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Agent skill

ML Engineer

by RightNow-AI in RightNow-AI/openfang

Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps

Apache-2.0Auto-check passedData & Analytics

Install ML Engineer

skills CLI
$ npx skills add RightNow-AI/openfang --skill ml-engineer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install RightNow-AI/openfang ml-engineer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/RightNow-AI/openfang.git skills-src && mkdir -p .claude/skills && cp -r skills-src/crates/openfang-skills/bundled/ml-engineer .claude/skills/ml-engineer && rm -rf skills-src

Use ~/.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/

Facts

Skill name
ml-engineer
GitHub stars
18k
Token cost
~987 tokens
SKILL.md length
490 words
Files
1
Skills in repo
68
Repo updated
First seen
Licence
Apache-2.0

At a glance

Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps

  • Tasks that involve Machine learning
  • SKILL.md covers Key Principles, Techniques, Common Patterns and Pitfalls to Avoid
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Deep learning

What it does

ML Engineer is an agent skill from RightNow-AI/openfang. Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps

Its SKILL.md is about 990 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Machine learning, Deep learning and MLOps. It works with PyTorch and scikit-learn. The repository describes itself as: Open-source Agent Operating System. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Machine learning
  • Tasks that involve Deep learning
  • Tasks that involve MLOps

Example prompts

  • “/ml-engineer”

What it can do on your machine

Read from SKILL.md and the folder at commit acf2587. 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

ML Engineer loads about 987 tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 490 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~25
When it runs · the whole SKILL.md, loaded when a task matches
~987

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 RightNow-AI/openfang at commit acf2587, republished under its Apache-2.0 licence (© RightNow-AI). 490 words, ~987 tokens.

Download SKILL.mdSave it as .claude/skills/ml-engineer/SKILL.md (or your agent's skills folder).
name
ml-engineer
description
Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps

Machine Learning Engineer

A machine learning practitioner with deep expertise in model development, training infrastructure, evaluation methodology, and production deployment. This skill provides guidance for building ML systems end-to-end using PyTorch for deep learning, scikit-learn for classical ML, and MLOps practices that ensure models are reproducible, monitored, and maintainable in production environments.

Key Principles

  • Start with a strong baseline using simple models and solid feature engineering before reaching for complex architectures; a well-tuned logistic regression often outperforms a poorly configured neural network
  • Evaluate models with metrics that align with business objectives, not just accuracy; precision, recall, F1, and AUC-ROC each tell different stories about model behavior on imbalanced data
  • Version everything: datasets, code, hyperparameters, and model artifacts; reproducibility is the foundation of trustworthy ML systems
  • Design training pipelines to be idempotent and resumable; checkpointing, deterministic seeding, and configuration files enable reliable experimentation
  • Monitor models in production for data drift, prediction drift, and performance degradation; a model that was accurate at deployment time can silently degrade as input distributions shift

Techniques

  • Structure PyTorch training with a clear pattern: define nn.Module subclass, configure DataLoader with proper num_workers and pin_memory, implement the training loop with optimizer.zero_grad(), loss.backward(), and optimizer.step()
  • Build scikit-learn pipelines with Pipeline and ColumnTransformer to chain preprocessing (scaling, encoding, imputation) with model fitting, ensuring that all transformations are fit on training data only
  • Perform hyperparameter tuning with GridSearchCV or RandomizedSearchCV using cross-validation; for expensive models, use Optuna or Bayesian optimization to search efficiently
  • Compute evaluation metrics on held-out test sets: classification_report for precision/recall/F1 per class, roc_auc_score for ranking quality, and confusion_matrix for error analysis
  • Engineer features systematically: log transforms for skewed distributions, interaction terms for feature combinations, target encoding for high-cardinality categoricals, and temporal features for time-series data
  • Track experiments with MLflow or Weights and Biases: log hyperparameters, metrics, artifacts, and model versions for every run
Show full SKILL.md (185 more words)Show less

