AWS AI ML
aws/agent-toolkit-for-aws
Selects, deploys, and customizes AI models on Amazon SageMaker.
Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade open-source MLflow 3 setup with…
Install Mlflow Mlops Migration
$ npx skills add pproenca/dot-skills --skill mlflow-mlops-migration -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install pproenca/dot-skills mlflow-mlops-migration --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.experimental/mlflow-mlops-migration .claude/skills/mlflow-mlops-migration && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "mlflow-mlops-migration" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/mlflow-mlops-migration into .claude/skills/mlflow-mlops-migration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlflow-mlops-migration", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/mlflow-mlops-migrationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add pproenca/dot-skills --skill mlflow-mlops-migration -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install pproenca/dot-skills mlflow-mlops-migration --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/.experimental/mlflow-mlops-migration .agents/skills/mlflow-mlops-migration && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mlflow-mlops-migration" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/mlflow-mlops-migration into .agents/skills/mlflow-mlops-migration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlflow-mlops-migration", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add pproenca/dot-skills --skill mlflow-mlops-migration -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install pproenca/dot-skills mlflow-mlops-migration --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/.experimental/mlflow-mlops-migration .cursor/skills/mlflow-mlops-migration && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mlflow-mlops-migration" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/mlflow-mlops-migration into .cursor/skills/mlflow-mlops-migration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlflow-mlops-migration", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/pproenca/dot-skills.git --path skills/.experimental/mlflow-mlops-migration--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add pproenca/dot-skills --skill mlflow-mlops-migration -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install pproenca/dot-skills mlflow-mlops-migration --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/.experimental/mlflow-mlops-migration .gemini/skills/mlflow-mlops-migration && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "mlflow-mlops-migration" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/mlflow-mlops-migration into .gemini/skills/mlflow-mlops-migration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlflow-mlops-migration", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install pproenca/dot-skills mlflow-mlops-migrationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add pproenca/dot-skills --skill mlflow-mlops-migration -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/.experimental/mlflow-mlops-migration .github/skills/mlflow-mlops-migration && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "mlflow-mlops-migration" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/mlflow-mlops-migration into .github/skills/mlflow-mlops-migration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlflow-mlops-migration", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add pproenca/dot-skills --skill mlflow-mlops-migration -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install pproenca/dot-skills mlflow-mlops-migration --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/.experimental/mlflow-mlops-migration .opencode/skills/mlflow-mlops-migration && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mlflow-mlops-migration" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/mlflow-mlops-migration into .opencode/skills/mlflow-mlops-migration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlflow-mlops-migration", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
mlflow-mlops-migration- GitHub stars
- 214
- Token cost
- ~1.9k tokens
- SKILL.md length
- 722 words
- Files
- 14 (incl. scripts, references)
- Skills in repo
- 182
- Repo updated
- First seen
- Licence
- MIT
At a glance
Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade open-source MLflow 3 setup with…
- Asked to set up MLflow
- SKILL.md covers When to Apply, Workflow Overview, Risk Level: Write and Requirements, plus 4 more sections
- Runs Shell scripts from its folder
- Migrate to MLflow 3
What it does
Mlflow Mlops Migration is an agent skill from pproenca/dot-skills. Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade open-source MLflow 3 setup with dev/staging/prod environments, registry-based promotion, and served models. Walks seven phases with a developer who may have zero MLflow 3 experience — assess the codebase (scripted read-only audit), model the registry domain (per-environment model names, aliases, gates), stand up tracking per environment, restructure training…
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including scripts and reference files (for example `config.json`, `gotchas.md` and `hooks/hooks.json`).
It sits in DevOps & Cloud, covering MLOps, Meeting notes and agendas and QA and bug reports. It works with MLflow. The repository describes itself as: A collection of AI agent skills following the Agent Skills open format. The licence is MIT.
When your agent uses it
- Asked to set up MLflow
- Migrate to MLflow 3
- Productionize model training and serving
- Design a dev/staging/prod MLOps cycle
Example prompts
- “/mlflow-mlops-migration”
Requirements
- Python 3
- A Bash shell
- Docker
What it can do on your machine
Read from SKILL.md and the folder at commit cf93c57. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Shell), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Mlflow Mlops Migration loads about 1.9k tokens when it runs, and up to ~8.5k if it reads all its reference files. Until then it costs about 214 tokens; SKILL.md has 722 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from pproenca/dot-skills at commit cf93c57, republished under its MIT licence (© pproenca). 722 words, ~1,900 tokens.
Download SKILL.mdSave it as
.claude/skills/mlflow-mlops-migration/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.- name
- mlflow-mlops-migration
- description
- Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade open-source MLflow 3 setup with dev/staging/prod environments, registry-based promotion, and served models. Walks seven phases with a developer who may have zero MLflow 3 experience — assess the codebase (scripted read-only audit), model the registry domain (per-environment model names, aliases, gates), stand up tracking per environment, restructure training code to MLflow 3 idioms, wire evaluation-gated promotion, serve and smoke-test, then run the ongoing MLOps loop. Use when asked to set up MLflow, migrate to MLflow 3, productionize model training and serving, or design a dev/staging/prod MLOps cycle. Pairs with the sibling mlflow-3 rule pack for every API decision.
