O*NET Occupation Explorer

An interactive HTML dashboard that pulls occupation data from the O*NET Web Services API and presents tasks, skills, knowledge, abilities, and AI Impact analysis in a self-contained, browser-ready report.

Features

• Occupation Search — search by keyword and select from matching O*NET occupations
• Interactive Dashboard — single-file HTML with tabbed navigation and Chart.js visualizations
• Six Analysis Tabs:
  • Overview — occupation summary with top skills, knowledge, and abilities at a glance
  • AI Impact — AI automation/augmentation scoring, recommended AI agents, and AI-era skills
  • Tasks — sortable task list ranked by importance
  • Skills — horizontal bar chart of skill importance ratings
  • Knowledge — knowledge domain analysis
  • Abilities — ability requirements breakdown
• AI Impact Analysis Engine — classifies every task as automate, augment, or human-essential using keyword pattern matching, then recommends relevant AI agents and skills
• Zero Dependencies — uses only Python standard library (urllib, json, argparse, re, html)

Prerequisites

1. Python 3.7+
2. O*NET Web Services API key — register free at services.onetcenter.org and generate a key from My Account

Quick Start

# Clone the repo
git clone https://github.com/johneparker/onet-explorer.git
cd onet-explorer

# Run with API key as argument
python onet_explorer.py "software developer" --api-key YOUR_API_KEY

# Or set environment variable
export ONET_API_KEY=your_api_key
python onet_explorer.py "registered nurse"

The script will:

1. Search O*NET for matching occupations
2. Let you select one interactively
3. Pull tasks, skills, knowledge, and abilities data
4. Run AI Impact analysis
5. Generate a self-contained HTML dashboard (e.g., onet_15-1252.00.html)

Open the generated HTML file in any browser — no server required.

Usage

python onet_explorer.py [-h] [--api-key API_KEY] [--output OUTPUT] keyword

Argument	Description
keyword	Occupation keyword to search (e.g., "data scientist")
--api-key	O*NET API key (or set ONET_API_KEY env var)
--output, -o	Output HTML filename (default: onet_<code>.html)

Examples

python onet_explorer.py "financial analyst"
python onet_explorer.py "registered nurse" -o nurse_dashboard.html
python onet_explorer.py "project manager" --api-key 0rQoQ-xxxxx-xxxxx-xxxxx

AI Impact Analysis

The AI Impact tab provides a data-driven assessment of how artificial intelligence may affect the occupation:

• Overall AI Impact Score (0–100) — weighted by task importance
• Task Classification — each task categorized as automate, augment, or human-essential
• AI Agent Recommendations — up to 8 relevant AI agents (e.g., Data Analytics Agent, Code Assistant, Document Processing Agent) with relevance scores
• AI Skills Recommendations — prioritized list of AI-era skills professionals should develop
• Strategic Outlook — narrative summary of the AI impact profile

The classification engine uses regex-based keyword pattern matching against three category dictionaries, with weighted scoring that includes a conservative bias toward human-essential classification to avoid over-claiming AI capability.

Web App (Render Deployment)

A Flask web interface is also included, letting users search and view dashboards in the browser without running the CLI.

Deploy to Render

1. Fork or push this repo to your GitHub account
2. Go to render.com → New → Blueprint
3. Connect this repository — Render will auto-detect render.yaml
4. Set the environment variable when prompted:
   • ONET_API_KEY — your O*NET Web Services API key
5. Deploy — your app will be live at https://onet-explorer.onrender.com

Run Locally

pip install -r requirements.txt
export ONET_API_KEY=your_api_key
python app.py

Then visit http://localhost:5000 in your browser.

How It Works

1. API Client — makes authenticated requests to the O*NET Web Services v2 REST API using X-API-Key header
2. Data Retrieval — fetches occupation summary, tasks, skills, knowledge, and abilities
3. AI Analysis — classifies tasks, scores overall impact, recommends agents and skills
4. Dashboard Generation — produces a single HTML file with embedded CSS, JavaScript, Chart.js charts, and JSON data

License

MIT