ZenML is a Python framework for turning machine-learning and AI workflows into tracked pipelines that run on different infrastructure backends. It is for ML and AI engineers building applications such as model-training workflows, language-model systems, and agent loops in company environments.
Borrowing it
Nothing to install: this file belongs to zenml-io/zenml. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zenml-io/zenml/main/AGENTS.mdgit clone --depth 1 https://github.com/zenml-io/zenmlWrote 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/instructions/zenml-io/zenml/agents-md)<a href="https://agentmods.dev/instructions/zenml-io/zenml/agents-md"><img src="https://agentmods.dev/badge/instructions/zenml-io/zenml/agents-md/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/instructions/zenml-io/zenml/agents-md"><img src="https://agentmods.dev/badge/instructions/zenml-io/zenml/agents-md.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 | $0.02047 | $0.02047 |
| Opus 5.5 | $0.00819 | $0.00819 |
| Sonnet 5.5 | $0.00409 | $0.00409 |
| Haiku 4.5 | $0.00205 | $0.00205 |
Grade A, and why
zenml AGENTS.md 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 14d 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ZenML Codex Agent Guidelines
ZenML is an extensible open-source MLOps framework for creating production-ready
ML pipelines. This root guide contains rules that must be loaded for every
Codex session in this repository. For detailed workflows, examples, and
subsystem recipes, use .agents/skills/zenml-repo-workflows/SKILL.md.
Project Structure
src/zenml/- Core source code.tests/- Unit and integration tests.docs/book/- Source documentation.examples/- Example projects.scripts/- Development utilities.
Task Scope and Completion
- For implementation requests, continue through the requested change, relevant checks, and final diff review. For audits and proposals, report findings without applying changes unless requested.
- Make routine choices from repository conventions. Ask when the answer changes behavior, compatibility, or authorization; continue independent authorized work while waiting. Do not ask again for actions already authorized.
- Current user instructions take precedence over skill guidelines. If a skill would block authorized work, identify the exact file and instruction and explain the conflict rather than silently stopping or expanding the task.
Always-Loaded Rules
- Use US English spelling in code, comments, docstrings, and documentation.
- Use Python 3.10+ compatible code.
- Type hint function parameters and return values.
- Follow Google Python style for docstrings. Include
Args,Returns,Yields, andRaisessections whenever the function contract requires them; do not use a summary-only docstring to omit applicable sections. - Prefer clear names and small functions over explanatory comments.
- Comments should explain intent, trade-offs, constraints, invariants, and tricky edge cases. Avoid comments that restate obvious code.
- Do not use multi-line banner comments to group classes or functions.
- Prefer typed contracts over
getattr/hasattrcapability checks when static typing can express the requirement. - Private methods and functions with a leading underscore should not be called outside their class or module.
- Integrations should avoid using ZenML private methods because externalized integrations will not be protected by in-repo type checks.
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.
- 14d ago Changed · +34 lines · +472 tokens per session 8f44ba714f14
- 2mo ago First seen · 149 lines · 1,575 tokens per session scan A 63f45f69cffe
zenml AGENTS.md is an instructions file published in the GitHub repository zenml-io/zenml (5,629 stars, last pushed yesterday), licensed Apache-2.0. It adds 2,047 tokens to every session, about $0.0082 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.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.