issuedb 2.2.0

A command-line issue tracking system for software development projects

IssueDB

A command-line issue tracking system for software development projects. IssueDB provides a simple yet concrete way to manage issues, bugs, and tasks directly from your terminal with a per-directory database model - each directory gets its own issue database.

Features

• Per-Directory Databases: Each directory has its own issuedb.sqlite - your issues live where your code lives
• Simple Issue Management: Create, update, delete, and list issues
• Bulk Operations: Update multiple issues at once with filters, or create/update/close multiple issues from JSON
• Priority Levels: Categorize issues as low, medium, high, or critical
• Status Tracking: Track issues through open, in-progress, and closed states
• FIFO Queue Management: Get the next issue to work on based on priority and creation date
• Full-text Search: Search issues by keyword in title and description
• Summary & Reports: Aggregate statistics and detailed breakdowns by status/priority
• Audit Logging: Complete immutable history of all changes
• JSON Output: Machine-readable output for scripting and automation
• LLM Agent Integration: Built-in prompt for programmatic usage
• Natural Language Interface: Ollama integration for conversational issue management
• Local Storage: SQLite database with no external dependencies
• Zero Dependencies: Uses only Python standard library

Per-Directory Project Model

IssueDB v2.0 uses a per-directory model where each directory has its own issuedb.sqlite database:

my-workspace/
├── frontend/
│   ├── src/
│   ├── issuedb.sqlite      # Frontend issues
│   └── README.md
├── backend/
│   ├── api/
│   ├── issuedb.sqlite      # Backend issues
│   └── README.md
└── docs/
    ├── guides/
    └── issuedb.sqlite          # Documentation issues

Benefits

• Better isolation: Each project/directory has its own independent database
• Simpler mental model: Your issues are where your code is
• Easier backup: Just backup the directory to preserve all issues
• Natural organization: Filesystem directories already organize projects
• Git-friendly: Database file can be .gitignored or committed per project needs

Working with Multiple Projects

# Work on frontend issues
cd ~/workspace/frontend
issuedb-cli create -t "Fix navbar bug"
issuedb-cli list

# Switch to backend issues
cd ~/workspace/backend
issuedb-cli create -t "Add authentication"
issuedb-cli list

# Each directory maintains its own issues

Installation

From PyPI (when published)

pip install issuedb

From Source

git clone https://github.com/rodmena-limited/issue-queue
cd issuedb
pip install -e .

Quick Start

Create your first issue

cd ~/my-project
issuedb-cli create --title "Fix login bug" --description "Users cannot log in with special characters" --priority high

List all open issues

issuedb-cli list --status open

Get the next issue to work on

issuedb-cli get-next

Usage

Creating Issues

Create a new issue:

issuedb-cli create -t "Add user authentication"

With additional details:

issuedb-cli create \
  --title "Implement OAuth2" \
  --description "Add Google and GitHub OAuth providers" \
  --priority high \
  --status open

Listing Issues

List all issues in current directory:

issuedb-cli list

Filter by status and priority:

issuedb-cli list --status open --priority high

Limit results:

issuedb-cli list --limit 10

Getting Issue Details

View a specific issue:

issuedb-cli get 42

Updating Issues

Update issue status:

issuedb-cli update 42 --status in-progress

Update multiple fields:

issuedb-cli update 42 \
  --title "Updated title" \
  --priority critical \
  --status in-progress

Bulk Updates

Close all open issues:

issuedb-cli bulk-update --filter-status open -s closed

Set all critical issues to high priority:

issuedb-cli bulk-update --filter-priority critical --priority high

Bulk Operations (JSON)

Create multiple issues at once:

# From stdin
echo '[
  {"title": "Issue 1", "priority": "high", "description": "First issue"},
  {"title": "Issue 2", "priority": "medium"},
  {"title": "Issue 3"}
]' | issuedb-cli --json bulk-create

# From file
issuedb-cli --json bulk-create -f issues.json

# Inline data
issuedb-cli --json bulk-create -d '[{"title": "Quick issue", "priority": "low"}]'

Update multiple specific issues:

# Update different fields on different issues
echo '[
  {"id": 1, "status": "closed", "description": "Completed"},
  {"id": 2, "priority": "high", "title": "Updated title"},
  {"id": 3, "status": "in-progress"}
]' | issuedb-cli --json bulk-update-json

Close multiple issues by ID:

# Close issues 1, 2, 3, and 5
echo '[1, 2, 3, 5]' | issuedb-cli --json bulk-close

# Or from file
issuedb-cli --json bulk-close -f issue_ids.json

Deleting Issues

Delete an issue (with audit trail preserved):

issuedb-cli delete 42

Comments

Add comments to issues to track notes, resolutions, or updates:

# Add a comment to an issue
issuedb-cli comment 42 -t "Fixed by updating the configuration file"

# List all comments on an issue
issuedb-cli list-comments 42
issuedb-cli --json list-comments 42

# Delete a comment
issuedb-cli delete-comment 5

Common use case - close an issue with a comment:

issuedb-cli update 42 -s closed && issuedb-cli comment 42 -t "Resolved: Updated dependencies to v2.0"

Getting Next Issue

Get the highest priority oldest issue:

issuedb-cli get-next

With status filter:

issuedb-cli get-next --status open

Searching Issues

Search by keyword:

issuedb-cli search --keyword "login"

With limit:

issuedb-cli search -k "bug" -l 5

Clearing All Issues

Clear all issues in current directory (requires confirmation):

issuedb-cli clear --confirm

Viewing Audit Logs

View all changes for an issue:

issuedb-cli audit --issue 42

View all audit logs:

issuedb-cli audit

Database Information

Get database statistics:

issuedb-cli info

Summary Statistics

Get aggregate statistics:

issuedb-cli summary

Summary shows:

• Total issue count
• Count by status (open, in-progress, closed)
• Count by priority (low, medium, high, critical)
• Percentage breakdowns

Detailed Report

Get a detailed report grouped by status:

issuedb-cli report

Get report grouped by priority:

issuedb-cli report --group-by priority

Reports include:

• Full issue details grouped by status or priority
• Count for each group
• Total issues

JSON Output

All commands support JSON output for scripting and automation:

issuedb-cli list --json | jq '.[].title'

issuedb-cli get-next --json | jq '.id'

Command Reference

Commands

• create - Create a new issue
• list - List issues with optional filters
• get - Get details of a specific issue
• update - Update issue fields
• bulk-update - Bulk update multiple issues
• delete - Delete an issue
• get-next - Get the next issue to work on
• search - Search issues by keyword
• clear - Clear all issues in current directory
• audit - View audit logs
• info - Get database information
• summary - Get summary statistics of issues
• report - Get detailed report of issues grouped by status or priority

Global Options

• --db PATH - Use a custom database file (default: ./issuedb.sqlite)
• --json - Output results in JSON format
• --prompt - Display LLM agent guide for automated usage
• --ollama REQUEST - Generate and execute command from natural language via Ollama
• --ollama-model MODEL - Ollama model to use (default: llama3)
• --ollama-host HOST - Ollama server host (default: localhost)
• --ollama-port PORT - Ollama server port (default: 11434)

Priority Levels

• low - Low priority
• medium - Medium priority (default)
• high - High priority
• critical - Critical priority

Status Values

• open - Issue is open (default)
• in-progress - Issue is being worked on
• closed - Issue is resolved

Examples

Example Workflow

# Navigate to your project
cd ~/my-project

# Create some issues
issuedb-cli create -t "Setup CI/CD pipeline" --priority high
issuedb-cli create -t "Add unit tests" --priority medium
issuedb-cli create -t "Update documentation" --priority low

