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 Kilo-Org/kilo-marketplace --skill airflowgit clone --depth 1 https://github.com/Kilo-Org/kilo-marketplaceWrote 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/kilo-org/kilo-marketplace/airflow)<a href="https://agentmods.dev/skills/kilo-org/kilo-marketplace/airflow"><img src="https://agentmods.dev/badge/skills/kilo-org/kilo-marketplace/airflow/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/kilo-org/kilo-marketplace/airflow"><img src="https://agentmods.dev/badge/skills/kilo-org/kilo-marketplace/airflow.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 | $0.00161 | $0.03964 |
| Opus 5 | $0.00081 | $0.01982 |
| Sonnet 5 | $0.00032 | $0.00793 |
| Haiku 4.5 | $0.00016 | $0.00396 |
Grade A, and why
airflow 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 10d 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.
This is a copy
89% identical to airflow — 21 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 410 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Airflow Operations
Use af commands to query, manage, and troubleshoot Airflow workflows.
Astro CLI
The Astro CLI is the recommended way to run Airflow locally and deploy to production. It provides a containerized Airflow environment that works out of the box:
# 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:
# 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
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 410 lines · 161 tokens per session scan A 68aeb12a6868
airflow is a skill published in the GitHub repository Kilo-Org/kilo-marketplace (175 stars, last pushed 20d ago), licensed Apache-2.0. It adds 161 tokens to every session and 3,964 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to airflow, differing in 21 lines, and is treated as a copy.
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