformation
Kilo-Org
Generates code that transforms datasets between ML schemas for model training or evaluation.
SkillAFreshness: A★ 195Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get airflow running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
airflow skill. Agent Mart. https://agentmart.dev/skills/kilo-org-airflow (last updated 2026-10-24)