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 astronomer/agents --skill configuring-airflow-language-sdksgit clone --depth 1 https://github.com/astronomer/agentsWrote 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/astronomer/agents/configuring-airflow-language-sdks)<a href="https://agentmods.dev/skills/astronomer/agents/configuring-airflow-language-sdks"><img src="https://agentmods.dev/badge/skills/astronomer/agents/configuring-airflow-language-sdks/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/astronomer/agents/configuring-airflow-language-sdks"><img src="https://agentmods.dev/badge/skills/astronomer/agents/configuring-airflow-language-sdks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00155 | $0.02289 |
| Opus 5.5 | $0.00062 | $0.00916 |
| Sonnet 5.5 | $0.00031 | $0.00458 |
| Haiku 4.5 | $0.00015 | $0.00229 |
Grade A, and why
configuring-airflow-language-sdks 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Configuring Airflow for Language SDKs
To run language SDK tasks, Airflow needs to know two things: which coordinator launches the native subprocess, and which queue routes to that coordinator. The mechanism is identical across every language SDK — only each coordinator's classpath and kwargs differ. This skill documents the shared wiring once, then the per-coordinator options. It is platform-neutral: the same settings apply on open-source Airflow and on managed platforms like Astro.
Experimental. The language SDKs are in preview; configuration keys may change.
For the task code, see authoring-language-sdk-tasks (and the per-language authoring skill, e.g. authoring-java-sdk-tasks, authoring-go-sdk-tasks). For building and shipping the artifact, see the per-language deploy skill (e.g. deploying-java-sdk-bundles, deploying-go-sdk-bundles).
Prerequisites on the worker
- The runtime or artifact the SDK needs must be present on the worker nodes, because the coordinator spawns a native subprocess per task instance. The exact requirement is per-SDK — see Per-coordinator options (the Java SDK needs a JRE 17+; the Go SDK needs no language runtime — the bundle is a self-contained native executable, but it must be built for the worker's OS/arch).
- The compiled/native artifact(s) must be reachable on the worker. See the per-language deploy skill.
- The coordinators ship with the Airflow Task SDK (
apache-airflow-task-sdk, installed with Airflow). No extra Python package is required.
The two settings
Both live in the [sdk] configuration section and apply to every language SDK:
coordinators— a JSON object mapping a coordinator name you choose to its implementation (classpath) and constructorkwargs.queue_to_coordinator— a JSON object mapping a task queue to a coordinator name.
A task whose stub sets queue="..." is handed to the named coordinator, which launches the native subprocess. The coordinator name is arbitrary — it just has to be the same string in both settings. The queue name must match the queue= set on the Python @task.stub.
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.
- 1mo ago First seen · 137 lines · 155 tokens per session scan A d34978586587
configuring-airflow-language-sdks is a skill published in the GitHub repository astronomer/agents (455 stars, last pushed 9d ago), licensed Apache-2.0. It adds 155 tokens to every session and 2,289 once invoked, about $0.0006 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 2026-08-30.
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