A reference guide for open-source MLflow 3, a tool for recording machine-learning experiments, managing model versions, evaluating models, and serving them.

Use it when writing or reviewing MLflow 3 code for tracking metrics and datasets, registering models, promoting versions across environments, setting evaluation gates, or serving models.

It helps prevent using older MLflow 2 instructions with MLflow 3, whose model management, storage, registry, and serving approaches have changed.

mlflow-3 — Skill for Claude Code and Codex by pproenca

Install

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.

Any agent

npx skills add pproenca/dot-skills --skill mlflow-3

Clone the repo

git clone --depth 1 https://github.com/pproenca/dot-skills

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/pproenca/dot-skills/mlflow-3/github.svg)](https://agentmods.dev/skills/pproenca/dot-skills/mlflow-3)

Your own site

<a href="https://agentmods.dev/skills/pproenca/dot-skills/mlflow-3"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/mlflow-3/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/pproenca/dot-skills/mlflow-3"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/mlflow-3.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 177 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,158 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00177	$0.02158
Opus 5	$0.00088	$0.01079
Sonnet 5	$0.00035	$0.00432
Haiku 4.5	$0.00018	$0.00216

Measured 5d ago against content hash 9010b23623c3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

mlflow-3 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 5d 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.

skills/.experimental/mlflow-3/SKILL.md · 97 lines

How it starts

The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MLflow 3

Library-reference skill for open-source MLflow 3 — 24 rules across 6 categories. MLflow 3 restructured the library around the model as a first-class entity, deprecated the registry-stage vocabulary, replaced the serving stack, and changed storage and serialization defaults; a model trained on the vast MLflow 2 corpus reproduces the old idioms fluently, which is exactly why each of these rules exists. There is no rule for things a capable model already gets right.

Scope is classic-ML MLOps on self-hosted OSS MLflow. GenAI features (mlflow.genai, tracing, prompt registry, AI Gateway) appear only where confusing them with the classic APIs is itself the trap. Databricks/Unity-Catalog-only features (Deployment Jobs) are flagged as out of scope where a model might scaffold them against OSS.

Pinned to mlflow 3.15.1 (Python ≥ 3.10). API claims were verified against the unpacked mlflow / mlflow-skinny 3.15.1 wheels.

When to Apply

Writing or reviewing training code that logs models, metrics, params, or datasets with MLflow
Registering model versions and wiring promotion across dev/staging/prod (aliases, copy_model_version, tags, webhooks)
Standing up or hardening an mlflow server — backend store, artifact store, auth
Evaluating candidate models and gating promotion on thresholds
Serving models — mlflow models serve, build-docker, /invocations clients, pre-deploy validation
Migrating an MLflow 2-era codebase (stages, artifact_path, mlflow.evaluate, ./mlruns) to MLflow 3

Rule Categories

#	Category	Prefix	Covers
1	Model Logging & LoggedModel	`log-`	`name=` not `artifact_path`, models decoupled from runs, input-example-driven signatures, register-at-log-time, skops/torch.export serialization defaults, model-linked metrics and `search_logged_models`
2	Model Registry & Promotion	`reg-`	Aliases replacing stages, alias-based lookup, per-environment registered models with `copy_model_version`, gate state in tags, OSS webhooks vs Databricks-only Deployment Jobs
3	Tracking Backend & Server	`track-`	`sqlite:///mlflow.db` default, database-only server backends and `migrate-filestore`, proxied artifacts topology, telemetry opt-out, autolog input-example default
4	Serving	`serve-`	FastAPI scoring server (MLServer removed), `/invocations` payload contract, `mlflow.models.predict` pre-deploy validation, `build-docker` for clusters
5	Evaluation & Gates	`eval-`	`mlflow.models.evaluate` vs `mlflow.genai.evaluate`, threshold gating with `validate_evaluation_results` after `baseline_model`'s removal
6	Environment & Reproducibility	`env-`	Generated environment files as the serving source of truth, dependency pinning and uv capture, bundling custom code with `code_paths`

Read the full file on GitHub · 97 lines

Files

What ships with it

28 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.

Changes

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.

5d ago First seen · 97 lines · 177 tokens per session scan A 9010b23623c3

Subscribe to this mod's changes

mlflow-3 is a skill published in the GitHub repository pproenca/dot-skills (205 stars, last pushed 24d ago), licensed MIT. It adds 177 tokens to every session and 2,158 once invoked, about $0.0009 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 2026-09-03.

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