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data-analysis-ml-agent-skills

v0.2.0

Evidence-first AI agent skills for reliable data analysis, machine learning, and reproducible model validation.

CodexClaude Code7 Skills

By aiopshwangLicense: MIT8 GitHub starsUpdated 2 hours ago

Directory evidence

Runtimes
Codex and Claude Code
Parsed components
7 skill or MCP entries
Source updated
Aug 24, 2026
Manifest status
Canonical path parsed

The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →

Install plugin

Installs for the current user
codex plugin marketplace add IchenDEV/agent-plugin-mkt
codex plugin marketplace upgrade agent-plugin-marketplace
codex plugin add data-analysis-ml-agent-skills@agent-plugin-marketplace

Paste and run these commands in a terminal with Codex. They add and refresh the PluginsMP catalog, then install this plugin.

The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.

Get the source manually
git clone https://github.com/aiopshwang/data-analysis-ml-agent-skills

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The repository root is the plugin root.

Plugin files

data-analysis-ml-agent-skills/
├── .codex-plugin/plugin.json
├── .claude-plugin/plugin.json
├── skills/auditing-data-and-ground-truth/SKILL.md
├── skills/designing-leakage-safe-experiments/SKILL.md
├── skills/diagnosing-ml-failures/SKILL.md
├── skills/running-decision-grade-data-science/SKILL.md
├── skills/shipping-reproducible-results/SKILL.md
├── skills/using-data-analysis/SKILL.md
└── skills/validating-models-and-claims/SKILL.md

Included Skills7

auditing-data-and-ground-truthskills/auditing-data-and-ground-truth/SKILL.md

Audit datasets, joins, labels, and ground truth before analysis or modeling. Use when data meaning, row grain, time semantics, source-of-truth reliability, or label construction may invalidate conclusions; not for general model evaluation after the evidence base is already trusted.

designing-leakage-safe-experimentsskills/designing-leakage-safe-experiments/SKILL.md

Design leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.

diagnosing-ml-failuresskills/diagnosing-ml-failures/SKILL.md

Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance validation.

running-decision-grade-data-scienceskills/running-decision-grade-data-science/SKILL.md

Orchestrate an end-to-end data analysis or machine learning project from decision framing through reproducible handoff. Use when a request spans multiple lifecycle stages or an ambiguous modeling request must become a decision-ready result; use narrower audit or experiment-design skills for isolated reviews.

shipping-reproducible-resultsskills/shipping-reproducible-results/SKILL.md

Package completed data analysis and ML work so an independent recipient can reproduce the claimed results, verify artifact lineage, and operate the handoff within its stated scope. Use when finalizing a project, study, model package, or review bundle; not for deploying to a live system.

using-data-analysisskills/using-data-analysis/SKILL.md

Route data analysis and machine learning work to the right skill in this suite. Use when starting any analysis, modeling, validation, or reproducibility task and the matching specialized skill is not yet clear; not needed when one specific skill already clearly applies.

validating-models-and-claimsskills/validating-models-and-claims/SKILL.md

Validate trained models and analytical claims against their intended decision, independent evidence, and human-reviewed ground truth. Use when reviewing model performance, analysis conclusions, launch claims, or evaluation reports; use failure diagnosis instead when the main task is locating a known defect.

Plugin manifests2

.codex-plugin/plugin.json
{
  "name": "data-analysis-ml-agent-skills",
  "version": "0.2.0",
  "description": "Evidence-first AI agent skills for reliable data analysis, machine learning, and reproducible model validation.",
  "author": {
    "name": "aiopshwang",
    "url": "https://github.com/aiopshwang"
  },
  "homepage": "https://github.com/aiopshwang/data-analysis-ml-agent-skills#readme",
  "repository": "https://github.com/aiopshwang/data-analysis-ml-agent-skills",
  "license": "MIT",
  "keywords": [
    "agent-skills",
    "data-analysis",
    "data-science",
    "machine-learning",
    "data-quality",
    "model-validation",
    "reproducibility"
  ],
  "skills": "./skills/",
  "interface": {
    "displayName": "Decision-Grade Data Science",
    "shortDescription": "Evidence-first data analysis and ML workflows.",
    "longDescription": "Turn ambiguous questions and messy data into auditable analyses and decision-ready models through explicit problem framing, data and ground-truth audits, leakage-safe experiments, independent validation, failure diagnosis, and reproducible handoff.",
    "developerName": "aiopshwang",
    "category": "Productivity",
    "capabilities": [],
    "defaultPrompt": [
      "Audit this dataset before I model it.",
      "Design a leakage-safe modeling experiment.",
      "Validate whether these model results support the claim."
    ]
  }
}
.claude-plugin/plugin.json
{
  "$schema": "https://json.schemastore.org/claude-code-plugin-manifest.json",
  "name": "data-analysis-ml-agent-skills",
  "displayName": "Decision-Grade Data Science",
  "version": "0.2.0",
  "description": "Evidence-first AI agent skills for reliable data analysis, machine learning, and reproducible model validation.",
  "author": {
    "name": "aiopshwang",
    "url": "https://github.com/aiopshwang"
  },
  "homepage": "https://github.com/aiopshwang/data-analysis-ml-agent-skills#readme",
  "repository": "https://github.com/aiopshwang/data-analysis-ml-agent-skills",
  "license": "MIT",
  "keywords": [
    "agent-skills",
    "data-analysis",
    "data-science",
    "machine-learning",
    "data-quality",
    "model-validation",
    "reproducibility"
  ],
  "skills": "./skills/"
}

For maintainers

If you maintain this plugin, link to this source-backed listing from your README so users can review its manifest and indexed components.

[data-analysis-ml-agent-skills on Agent Plugins Marketplace](https://pluginsmp.com/plugins/data-analysis-ml-agent-skills)