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Jira Automation CLI

Jira workflow automation and metrics reporting CLI tool - automates ticket transitions, generates sprint reports, and extracts actionable insights from project data

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Python Jira API CLI Pandas REST API

Project Overview

Jira workflow automation and metrics reporting CLI tool - automates ticket transitions, generates sprint reports, and extracts actionable insights from project data

README.md
README.md

Project Overview

A command-line tool that automates repetitive Jira workflows and generates actionable metrics reports. Built to streamline project management tasks and provide data-driven insights into team velocity, sprint health, and ticket lifecycle patterns.

Key Features

Workflow Automation

  • Bulk ticket transitions (e.g., move all Done tickets to Closed)
  • Automated sprint creation and assignment
  • Label synchronization across projects
  • Custom field updates based on rules

Metrics & Reporting

  • Sprint velocity tracking and trend analysis
  • Cycle time calculation (creation → resolution)
  • Workload distribution across team members
  • Burndown/burnup chart data generation
  • Blocker and dependency analysis

Data Export

  • CSV export for Excel analysis
  • JSON output for pipeline integration
  • Formatted markdown reports for standups
  • Custom report templates

Technical Implementation

Technologies Used

  • Python 3.10+: Core development
  • Jira REST API v3: Issue and project data access
  • Pandas: Data aggregation and analysis
  • Click: CLI framework for command structure
  • Rich: Terminal formatting and progress bars
  • python-dotenv: Configuration management

CLI Commands

# Transition tickets
jira-auto transition --from "In Progress" --to "Done" --project PROJ

# Generate sprint report
jira-auto report sprint --sprint "Sprint 23" --format markdown

# Export metrics
jira-auto export velocity --last 6 --output velocity.csv

# Analyze cycle time
jira-auto metrics cycle-time --project PROJ --days 30

Architecture

  1. API Client: Jira REST API wrapper with rate limiting
  2. Data Models: Pydantic models for Jira entities
  3. Transform Layer: Data cleaning and enrichment
  4. Analysis Engine: Metrics calculation
  5. CLI Interface: Click commands with Rich output

Real-World Impact

  • Reduced sprint closeout time from 30 minutes to 2 minutes
  • Identified bottlenecks causing 20% longer cycle times
  • Automated weekly status report generation
  • Enabled data-driven sprint planning decisions

Challenges

  1. API Rate Limiting: Implemented exponential backoff and request batching
  2. JQL Complexity: Built query builder for common filter patterns
  3. Data Consistency: Handled timezone issues and custom field variations
  4. Authentication: Supported both API tokens and OAuth flows
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