ML Pipeline Expert
Jeffallan/claude-skills
Designs ML pipeline infrastructure: experiment tracking with MLflow or Weights & Biases, Kubeflow and Airflow orchestration, Feast feature stores and model validation gates.
Expert in Machine Learning Operations bridging data science and DevOps.
Install Mlops Engineer
$ npx skills add majiayu000/claude-skill-registry --skill mlops-engineer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install majiayu000/claude-skill-registry mlops-engineer --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/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-ml/mlops-engineer-skill .claude/skills/mlops-engineer && 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 "mlops-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/mlops-engineer-skill into .claude/skills/mlops-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlops-engineer", 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/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/mlops-engineer-skillType 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 majiayu000/claude-skill-registry --skill mlops-engineer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install majiayu000/claude-skill-registry mlops-engineer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-ml/mlops-engineer-skill .agents/skills/mlops-engineer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mlops-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/mlops-engineer-skill into .agents/skills/mlops-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlops-engineer", 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 majiayu000/claude-skill-registry --skill mlops-engineer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install majiayu000/claude-skill-registry mlops-engineer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-ml/mlops-engineer-skill .cursor/skills/mlops-engineer && 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 "mlops-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/mlops-engineer-skill into .cursor/skills/mlops-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlops-engineer", 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/majiayu000/claude-skill-registry.git --path skills/ai-ml/mlops-engineer-skill--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 majiayu000/claude-skill-registry --skill mlops-engineer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install majiayu000/claude-skill-registry mlops-engineer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-ml/mlops-engineer-skill .gemini/skills/mlops-engineer && 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 "mlops-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/mlops-engineer-skill into .gemini/skills/mlops-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlops-engineer", 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 majiayu000/claude-skill-registry mlops-engineerInstalls 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 majiayu000/claude-skill-registry --skill mlops-engineer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-ml/mlops-engineer-skill .github/skills/mlops-engineer && 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 "mlops-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/mlops-engineer-skill into .github/skills/mlops-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlops-engineer", 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 majiayu000/claude-skill-registry --skill mlops-engineer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install majiayu000/claude-skill-registry mlops-engineer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-ml/mlops-engineer-skill .opencode/skills/mlops-engineer && 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 "mlops-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/mlops-engineer-skill into .opencode/skills/mlops-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlops-engineer", 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
mlops-engineer- GitHub stars
- 666
- Used in
- 1 other repo
- Token cost
- ~820 tokens
- SKILL.md length
- 310 words
- Files
- 2
- Skills in repo
- 971
- Repo updated
- First seen
- Licence
- MIT
At a glance
Expert in Machine Learning Operations bridging data science and DevOps.
- Works in 3 steps: ML Pipeline Setup → Model Deployment → Model Monitoring
- Building ML pipelines
- SKILL.md covers Purpose, When to Use, Quick Start and Decision Framework, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Mlops Engineer is an agent skill from majiayu000/claude-skill-registry. Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving. Triggers include "MLOps", "ML pipeline", "model deployment", "feature store", "model versioning", "ML monitoring", "Kubeflow", "MLflow".
Its SKILL.md is about 820 tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).
It sits in DevOps & Cloud, covering MLOps. It works with MLflow. The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is MIT.
When your agent uses it
- Building ML pipelines
- Model versioning
- Production ML serving
- Model deployment
Example prompts
- “ML pipeline”
- “model deployment”
- “feature store”
- “/mlops-engineer”
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 000116a. 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
No scripts in the folder and no shell commands in SKILL.md.
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
Mlops Engineer loads about 820 tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 310 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from majiayu000/claude-skill-registry at commit 000116a, republished under its MIT licence (© majiayu000). 310 words, ~820 tokens.
Download SKILL.mdSave it as
.claude/skills/mlops-engineer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- mlops-engineer
- description
- Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving. Triggers include "MLOps", "ML pipeline", "model deployment", "feature store", "model versioning", "ML monitoring", "Kubeflow", "MLflow".
MLOps Engineer
Purpose
Provides expertise in Machine Learning Operations, bridging data science and DevOps practices. Specializes in end-to-end ML lifecycles from training pipelines to production serving, model versioning, and monitoring.
When to Use
- Building ML training and serving pipelines
- Implementing model versioning and registry
- Setting up feature stores
- Deploying models to production
- Monitoring model performance and drift
- Automating ML workflows (CI/CD for ML)
- Implementing A/B testing for models
- Managing experiment tracking
Quick Start
Invoke this skill when:
- Building ML pipelines and workflows
- Deploying models to production
- Setting up model versioning and registry
- Implementing feature stores
- Monitoring production ML systems
Do NOT invoke when:
- Model development and training → use
/ml-engineer - Data pipeline ETL → use
/data-engineer - Kubernetes infrastructure → use
/kubernetes-specialist - General CI/CD without ML → use
/devops-engineer
Decision Framework
ML Lifecycle Stage?
