🎯 JIRA with Gemini AI

An interactive CLI tool for JIRA issue management powered by Google's Gemini AI.

✨ Features

• 🔍 Smart Search: Query JIRA using JQL or filter IDs
• 🤖 AI-Powered Processing:
  • Edit issues with natural language instructions
  • Analyze issues for insights and patterns
  • Estimate story points automatically
  • Get workflow transition recommendations
  • Classify work types intelligently
• ⚡ Batch Processing: Handle large datasets efficiently (100 issues per batch)
• 💾 Export Results: Save processed issues to JSON files
• 🛠️ Debug-Friendly: Raw AI responses saved for inspection
• 📝 Manual Execution: Auto-generate bash scripts for manual JIRA updates

🚀 Installation

# Run directly with npx (recommended)
npx @khala/jira-ai

# Or install globally
npm install -g @khala/jira-ai
jira-gemini

⚙️ Setup

1. Environment Variables: Create a .env file with:

   # JIRA Configuration
   JIRA_TOKEN=your-jira-token
   JIRA_STAGE_TOKEN=your-stage-token  # Optional for staging
   IS_PROD=true  # false for staging environment
   PROXY_URL=your-proxy-url  # Optional for staging
   
   # Gemini AI Configuration
   GEMINI_API_KEY=your-gemini-api-key

2. JIRA Token: Get your token from JIRA settings → Personal Access Tokens

3. Gemini API Key: Get your key from Google AI Studio

🎮 Usage

Run the interactive CLI:

npx @khala/jira-ai

🔍 Dry Run Mode

Test the application without making actual changes to JIRA:

npx @khala/jira-ai --dry-run
# or
npx @khala/jira-ai -d

In dry run mode:

• No JIRA updates: All JIRA operations are simulated
• Clear indication: All prompts and messages show [DRY RUN] prefix
• Full workflow: Complete AI processing and confidence-based selection
• Simulated results: Issues saved to static/success.json as if updates succeeded
• Safe testing: Perfect for validating AI suggestions before applying changes

Search Options

• JQL Query: Use JIRA Query Language for advanced searches
• Filter ID: Use saved JIRA filter by ID

AI Actions

• Edit Issues: Provide natural language instructions to modify issues
• Analysis: Get insights about issue patterns, priorities, and trends
• Story Points: Automatically estimate story points using AI
• Workflow: Get recommendations for issue transitions
• Work Type Classification: Categorize issues by work type with confidence percentages

🎯 Confidence-Based JIRA Updates

After AI processing, the application offers a smart update workflow where you can selectively apply changes to JIRA:

1. 📊 Review Results: All AI enhancements saved to static/issues.json
2. 🎚️ Set Confidence Threshold: Choose minimum confidence level (default: 85%)
3. 🔍 Filter Eligible Issues: Only issues meeting the threshold are shown
4. ☑️ Select Update Types: Choose which AI-generated changes to apply:
   • Work Type Classifications → Update JIRA custom fields
   • AI Edits → Apply summary/description changes
   • Story Points → Update story points field
   • Workflow Transitions → Apply state changes
5. 📝 Select Specific Tickets: Choose individual tickets to update for each action type
6. 🚀 Apply to JIRA: Selectively update JIRA with high-confidence AI suggestions
7. 📜 Bash Scripts Generated: Receive executable scripts for manual review and execution

Confidence Levels:

• 95%: Very High Confidence (Most Restrictive)
• 85%: High Confidence (Default)
• 75%: Medium-High Confidence
• 65%: Medium Confidence
• 50%: Any Confidence (Least Restrictive)

📝 Manual Execution Scripts

For every JIRA update operation, the application automatically generates executable bash scripts that allow you to review and manually execute the exact API calls:

🎯 What's Generated

• Timestamped Scripts: static/jira-update-{action}-{timestamp}.sh
• Complete API Calls: Ready-to-run curl commands with proper authentication
• Environment Setup: Automatic JIRA URL and token configuration
• Error Handling: Success/failure tracking and reporting
• Prerequisites Guide: Installation and setup instructions included

🚀 Usage

# Make executable
chmod +x static/jira-update-*.sh

# Execute specific action script
./static/jira-update-work-type-2025-01-15T10-30-45.sh

# Review before execution
cat static/jira-update-story-points-2025-01-15T10-31-20.sh

✅ Benefits

• Review Control: Inspect exact API calls before execution
• Manual Timing: Execute updates when convenient
• Audit Trail: Keep scripts as records of changes
• Debugging: Easy to modify and re-run specific updates
• Offline Capability: Execute later without the CLI tool

🔧 Prerequisites for Script Execution

# Install jq for JSON processing
sudo apt-get install jq  # Ubuntu/Debian
brew install jq          # macOS

# Set environment variables
export JIRA_TOKEN="your-jira-token"
export IS_PROD="true"  # or "false" for staging

Scripts are generated in both regular and dry run modes, giving you maximum flexibility and control.

📊 Work Type Categories

• Associate well being: Engineer's well being
• Future sustainability: Better future work
• Incidents and support: Outages and problems
• Quality / Stability / Reliability: Quality assurance
• Security and compliance: Product security
• Product / Portfolio work: Product itself

🔧 Development

# Clone and install
git clone https://github.com/karelhala/jira-with-ai.git
cd jira-with-ai
npm install

# Run locally
npm start

# Lint and format code
npm run lint      # Check for issues
npm run lint:fix  # Fix linting and formatting issues

📝 Output

• Console: Real-time progress and results
• static/issues.json: Processed issues with AI enhancements
• static/success_issues.json: Successfully updated issues (moved from issues.json after JIRA updates)
• static/success.json: Dry run results with simulated successful updates (dry run mode only)
• static/raw_*_*.txt: Debug files with raw AI responses
• static/jira-update-*-{timestamp}.sh: Executable bash scripts for manual JIRA updates

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run npm run lint to ensure code quality
5. Submit a pull request

📄 License

Apache-2.0 - see LICENSE file for details.

🏷️ Keywords

jira ai gemini cli automation issue-management google-ai workflow