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 dag-factorygit 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/dag-factory)<a href="https://agentmods.dev/skills/astronomer/agents/dag-factory"><img src="https://agentmods.dev/badge/skills/astronomer/agents/dag-factory.svg" alt="Measured on agentmods" 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 | $0.00080 | $0.04533 |
| Opus 5 | $0.00040 | $0.02266 |
| Sonnet 5 | $0.00016 | $0.00907 |
| Haiku 4.5 | $0.00008 | $0.00453 |
Grade A, and why
dag-factory 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 8d 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 — 498 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DAG Factory
You are helping a user build Apache Airflow DAGs declaratively with dag-factory, a library that turns YAML configuration files into Airflow DAGs. Execute steps in order and prefer the simplest configuration that meets the user's needs.
Package:
dag-factoryon PyPI Repo: https://github.com/astronomer/dag-factory Docs: https://astronomer.github.io/dag-factory/latest/ Targets: dag-factory v1.0+ only. For pre-1.0 projects, see reference/migration.md before applying any guidance from this skill. Requires: Python 3.10+, Airflow 2.4+ (Airflow 3 supported)
Before Starting
Confirm with the user:
- Airflow version ≥2.4
- Python version ≥3.10
- dag-factory version: this skill targets v1.0+. If the project is on <1.0, follow reference/migration.md to upgrade before continuing.
- Use case: dag-factory is for declarative, low-code DAG authoring. If the user needs reusable, validated Pythonic templates with Pydantic, suggest blueprint instead. If they need full Python flexibility, suggest the authoring-dags skill.
Determine What the User Needs
| User Request | Action |
|---|---|
| "Create a YAML DAG" / "Convert this Python DAG to YAML" | Go to Defining a DAG in YAML |
| "Set up dag-factory in my project" | Go to Project Setup |
| "Share defaults across DAGs" / "Set start_date once" | Go to Defaults |
| "Use a custom operator" / "Use KPO / Slack / Snowflake" | Go to Custom & Provider Operators |
| "Dynamic / mapped tasks" / "expand / partial" | Go to Dynamic Task Mapping |
| "Schedule on dataset" / "Outlets and inlets" | Go to Datasets |
| "Add a callback" / "Slack on failure" | Go to Callbacks |
| "Use a timetable" / "datetime in YAML" / "timedelta in YAML" | Go to Custom Python Objects (__type__) |
| "Lint my YAML" / "Validate" | Go to Validation Commands |
| "Convert Airflow 2 YAML to Airflow 3" | Go to Validation Commands (dagfactory convert) |
| "Migrate from dag-factory <1.0" | See reference/migration.md |
| dag-factory errors / troubleshooting | Go to Troubleshooting |
What ships with it
1 file 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.
- 8d ago First seen · 498 lines · 80 tokens per session scan A 201378268d0a
dag-factory is a skill published in the GitHub repository astronomer/agents (439 stars, last pushed 3d ago), licensed Apache-2.0. It adds 80 tokens to every session and 4,533 once invoked, about $0.0004 per session on Opus 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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