MLflow MLOps Migration
A phased, gated workflow that turns an arbitrary ML codebase — however unstructured — into a
production-grade open-source MLflow 3 setup covering the full MLOps cycle: tracked experiments,
a domain-modelled registry, dev/staging/prod separation, evaluation-gated promotion, and served
models. It is written to be driven with a developer who has no MLflow 3 experience: every phase
produces a reviewable artifact before anything is changed, and every API decision defers to the
sibling mlflow-3 rule pack (which is pinned to mlflow 3.15.1 and names the
MLflow 2-era idioms this migration exists to remove).
When to Apply
Use this skill when:
- A team wants MLflow (or has a messy/partial MLflow 2 setup) and needs the path to a production-grade MLflow 3 deployment — not just API fixes.
- Training code exists but experiments are untracked, models are shipped by copying files, or "deployment" means a pickle in a bucket.
- You are asked to design or review a dev/staging/prod model-promotion story.
- An MLflow 2 → 3 migration touches infrastructure (stages,
./mlrunsfile stores, MLServer), not only client code.
Don't use it for a single API question — read the relevant mlflow-3 rule directly.
Workflow Overview
0 assess ─▶ 1 domain-model ─▶ 2 environments ─▶ 3 instrument ─▶ 4 promote ─▶ 5 serve ─▶ 6 operate
audit registry tracking per training code eval-gated validate, retrain loop,
report naming, alias env (dev local, → MLflow 3 copy_model_ serve, challenger,
(script, + gate design stg/prod DB+S3 idioms (rule version + smoke-test maintenance
read-only) (interview) + auth) pack) alias flip /invocations (gated)| Phase | Action | Deliverable | Risk |
|---|---|---|---|
| 0 | Run scripts/00-assess.sh <codebase> — read-only audit | mlflow-assessment.md report | read-only |
| 1 | Interview + domain modelling | Registry domain doc (names, aliases, gates) | read-only |
| 2 | Stand up tracking per environments; dev via scripts/scaffold-dev-tracking.sh | Reachable tracking server(s), config.json filled | write |
| 3 | Restructure training code to MLflow 3 idioms (sibling rule pack) | Refactored code, first LoggedModels registered | write |
| 4 | Wire promotion — evaluate gate, tags, copy_model_version, alias flip | Promotion script/CI job | write |
| 5 | Serve — mlflow.models.predict, then serve/build-docker, smoke /invocations | Served model per environment | write |
| 6 | Operate — retraining, challenger evaluation, maintenance (see workflow) | Runbook habits, scheduled jobs | write |
| ✓ | Run scripts/verify.sh after phases 2–5 | Pass/fail assertion report | read-only |
Phases run in order — each has entry/exit criteria in references/workflow.md,
and scripts/verify.sh is the exit gate for the infrastructure phases. Re-running any phase is safe:
00-assess.sh regenerates only its own report (and refuses to clobber anything else),
scaffold-dev-tracking.sh refuses to overwrite (exit code 2 = already done), and verify.sh only
reads. The one non-idempotent step is promotion's copy_model_version — see
references/promotion.md for how to resume instead of re-copying.
Risk Level: Write
This workflow edits training code, writes infrastructure files, and stands up services. Guardrails:
- Nothing in phase 0–1 modifies anything — always complete both before touching code or infra.
- Confirm with the user before: starting/replacing any tracking server, rewriting a training
entrypoint, flipping a prod
@championalias (dev/staging flips may be automated by the phase-4 pipeline), and exposing a serving endpoint beyond localhost. - Two maintenance commands are destructive and must be run only with explicit user confirmation and
a stated reason:
mlflow gc(permanently deletes soft-deleted runs and experiments — registry entities are untouched) andmlflow db upgrade(irreversible schema migration — snapshot the database first). A PreToolUse hook in hooks/hooks.json blocks both unlessMLFLOW_MAINTENANCE_ACK=yesis set for that command, so they cannot run un-confirmed by accident.
Show full SKILL.md (239 more words)Show less
Requirements
- Python ≥ 3.10 with
mlflow==3.15.1installed in the project environment - bash, curl, jq — the scripts use them
- uv — the serving phase uses
--env-manager uvfor fast isolated environment rebuilds (substitutevirtualenveverywhere if uv is unavailable) - Docker + docker-compose — for the dev tracking stack and
build-dockerserving images - A database + object store per shared environment (staging/prod) — PostgreSQL/MySQL and S3/GCS/Azure; dev runs on the scaffolded local stack
- The sibling
mlflow-3skill — phase 3 cites its rules; if it is not installed, read the MLflow 3 migration guide instead (the workflow still works, with more manual verification)
Setup
config.json starts empty. Phase 2 fills it (tracking URIs per environment, registry namespace,
model name, serving URL). If fields are empty when a script needs them, the script says which ones —
fill them via the _setup_instructions in the file.
