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 zenml-io/skills --skill pipeline-authoringgit clone --depth 1 https://github.com/zenml-io/skillsWrote 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/zenml-io/skills/pipeline-authoring)<a href="https://agentmods.dev/skills/zenml-io/skills/pipeline-authoring"><img src="https://agentmods.dev/badge/skills/zenml-io/skills/pipeline-authoring/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/zenml-io/skills/pipeline-authoring"><img src="https://agentmods.dev/badge/skills/zenml-io/skills/pipeline-authoring.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 | $0.00229 | $0.07940 |
| Opus 5 | $0.00114 | $0.03970 |
| Sonnet 5 | $0.00046 | $0.01588 |
| Haiku 4.5 | $0.00023 | $0.00794 |
Grade A, and why
zenml-pipeline-authoring 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 today.
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 — 685 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Author ZenML Pipelines
This skill guides pipeline authoring: steps, artifacts, configuration, Docker settings, materializers, metadata, secrets, and visualizations.
Start Here: Interview the User
Do not rush to code. Before writing a single line, thoroughly understand what the user wants to build. The interview is the most important step — a well-scoped pipeline that does 3 things well beats a sprawling one that does 10 things poorly.
For complex or multi-pipeline projects: If the user describes something ambitious (e.g., "build me an end-to-end ML platform with data ingestion, feature engineering, training, evaluation, deployment, monitoring, and retraining"), or if they mention multiple pipelines, invoke the zenml-scoping skill first. It runs a deeper architectural interview that decomposes the system into pipeline units, identifies what doesn't belong in a pipeline at all, and produces a pipeline_architecture.md spec. Once that's done, come back here to build each pipeline one at a time.
For single, focused pipelines: If the user's request is clearly one pipeline (e.g., "build a training pipeline for my CSV data"), proceed with the questions below. If the answers are obvious from context, infer them and proceed. Only ask when genuinely ambiguous.
Q1: Static or dynamic pipeline?
Most pipelines are static (fixed DAG). Use dynamic (@pipeline(dynamic=True)) only when the number of steps or their wiring depends on runtime values (e.g., "process N documents where N comes from a query"). See Dynamic Pipelines and references/dynamic-pipelines.md.
Q2: Local or remote orchestrator? If remote (Kubernetes, Vertex AI, SageMaker, AzureML), the Artifact Golden Rule is critical, and you will need Docker Settings. If local-only for now, you can defer those concerns. Ask whether the user already has a stack set up — if not, point them to the ZenML docs for stack setup (this skill does not cover stack creation).
What ships with it
8 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.
- today Changed · +4 lines · +61 tokens per session 298716f27667
- 12d ago First seen · 681 lines · 168 tokens per session scan A b38db1a22c7d
zenml-pipeline-authoring is a skill published in the GitHub repository zenml-io/skills (6 stars, last pushed today), licensed MIT. It adds 229 tokens to every session and 7,940 once invoked, about $0.0011 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 2026-08-31.
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