Common Patterns

  • Train-Validate-Test Split: Use stratified splitting (80/10/10) to maintain class distribution; never touch the test set during development, only for final evaluation
  • Learning Rate Schedule: Use warmup followed by cosine annealing or reduce-on-plateau for training stability; sudden large learning rates cause divergence in deep networks
  • Ensemble Methods: Combine predictions from diverse models (gradient boosting + neural network + linear model) to improve robustness and reduce variance
  • Model Registry: Promote models through stages (staging, production, archived) in MLflow Model Registry with approval gates and automated validation checks

Pitfalls to Avoid

  • Do not evaluate on the training set or leak test data into preprocessing; this produces overly optimistic metrics that do not reflect real-world performance
  • Do not train models without understanding the data: check for class imbalance, missing values, duplicates, and label noise before building any model
  • Do not deploy models without a rollback plan; maintain the previous model version in production so you can revert quickly if the new model underperforms
  • Do not treat feature engineering as a one-time task; as the domain evolves and new data sources become available, revisit and expand the feature set regularly

© RightNow-AI, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in crates/openfang-skills/bundled/ml-engineer of RightNow-AI/openfang.

Open the folder on GitHubat commit acf2587

Compare with similar skills

ML 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.

ML Engineer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
ML Engineer this skillRightNow-AI/openfang18k—~987Automated safety check: PassApache-2.0
Editomegaml/omegaml107—~206Automated safety check: PassApache-2.0
Databricks ML Trainingdatabricks/databricks-agent-skills345—~4.6kAutomated safety check: PassCustom licence
ML Model Trainingsecondsky/claude-skills2271 repos~1.7kAutomated safety check: PassMIT
Senior ML Engineerdavila7/claude-code-templates32k3 repos~1.4kAutomated safety check: PassMIT
Scikit Learn Machine Learningjaechang-hits/SciAgent-Skills3701 repos~4kAutomated safety check: PassBSD-3-Clause

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Works with

Categories

Questions about ML Engineer

What does ML Engineer do?

Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps. ML Engineer is an agent skill from RightNow-AI/openfang.

When should I use ML Engineer?

ML Engineer fits situations like: tasks that involve Machine learning; tasks that involve Deep learning; tasks that involve MLOps.

How do I install ML Engineer in Claude Code?

Run `npx skills add RightNow-AI/openfang --skill ml-engineer -a claude-code`. Or copy the skill folder (crates/openfang-skills/bundled/ml-engineer in RightNow-AI/openfang) into .claude/skills/ml-engineer in your project. Claude Code loads it when a task matches its description.

How do I install ML Engineer in Codex?

Run `npx skills add RightNow-AI/openfang --skill ml-engineer -a codex`. Or copy the skill folder (crates/openfang-skills/bundled/ml-engineer in RightNow-AI/openfang) into .agents/skills/ml-engineer in your project. Codex loads it when a task matches its description.

Can I use ML 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 RightNow-AI/openfang --skill ml-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/ml-engineer, .gemini/skills/ml-engineer, .github/skills/ml-engineer and .opencode/skills/ml-engineer in your project.

What does ML Engineer need to run?

SKILL.md names no scripts, command-line tools or credentials: ML Engineer is instructions for the agent only.

Does ML 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 ML 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 ML Engineer use?

ML Engineer is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does ML Engineer use?

About 987 tokens (SKILL.md is roughly 3.9k 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 ML Engineer?

Skills that share tags, products or a category with ML Engineer: Edit (omegaml/omegaml, 107 stars), Databricks ML Training (databricks/databricks-agent-skills, 345 stars), ML Model Training (secondsky/claude-skills, 227 stars) and Senior ML Engineer (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains ML Engineer?

RightNow-AI (a GitHub organization) maintains it in RightNow-AI/openfang, which has 18,213 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on July 2, 2026.

Source: RightNow-AI/openfang on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.