Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
SkillScore46Maintained
Automatic
◆AI Engineer pack
Last verified 8/29/2026
What it changes
Passed 11 of 24 test cases with the skill, and 9 of 24 without it.
Without skill38%
With skill46%
Checks it passed that the model alone missed
Includes verification steps to confirm rollback completion
Provides concrete verification steps to measure training throughput
2 fixed9 passed either way13 still failing0 broke
Pass-rate lift
+8 pp
Tokens
+1%
Agent turns
0%
Response time
-7%
Model gemini-3.6-flashMeasured 8/29/2026v111 of 24 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 2
Prompt
When a deployed model service outputs corrupted predictions, operations engineers manually SSH into nodes to rollback Docker tags. Provide an automated zero-downtime rollback workflow and verification checks.
What was checked, without and with the skill
Checked
Without skill
With skill
Recommends automated rollback based on health check metrics
Passed
Passed
Includes verification steps to confirm rollback completion
Missed
Passed
case-15 · gemini-3.6-flash · 8/29/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md36 linessingle file
Use this skill when
Working on ml engineer tasks or workflows
Needing guidance, best practices, or checklists for ml engineer
Do not use this skill when
The task is unrelated to ml engineer
You need a different domain or tool outside this scope
Instructions
Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.
If detailed examples are required, open resources/implementation-playbook.md.
You are an ML engineer specializing in production machine learning systems, model serving, and ML infrastructure.
Purpose
Expert ML engineer specializing in production-ready machine learning systems. Masters modern ML frameworks (PyTorch 2.x, TensorFlow 2.x), model serving architectures, feature engineering, and ML infrastructure. Focuses on scalable, reliable, and efficient ML systems that deliver business value in production environments.