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 agentmods add instructions/fmind/mlops-python-package/agents-mdgit clone --depth 1 https://github.com/fmind/mlops-python-packageWhat 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 | $0.01248 | $0.01248 |
| Opus 5 | $0.00624 | $0.00624 |
| Sonnet 5 | $0.00250 | $0.00250 |
| Haiku 4.5 | $0.00125 | $0.00125 |
Grade A, and why
mlops-python-package 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 yesterday.
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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Context and rules for AI agents working in this repository. Humans should start with README.md.
Project overview
- Name: bikes — a production-shaped MLOps package that predicts the number of bikes available.
- Description: reference implementation for the MLOps Coding Course, generated from cookiecutter-mlops-package.
- Language: Python 3.14+ (
pyproject.toml), managed withuv. - Stack: MLflow (tracking, registry, projects, evaluation), scikit-learn, pandas, Pydantic + Pandera validation, OmegaConf YAML configs, loguru.
- Skills: the reusable practices behind this package are published as Agent Skills in mlops-coding-skills. Install them from there rather than vendoring a copy here — a copy drifts, and this repository already lost a month to proving it.
Setup & core commands
All work goes through mise (see mise.toml); git hooks (lefthook.yml) and CI call the same tasks.
- Everything:
mise run all— format, check, test, build. This is the gate; CI runs this exact task and nothing else. - Install:
mise run install— sync the virtualenv (uv sync) and install git hooks. - Format:
mise run format—ruff(import sort + format, including Python inside Markdown) anddprint(JSON/Markdown/TOML/YAML). - Check:
mise run check—rufflint,tytypes,pip-auditdeps,dprint/validate-pyproject/uv lockformat,gitleakssecrets,trivyfilesystem scan,hadolintDockerfile,actionlint+zizmorworkflows. - Test:
mise run test—pytestwith coverage (fails under 100%);mise run test:parallelis a faster, coverage-free local loop. - Build:
mise run build—uv build(wheel + sdist);mise run build:imagebuilds the Docker image. - Docs:
mise run docs—pdocAPI reference intodocs/. - MLflow jobs:
mise run projectruns every job;mise run project:run <name>runs one (confs/<name>.yaml).
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.
- yesterday First seen · 46 lines · 1,248 tokens per session scan A 3ee6e58ede2b
mlops-python-package AGENTS.md is an instructions file published in the GitHub repository fmind/mlops-python-package (1,416 stars, last pushed 8d ago), licensed MIT. It adds 1,248 tokens to every session, about $0.0062 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.
Other instructions, from other repositories
marimo AGENTS.md
Instructions for marimo-team/marimo, covering marimo development guidelines, your primary responsibility is to the project and its users, quick setup, development commands and python.
dagster CLAUDE.md
Instructions for dagster-io/dagster, covering dagster development guide, quick references, environment setup, essential commands and code quality - run after every python edit.
FLAML copilot-instructions.md
Instructions for microsoft/FLAML, covering github copilot instructions for flaml, project overview, build and test commands, installation and basic installation.
zenml CLAUDE.md
Instructions for zenml-io/zenml, covering zenml claude code guidelines, project structure, use zenml docs via mcp, code style & quality standards and commenting policy — explain why, not what.
marimo copilot-instructions.md
Instructions for marimo-team/marimo: For example, if there is a frontend change to make a border thicker, how does this look in run mode, app-view and edit-view? For a backend change, has the user tested different scenarios?
zenml AGENTS.md
Instructions for zenml-io/zenml, covering zenml codex agent guidelines, project structure, always-loaded rules, common commands and branches, git, and prs.