Skills machine-learning
📦

machine-learning

v1.0.1 Content revision r1 Safe ⚙️ External commands

Build Machine Learning Pipelines

Machine learning projects fail when data, metrics, and deployment choices are unclear. This skill gives structured guidance for model planning, preprocessing, evaluation, and MLOps.

Supports: Claude Codex Code(CC)
🥉 77 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "machine-learning" from https://skillstore.io/skills/89jobrien-machine-learning.md and its manifest at https://skillstore.io/api/skills/89jobrien-machine-learning/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/89jobrien-machine-learning.md Signed manifest GET /api/skills/89jobrien-machine-learning/manifest Signed lockfile GET /api/skills/89jobrien-machine-learning/lockfile

Test it

Using "machine-learning". A product team wants churn prediction from account, usage, billing, and support data.

Expected outcome:

The skill would frame this as binary classification, recommend stratified splits, suggest AUC-ROC and PR-AUC, and flag leakage from post-churn fields.

Using "machine-learning". A dataset has missing numerical values, high-cardinality categories, and strong outliers.

Expected outcome:

The skill would recommend train-only imputation, target or frequency encoding review, robust scaling, clipping options, and pipeline-based preprocessing.

Using "machine-learning". An ML team wants to release a model behind an API.

Expected outcome:

The skill would outline staging checks, shadow testing, gradual rollout, latency monitoring, drift tracking, model versioning, and rollback planning.

Security Audit

Safe
v6 • 7/5/2026 Open versioned report

All six static findings are false positives caused by Markdown formatting or machine learning terminology. No prompt injection, malicious intent, data exfiltration, or unsafe execution behavior was found in the reviewed files.

2
Files scanned
374
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

⚙️ External commands (4)
SKILL.md:178-187 SKILL.md:187-206 SKILL.md:206-207 SKILL.md:207-208
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
Share & cite this report

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Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

Joseph OBrien. (2026). machine-learning security audit report (audit version 6) [Author version 1.0.1]. Skillstore. https://skillstore.io/skills/89jobrien-machine-learning/audits/6

BibTeX citation

@techreport{joseph-obrien-89jobrien-machine-learning-2026, author = {Joseph OBrien}, title = {machine-learning security audit report (audit version 6)}, institution = {Skillstore}, year = {2026}, number = {6}, url = {https://skillstore.io/skills/89jobrien-machine-learning/audits/6}, note = {Author version 1.0.1} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "machine-learning security audit report (audit version 6)" version: "1.0.1" type: report authors: - name: "Joseph OBrien" date-released: "2026-07-05" url: "https://skillstore.io/skills/89jobrien-machine-learning/audits/6" identifiers: - type: other value: "skillstore:89jobrien-machine-learning:audit:6" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High
41
Architecture
100
Maintainability
87
Content
70
Community
91
Spec Compliance

What You Can Build

Plan a New ML Project

Define the problem type, success metrics, data split strategy, and first model candidates before implementation begins.

Prepare Training Data

Choose imputation, encoding, scaling, outlier handling, and leakage prevention steps for a reliable dataset.

Review Production Readiness

Check serving approach, model versioning, CI/CD tests, monitoring signals, and retraining triggers before launch.

Try These Prompts

Choose the ML Approach 
Use the machine-learning skill to classify my problem type, recommend metrics, and suggest baseline models. My goal is: [describe goal]. My data is: [describe data].
Design Data Preparation 
Use the machine-learning skill to design preprocessing for this dataset: [columns, types, missing values, target]. Include leakage risks and train-test split guidance.
Build an Evaluation Plan 
Use the machine-learning skill to create an evaluation plan for [classification, regression, ranking, clustering]. Include primary metrics, validation strategy, and error analysis steps.
Prepare for Production ML 
Use the machine-learning skill to review production readiness for this model: [model, latency needs, traffic, monitoring plan]. Identify deployment risks and retraining triggers.

Best Practices

  • Define the prediction target, metric, and data split before selecting models.
  • Fit preprocessing only on training data, then transform validation and test data.
  • Track code version, data version, hyperparameters, metrics, and model artifacts for every experiment.

Avoid

  • Do not tune on the test set or report validation results as final performance.
  • Do not use future information or target-derived fields as model inputs.
  • Do not deploy without monitoring latency, input drift, prediction drift, and error rates.

Frequently Asked Questions

Can this skill train a model directly?
No. It provides workflow guidance and patterns. You still need data, code, compute, and framework-specific implementation.
Which AI tools can use this skill?
The report declares support for Claude, Codex, and Claude Code.
Does it cover feature engineering?
Yes. It covers numerical, categorical, temporal, text, geospatial, interaction, and preprocessing patterns.
Does it help with model evaluation?
Yes. It recommends metrics by problem type and emphasizes held-out tests, cross-validation, overfitting checks, and error analysis.
Is it specific to one machine learning framework?
No, but the included preprocessing examples are closest to Python and scikit-learn workflows.
Does it include production guidance?
Yes. It covers serving patterns, versioning, CI/CD checks, monitoring signals, fallback strategies, and retraining triggers.

Developer Details

License

MIT

Author version

v1.0.1

Skillstore revision

r1

Repository

https://github.com/89jobrien/steve/tree/main/steve/skills/machine-learning

Ref

c4037264bbd363c572662d6154a3ab28f5ca4f53

Maintenance freshness

7/18/2026

Usage

32 downloads · 224 views

File structure

📁 references/

📄 preprocessing.md

📄 SKILL.md