├── Experimentation
│ └── MLflow/Weights & Biases for tracking
├── Training Pipeline
│ └── Kubeflow/Airflow/Vertex AI
├── Model Registry
│ └── MLflow Registry/Vertex Model Registry
├── Serving
│ ├── Batch → Spark/Dataflow
│ └── Real-time → TF Serving/Seldon/KServe
└── Monitoring
└── Evidently/Fiddler/custom metricsCore Workflows
1. ML Pipeline Setup
- Define pipeline stages (data prep, training, eval)
- Choose orchestrator (Kubeflow, Airflow, Vertex)
- Containerize each pipeline step
- Implement artifact storage
- Add experiment tracking
- Configure automated retraining triggers
2. Model Deployment
- Register model in model registry
- Build serving container
- Deploy to serving infrastructure
- Configure autoscaling
- Implement canary/shadow deployment
- Set up monitoring and alerts
3. Model Monitoring
- Define key metrics (latency, throughput, accuracy)
- Implement data drift detection
- Set up prediction monitoring
- Create alerting thresholds
- Build dashboards for visibility
- Automate retraining triggers
Best Practices
- Version everything: code, data, models, configs
- Use feature stores for consistency between training and serving
- Implement CI/CD specifically designed for ML workflows
- Monitor data drift and model performance continuously
- Use canary deployments for model rollouts
- Keep training and serving environments consistent
Anti-Patterns
| Anti-Pattern | Problem | Correct Approach |
|---|---|---|
| Manual deployments | Error-prone, slow | Automated ML CI/CD |
| Training-serving skew | Prediction errors | Feature stores |
| No model versioning | Can't reproduce or rollback | Model registry |
| Ignoring data drift | Silent degradation | Continuous monitoring |
| Notebook-to-production | Unmaintainable | Proper pipeline code |
© majiayu000, 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 1 other file in skills/ai-ml/mlops-engineer-skill of majiayu000/claude-skill-registry.
- SKILL.md
- metadata.json
Open the folder on GitHubat commit 000116a
Used in 1 other repository
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Mlops Engineer 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 |
|---|---|---|---|---|---|---|
| Mlops Engineer this skillmajiayu000/claude-skill-registry | 666 | 1 repos | ~820 | Automated safety check: Pass | MIT | |
| ML Pipeline ExpertJeffallan/claude-skills | 12k | 1 repos | ~1.9k | Automated safety check: Pass | MIT | |
| 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 | |
| Sync ML Reportsprobabl-ai/skills | 135 | — | ~1.6k | Automated safety check: Pass | BSD-3-Clause |
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Questions about Mlops Engineer
What does Mlops Engineer do?
Expert in Machine Learning Operations bridging data science and DevOps. Mlops Engineer is an agent skill from majiayu000/claude-skill-registry. Expert in Machine Learning Operations bridging data science and DevOps.
When should I use Mlops Engineer?
Mlops Engineer fits situations like: building ML pipelines; model versioning; production ML serving; model deployment.
How do I install Mlops Engineer in Claude Code?
Run `npx skills add majiayu000/claude-skill-registry --skill mlops-engineer -a claude-code`. Or copy the skill folder (skills/ai-ml/mlops-engineer-skill in majiayu000/claude-skill-registry) into .claude/skills/mlops-engineer in your project. Claude Code loads it when a task matches its description.
How do I install Mlops Engineer in Codex?
Run `npx skills add majiayu000/claude-skill-registry --skill mlops-engineer -a codex`. Or copy the skill folder (skills/ai-ml/mlops-engineer-skill in majiayu000/claude-skill-registry) into .agents/skills/mlops-engineer in your project. Codex loads it when a task matches its description.
Can I use Mlops Engineer 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 majiayu000/claude-skill-registry --skill mlops-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mlops-engineer, .gemini/skills/mlops-engineer, .github/skills/mlops-engineer and .opencode/skills/mlops-engineer in your project.
What does Mlops Engineer need to run?
SKILL.md names no scripts, command-line tools or credentials: Mlops Engineer is instructions for the agent only.
Does Mlops Engineer 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 Mlops Engineer 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. Review the folder before installing.
What licence does Mlops Engineer use?
Mlops Engineer 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 Mlops Engineer use?
About 820 tokens (SKILL.md is roughly 3.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Mlops Engineer?
Skills that share tags, products or a category with Mlops Engineer: ML Pipeline Expert (Jeffallan/claude-skills, 12k 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 Mlops Engineer?
majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 971 skills in this directory. The repository was last updated on October 7, 2026.
Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.