MLOps Python Package is a Python project template for organizing machine-learning operations and data-pipeline code. Data scientists and engineers use it as a starting point for workflows such as experiment tracking, model registries, and real-time inference.
Borrowing it
Nothing to install: this file belongs to fmind/mlops-python-package. 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/fmind/mlops-python-package/main/AGENTS.mdgit clone --depth 1 https://github.com/fmind/mlops-python-packageWrote 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/fmind/mlops-python-package/agents-md)<a href="https://agentmods.dev/instructions/fmind/mlops-python-package/agents-md"><img src="https://agentmods.dev/badge/instructions/fmind/mlops-python-package/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/fmind/mlops-python-package/agents-md"><img src="https://agentmods.dev/badge/instructions/fmind/mlops-python-package/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 | $0.01423 | $0.01423 |
| Opus 5.5 | $0.00569 | $0.00569 |
| Sonnet 5.5 | $0.00285 | $0.00285 |
| Haiku 4.5 | $0.00142 | $0.00142 |
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 7d 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 — 52 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 hourly bike rental demand.
- 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, plus a clean-tree check. - Install:
mise run install— sync the virtualenv (uv sync --all-groups) 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 the connectedPipelineJob(tune → train → evaluate → promote → infer → explain);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.
- 7d ago Changed · -3 tokens per session 49a99c41c2b8
- 25d ago Changed · +5 lines · +103 tokens per session 553e92029268
- 1mo ago Changed · +1 lines · +75 tokens per session 8dbf6c1d0617
- 2mo ago 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 (1,421 stars, last pushed yesterday), licensed MIT. It adds 1,423 tokens to every session, about $0.0057 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.
mlops-python-package AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| mlops-python-package AGENTS.mdfmind/mlops-python-package | Instructions file | 1.4k | yesterday | MIT | A | 1,423 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |