when-developing-ml-models-use-ml-expert
Build ML Models With Expert Workflow
ML model projects need a repeatable path from data preparation to deployment. This skill gives Claude, Codex, and Claude Code a structured workflow for training, evaluation, and packaging.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "when-developing-ml-models-use-ml-expert" from https://skillstore.io/skills/dnyoussef-when-developing-ml-models-use-ml-expert.md and its manifest at https://skillstore.io/api/skills/dnyoussef-when-developing-ml-models-use-ml-expert/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.
Test it
Using "when-developing-ml-models-use-ml-expert". Train a classification model for customer churn prediction.
Expected outcome:
- Data audit summary with missing values, feature types, and target balance.
- Recommended dense neural network with training callbacks and validation metrics.
- Deployment checklist covering model file, scaler file, report, and inference entry point.
Using "when-developing-ml-models-use-ml-expert". Evaluate whether my trained model is ready for release.
Expected outcome:
- Test metric summary with overfitting indicators and validation gaps.
- Evaluation report outline with classification details and training history.
- Release readiness notes for artifact safety, reproducibility, and documentation.
Using "when-developing-ml-models-use-ml-expert". Create a mobile-friendly export plan for a TensorFlow model.
Expected outcome:
- TensorFlow Lite export plan with required preprocessing artifacts.
- Deployment package inventory for local inference and mobile integration.
- Operational limits for latency, model size, and trusted artifact handling.
Security Audit
Medium RiskMost static findings are Markdown backtick fences or safe relative artifact writes in SKILL.md, so they are adjudicated as false positives. One semantic issue remains: the generated inference script loads serialized ML artifacts without a trust-boundary warning, which can be unsafe with untrusted files.
Confirmed security concerns (1)
Risk Factors
⚙️ External commands (32)
📁 Filesystem access (8)
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<iframe src="https://skillstore.io/embed/skills/dnyoussef-when-developing-ml-models-use-ml-expert.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
DNYoussef. (2026). when-developing-ml-models-use-ml-expert security audit report (audit version 8) [Author version 1.0.0]. Skillstore. https://skillstore.io/skills/dnyoussef-when-developing-ml-models-use-ml-expert/audits/8BibTeX citation
@techreport{dnyoussef-dnyoussef-when-developing-ml-models-use-ml-expert-2026,
author = {DNYoussef},
title = {when-developing-ml-models-use-ml-expert security audit report (audit version 8)},
institution = {Skillstore},
year = {2026},
number = {8},
url = {https://skillstore.io/skills/dnyoussef-when-developing-ml-models-use-ml-expert/audits/8},
note = {Author version 1.0.0}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "when-developing-ml-models-use-ml-expert security audit report (audit version 8)"
version: "1.0.0"
type: report
authors:
- name: "DNYoussef"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/dnyoussef-when-developing-ml-models-use-ml-expert/audits/8"
identifiers:
- type: other
value: "skillstore:dnyoussef-when-developing-ml-models-use-ml-expert:audit:8"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Prototype a Classifier
Build a first supervised learning model with clean data splits, normalized features, and baseline validation metrics.
Prepare Deployment Artifacts
Export trained models, save preprocessing assets, and assemble a deployment folder with an inference entry point.
Compare Model Architectures
Evaluate task complexity, feature count, and data size before selecting a neural network or classical model approach.
Try These Prompts
Use this skill to build a basic classification model from my dataset. Ask for the target column, metric goal, and deployment format first.
Use this skill to clean my tabular data, split train validation test sets, select a model architecture, and define training callbacks.
Use this skill to design a production ML workflow with evaluation reports, model artifact packaging, and inference script generation.
Use this skill to review my ML pipeline for overfitting, leakage, metric choice, artifact safety, and deployment readiness.
Best Practices
- Define the target metric and acceptance threshold before training begins.
- Keep train, validation, and test data separated throughout preprocessing and evaluation.
- Record model parameters, training history, data assumptions, and artifact versions.
Avoid
- Do not evaluate only on training data or tune repeatedly on the test set.
- Do not package model artifacts without documenting preprocessing requirements.
- Do not load serialized artifacts from untrusted sources during inference.
Frequently Asked Questions
What type of ML work does this skill support?
Can it choose between TensorFlow and PyTorch?
Does it train a model automatically?
Can it help with production deployment?
What inputs should I provide?
Is it safe for sensitive datasets?
Developer Details
Author
DNYoussefLicense
MIT
Author version
v1.0.0
Skillstore revision
r1
Ref
c579efd02184f1128d6741d16e6c8d951d7d4208
Maintenance freshness
7/22/2026
Usage
3 downloads · 183 views
File structure