Quick Reference
| I need to… | Go to |
|---|---|
| Audit what the codebase does today | scripts/00-assess.sh <dir> + references/assessment.md |
| Decide model names / aliases / gates | references/domain-modelling.md |
| Stand up dev tracking in one command | scripts/scaffold-dev-tracking.sh <dir> |
| Design staging/prod tracking topology | references/environments.md |
Rewrite log_model / stages / evaluate calls | sibling mlflow-3 rules (log-*, reg-*, eval-*) |
| Build the promotion pipeline | references/promotion.md |
| Serve and smoke-test a model | references/serving.md |
| Check the setup actually works | scripts/verify.sh |
| See every phase's entry/exit criteria | references/workflow.md |
Gotchas
See gotchas.md — failure points discovered while running this workflow, including the
migrate-filestore SQLite-only target and the basic-auth bootstrap credentials.
Related Skills
mlflow-3— the sibling library-reference rule pack this workflow cites at every API decision
© pproenca, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 13 other files (scripts, references) in skills/.experimental/mlflow-mlops-migration of pproenca/dot-skills.
- SKILL.md
- config.json
- gotchas.md
- hooks/hooks.json
- metadata.json
- references/assessment.md
- references/domain-modelling.md
- references/environments.md
- references/promotion.md
- references/serving.md
- references/workflow.md
- scripts/00-assess.sh
- scripts/scaffold-dev-tracking.sh
- scripts/verify.sh
Open the folder on GitHubat commit cf93c57
Compare with similar skills
Mlflow Mlops Migration next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Mlflow Mlops Migration this skillpproenca/dot-skills | 214 | — | ~1.9k | Automated safety check: Pass | MIT | |
| AWS AI MLaws/agent-toolkit-for-aws | 2.8k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Senior ML Engineeralirezarezvani/claude-skills | 28k | 2 repos | ~2.4k | Automated safety check: Pass | MIT | |
| Implementing Mlopsancoleman/ai-design-components | 526 | 1 repos | ~9.2k | Automated safety check: Pass | MIT | |
| Mlops Engineeraiskillstore/marketplace | 430 | 7 repos | ~2.8k | Automated safety check: Pass | None | |
| ML Pipeline Automationsecondsky/claude-skills | 227 | 1 repos | ~3.2k | Automated safety check: Pass | MIT |
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Questions about Mlflow Mlops Migration
What does Mlflow Mlops Migration do?
Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade open-source MLflow 3 setup with…. Mlflow Mlops Migration is an agent skill from pproenca/dot-skills. Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade open-source MLflow 3 setup with dev/staging/prod environments, registry-based promotion, and served models.
When should I use Mlflow Mlops Migration?
Mlflow Mlops Migration fits situations like: asked to set up MLflow; migrate to MLflow 3; productionize model training and serving; design a dev/staging/prod MLOps cycle.
How do I install Mlflow Mlops Migration in Claude Code?
Run `npx skills add pproenca/dot-skills --skill mlflow-mlops-migration -a claude-code`. Or copy the skill folder (skills/.experimental/mlflow-mlops-migration in pproenca/dot-skills) into .claude/skills/mlflow-mlops-migration in your project. Claude Code loads it when a task matches its description.
How do I install Mlflow Mlops Migration in Codex?
Run `npx skills add pproenca/dot-skills --skill mlflow-mlops-migration -a codex`. Or copy the skill folder (skills/.experimental/mlflow-mlops-migration in pproenca/dot-skills) into .agents/skills/mlflow-mlops-migration in your project. Codex loads it when a task matches its description.
Can I use Mlflow Mlops Migration in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add pproenca/dot-skills --skill mlflow-mlops-migration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mlflow-mlops-migration, .gemini/skills/mlflow-mlops-migration, .github/skills/mlflow-mlops-migration and .opencode/skills/mlflow-mlops-migration in your project.
What does Mlflow Mlops Migration need to run?
Going by SKILL.md and its folder, Mlflow Mlops Migration needs a shell for the scripts in its folder. Our summary lists: Python 3; A Bash shell; Docker.
Does Mlflow Mlops Migration access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Mlflow Mlops Migration safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Mlflow Mlops Migration use?
Mlflow Mlops Migration is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Mlflow Mlops Migration use?
About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.6k tokens, read only when the agent opens those files.
What are the alternatives to Mlflow Mlops Migration?
Skills that share tags, products or a category with Mlflow Mlops Migration: AWS AI ML (aws/agent-toolkit-for-aws, 2.8k stars), Senior ML Engineer (alirezarezvani/claude-skills, 28k stars), Implementing Mlops (ancoleman/ai-design-components, 526 stars) and Mlops Engineer (aiskillstore/marketplace, 430 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mlflow Mlops Migration?
pproenca (a GitHub user) maintains it in pproenca/dot-skills, which has 214 GitHub stars. The repository holds 182 skills in this directory. The repository was last updated on August 15, 2026.
Source: pproenca/dot-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.