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/CLAUDE.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/claude-md)<a href="https://agentmods.dev/instructions/zenml-io/zenml/claude-md"><img src="https://agentmods.dev/badge/instructions/zenml-io/zenml/claude-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/claude-md"><img src="https://agentmods.dev/badge/instructions/zenml-io/zenml/claude-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 | $0.04696 | $0.04696 |
| Opus 5.5 | $0.01878 | $0.01878 |
| Sonnet 5.5 | $0.00939 | $0.00939 |
| Haiku 4.5 | $0.00470 | $0.00470 |
Grade A, and why
zenml CLAUDE.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 2mo 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 — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ZenML Claude Code Guidelines
This document provides guidance for Claude Code when working with the ZenML codebase. ZenML is an extensible, open-source MLOps framework for creating production-ready ML pipelines.
Project Structure
/src/zenml/- Core source code/tests/- Test suite (unit, integration)/docs/- Documentation/examples/- Example projects/scripts/- Development utilities
Use filesystem navigation tools to explore the codebase structure as needed.
Use ZenML Docs via MCP
Claude Code can query ZenML documentation via the built-in GitBook MCP server: https://docs.zenml.io/~gitbook/mcp. This enables real-time, source-of-truth lookups from the docs while you code, reducing hallucinations and speeding up feature discovery.
Quick setup (CLI):
claude mcp add zenmldocs --transport http https://docs.zenml.io/~gitbook/mcp
Note: The MCP server indexes the latest released docs, not the develop branch. For full setup details and editor alternatives, see docs/book/reference/llms-txt.md.
Code Style & Quality Standards
- Use US English spelling in all code, comments, docstrings, and documentation (e.g., "initialize", "stabilize", "color"). The CI enforces this via
typos(configured in.typos.toml).
Commenting policy — explain why, not what
- Use comments to document intent, trade‑offs, constraints, invariants, and tricky edge cases—i.e., why the code is this way—rather than narrating changes. Prefer self‑explanatory code; add comments only where extra context is needed. Write for a reader 6+ months later.
- Use for: complex logic/algorithms, non‑obvious design decisions, business rules/constraints, API purpose/contracts, edge cases.
- Avoid: change‑tracking comments ("Updated from previous version", "New implementation", "Changed to use X instead of Y", "Refactored this section").
- Avoid simple explanatory comments, where it is already clear from the code itself.
- Avoid useless one-line comments interleaved with code that merely narrate the implementation. Favor expressive names and small, focused functions.
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.
- 2mo ago First seen · 422 lines · 4,696 tokens per session scan A f78240ad4eaa
zenml CLAUDE.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 4,696 tokens to every session, about $0.0188 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.