ml-pipeline

v0.4.0

Data-first ML discipline: forces coding agents through a strict 16-step pipeline — inspect and understand the raw data, define the prediction problem, clean, engineer, and split it BEFORE any model training — with explicit user-permission gates, marimo notebooks, and matplotlib visuals at every step.

Codex1 Skill

By Jananthan ParamsothyLicense: Apache-2.01.1k GitHub starsUpdated 13 hours ago

Directory evidence

Runtimes: Codex
Parsed components: 1 skill or MCP entry
Source updated: Sep 23, 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 ml-pipeline for Codex

Installs for the current user

codex plugin marketplace add hashgraph-online/awesome-codex-plugins
codex plugin marketplace upgrade awesome-codex-plugins
codex plugin add ml-pipeline@awesome-codex-plugins

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

Compatibility: the page URL and API slug “ml-pipeline” remain stable.

Codex: ml-pipeline@agent-plugin-marketplace → ml-pipeline@awesome-codex-plugins

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/hashgraph-online/awesome-codex-plugins

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is plugins/jananthan30/ml-pipeline/.

Plugin files

plugins/jananthan30/ml-pipeline/

├── .codex-plugin/plugin.json
└── skills/ml-pipeline/SKILL.md

Included Skills1

ml-pipelineskills/ml-pipeline/SKILL.md

MANDATORY whenever a task involves training, fine-tuning, tuning, or evaluating a machine-learning model on data (tabular, time series, text, images — any modality). Enforces a strict 16-step pipeline that starts with inspecting the raw data, gates each phase behind the user's explicit permission, and produces marimo notebooks with matplotlib visuals so the user can see and understand every step. Never jump straight to model training.

Plugin manifests1

plugins/jananthan30/ml-pipeline/.codex-plugin/plugin.json

{
  "name": "ml-pipeline",
  "version": "0.4.0",
  "description": "Data-first ML discipline: forces coding agents through a strict 16-step pipeline — inspect and understand the raw data, define the prediction problem, clean, engineer, and split it BEFORE any model training — with explicit user-permission gates, marimo notebooks, and matplotlib visuals at every step.",
  "author": {
    "name": "Jananthan Paramsothy"
  },
  "license": "Apache-2.0",
  "homepage": "https://github.com/jananthan30/ml-pipeline",
  "repository": "https://github.com/jananthan30/ml-pipeline",
  "keywords": [
    "machine-learning",
    "ml-pipeline",
    "eda",
    "data-cleaning",
    "data-leakage",
    "marimo",
    "matplotlib",
    "workflow-discipline"
  ],
  "skills": "./skills/",
  "interface": {
    "displayName": "ML Pipeline",
    "developerName": "Jananthan Paramsothy",
    "category": "Productivity",
    "shortDescription": "Stops coding agents from jumping straight to model training.",
    "longDescription": "Enforces a strict 16-step, data-first ML pipeline: data inspection, EDA, and prediction-problem definition before any cleaning or modeling; train/val/test split before feature engineering and preprocessing; a baseline before any complex model; and a final test set that is touched exactly once. Explicit user-permission gates after each phase, marimo notebooks as the workbench, matplotlib figures at every data-facing step, and a per-project PIPELINE.md checklist so sessions resume instead of restarting.",
    "description": "Stops coding agents from jumping straight to model training. 16 strict steps, 4 permission gates, visuals at every step.",
    "capabilities": [
      "skills"
    ],
    "websiteURL": "https://github.com/jananthan30/ml-pipeline"
  }
}