Engineering Workflow Dashboard and Jira Automation Lab

Python automation project for engineering workflow reporting and Jira-based infrastructure ticket routing.

This project demonstrates how Python can connect to workflow tools, normalize messy operational data, generate human-readable reports, and safely plan or apply Jira workflow updates in a sandbox.

What This Project Does

The repo has two related workflows.

1. Workflow reporting pipeline

• connects to Jira Cloud and GitHub REST APIs
• exports workflow data to CSV
• normalizes nested API payloads
• computes summary metrics
• generates Markdown and HTML reports
• keeps runtime settings in config.toml
• keeps secrets out of Git through .env

2. Jira infrastructure routing automation

• fetches real Jira issues from a personal sandbox project
• classifies tickets into:
  • Linux Operations
  • Windows Operations
  • Network Operations
  • Human Review
• builds a dry-run action plan by default
• optionally applies live Jira sandbox updates
• adds audit comments
• applies Jira-safe labels such as linux-operations
• attempts workflow transition when the Jira workflow allows it

Why This Matters

Engineering and operations teams often work across messy tickets, inconsistent human-entered strings, and disconnected tools.

This project explores a practical pattern:

raw workflow data
→ normalized records
→ classification
→ dry-run action plan
→ safe live update only after review

The goal is not to create a production enterprise Jira app. The goal is to demonstrate practical Python automation patterns for operational workflows.

Key Lessons Learned

During live Jira sandbox integration, the workflow exposed real integration issues:

• expired Jira API token
• missing https:// in the base URL
• JQL status mismatch
• Jira description fields returning unexpected shapes
• Jira Cloud comments requiring Atlassian Document Format
• Jira labels rejecting spaces
• need to separate human-readable names from API-safe machine values
• need for dry-run mode before live updates

Main Files

• scripts/jira_client.py — Jira REST API boundary. Handles authentication, issue search, comments, labels, transitions, and response/error handling.
• scripts/infra_ticket_classifier.py — Deterministic classification logic for routing tickets into Linux, Windows, Network, or Human Review categories.
• scripts/infra_workflow_orchestrator.py — Coordinates classification, action planning, dry-run behavior, and live execution.
• scripts/route_jira_issues.py — Command-line entry point for the Jira routing workflow.
• tests/ — Pytest coverage for the Jira client, classifier, and orchestrator behavior.
• docs/ — Learning maps, code-flow maps, and project documentation.

Sequential Code Flow

python -m scripts.route_jira_issues

1. route_jira_issues.py starts the command.
2. JiraClient searches Jira issues.
3. InfraWorkflowOrchestrator receives the issue list.
4. InfraTicketClassifier classifies each issue.
5. The orchestrator builds an action dictionary.
6. Dry-run mode prints the action only.
7. Live mode calls JiraClient to add comments, update labels, and transition issues.

Safety Boundaries

• This project uses a personal Jira Cloud sandbox.
• .env secrets are not committed.
• Dry-run is the default.
• Live mode should only be used after dry-run output is reviewed.
• No employer, customer, banking, or production data is used.
• This is project-practice automation, not production enterprise deployment.

Setup

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Create .env from .env.example:

cp .env.example .env

Expected .env shape:

JIRA_BASE_URL=https://your-site.atlassian.net
JIRA_EMAIL=your-email@example.com
JIRA_API_TOKEN=your-token
GITHUB_TOKEN=optional-token

Do not commit .env.

Run Tests

python -m pytest -q

Run focused tests:

python -m pytest -q tests/test_jira_client.py
python -m pytest -q tests/test_infra_ticket_classifier.py
python -m pytest -q tests/test_infra_workflow_orchestrator.py

Run Read-Only Jira Checks

python -m scripts.connect_to_jira
python -m scripts.fetch_jira_issues

Run Jira Routing in Dry-Run Mode

python -m scripts.route_jira_issues

Dry-run mode prints planned actions and does not modify Jira.

Run Live Mode

Only use live mode against a sandbox project after reviewing dry-run output:

python -m scripts.route_jira_issues --live

Example Dry-Run Output

Processing 12 issues...
Dry-run mode: No changes made.
- PW-11: Human Review
- PW-10: Human Review
- PW-13: Linux Operations

What This Demonstrates

• Python automation
• Jira REST API integration
• workflow orchestration
• deterministic ticket classification
• dry-run/live execution safety
• API error debugging
• structured operational reporting
• pytest-tested automation logic
• practical infrastructure workflow automation