ML Engineer Agent — full ML lifecycle guidance
A senior ML-engineer persona for agents covering the complete model lifecycle — pipeline development, training, validation, deployment, monitoring, drift detection, retraining and versioning with MLflow/Kubeflow/TensorFlow/scikit-learn
- What
- A senior ML-engineer persona for agents covering the complete model lifecycle — pipeline development, training, validation, deployment, monitoring, drift detection, retraining and versioning with MLflow/Kubeflow/TensorFlow/scikit-learn
- Cost
- Free
- Needs
- an ML project you want lifecycle guidance on; your own compute and data; the MLOps tools you already use (MLflow, Kubeflow, or similar)
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @tony363's agent-ml-engineer skill, listed here with credit to its creator: a senior ML-engineer persona for agents covering the complete machine-learning lifecycle — pipeline development, model training, validation, deployment, monitoring and optimization, with emphasis on building scalable, reliable production ML systems: hitting accuracy targets, training under 4 hours, sub-50ms inference latency, automatic drift detection, automated retraining, and systematic versioning, using MLflow, Kubeflow, TensorFlow, scikit-learn and Optuna. Honest caveats: the skill body is thin — it's a persona and checklist rather than a how-to (pair it with the deeper @seb1n ml-pipeline-creation, model-training and model-deployment skills for the actual workflows); the metrics it lists (accuracy targets, <4h training, <50ms latency) are aspirations, not guarantees — they depend on your data, model and hardware; it's a different build from the identically-themed `rightnow-ai-openfang-ml-engineer` already live on Skill Harbor — compare before choosing. MIT licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: an ML project you want lifecycle guidance on; your own compute and data; the MLOps tools you already use (MLflow, Kubeflow, or similar) Install "ML Engineer Agent — full ML lifecycle guidance" for me. It gives my agent @tony363's senior ML-engineer persona: full-lifecycle guidance — pipeline development, training, validation, deployment, monitoring, drift detection, automated retraining and versioning — with MLflow, Kubeflow, TensorFlow, scikit-learn and Optuna. Pair it with the deeper @seb1n workflow skills for the actual how-tos. MIT licensed. Repository: https://github.com/tony363/superclaude/blob/main/.claude/skills/agent-ml-engineer/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This is a persona/checklist skill — any executable side beyond normal examples is a red flag. This repo should contain zero secrets in code. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "agent-ml-engineer". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. point the agent at my ML project, wire it to my existing MLOps stack, and treat the listed metrics as aspirations — they depend on my data, model and hardware). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.