# Get the next issue to work on
issuedb-cli get-next

# Start working on it
issuedb-cli update 1 --status in-progress

# Complete the issue
issuedb-cli update 1 --status closed

# Check remaining open issues
issuedb-cli list --status open

Multi-Project Workflow

# Work on frontend
cd ~/projects/frontend
issuedb-cli create -t "Fix navbar styling"
issuedb-cli list

# Work on backend
cd ~/projects/backend
issuedb-cli create -t "Add authentication endpoint"
issuedb-cli list

# Each project maintains its own issues

Integration with Scripts

#!/bin/bash
# Get next issue ID and mark it as in-progress
cd ~/my-project
ISSUE_ID=$(issuedb-cli get-next --json | jq -r '.id')
if [ "$ISSUE_ID" != "null" ]; then
    echo "Working on issue $ISSUE_ID"
    issuedb-cli update $ISSUE_ID --status in-progress
fi

LLM Agent Integration

IssueDB is designed to be easily used by LLM agents:

import subprocess
import json
import os

def get_next_issue(project_dir):
    os.chdir(project_dir)
    result = subprocess.run(
        ["issuedb-cli", "get-next", "--json"],
        capture_output=True,
        text=True
    )
    return json.loads(result.stdout) if result.returncode == 0 else None

def create_issue(project_dir, title, description=None, priority="medium"):
    os.chdir(project_dir)
    cmd = ["issuedb-cli", "create",
           "--title", title,
           "--priority", priority,
           "--json"]
    if description:
        cmd.extend(["--description", description])

    result = subprocess.run(cmd, capture_output=True, text=True)
    return json.loads(result.stdout) if result.returncode == 0 else None

Advanced LLM Agent Integration

IssueDB provides a specialized prompt optimized for LLM agents to use the CLI directly:

# Get the LLM agent guide
issuedb-cli --prompt

This outputs a comprehensive guide that instructs LLM agents to:

• Output only executable shell commands (no markdown, no explanations)
• Use proper syntax and quoting
• Leverage JSON output for machine parsing
• Follow FIFO priority-based workflows

Example LLM agent workflow:

import subprocess
import json
import os

# Get the agent prompt to include in your LLM context
prompt = subprocess.run(["issuedb-cli", "--prompt"], capture_output=True, text=True).stdout

# Your LLM can now generate direct issuedb-cli commands
# Example: User says "Create a high priority bug for login issues"
# LLM outputs: issuedb-cli create -t "Fix login bug" --priority high

# Execute the command in the correct directory
os.chdir("~/my-project")
result = subprocess.run(llm_generated_command.split(), capture_output=True, text=True)

The prompt ensures LLM agents generate commands that are:

• Directly executable without post-processing
• Properly quoted for shell safety
• Machine-readable when using --json flag
• Priority-aware for optimal workflow

Natural Language Interface with Ollama

IssueDB can integrate directly with Ollama for natural language command generation:

# Use natural language to create issues
issuedb-cli --ollama "we have many junk files and we need to fix it fast"

# Get the next task to work on
issuedb-cli --ollama "what should I work on next?"

# Search for issues
issuedb-cli --ollama "find all critical bugs"

# Update issues
issuedb-cli --ollama "mark issue 42 as completed"

Setup

1. Install and start Ollama:

# Install Ollama
curl https://ollama.ai/install.sh | sh

# Pull a model (e.g., llama3, mistral, codellama)
ollama pull llama3

# Start Ollama server
ollama serve

2. Use issuedb-cli with natural language:

cd ~/my-project
issuedb-cli --ollama "create a critical bug for login failures"

Configuration

Configure Ollama connection via command-line flags or environment variables:

# Command-line flags
issuedb-cli --ollama "your request" \
  --ollama-model mistral \
  --ollama-host localhost \
  --ollama-port 11434

# Environment variables
export OLLAMA_HOST=localhost
export OLLAMA_PORT=11434
export OLLAMA_MODEL=llama3

issuedb-cli --ollama "your request"

Default values:

• Model: llama3 (or from OLLAMA_MODEL env)
• Host: localhost (or from OLLAMA_HOST env)
• Port: 11434 (or from OLLAMA_PORT env)

How It Works

1. Ollama receives your natural language request
2. The agent prompt guides the LLM to generate a valid issuedb-cli command
3. The command is automatically validated and executed
4. Results are displayed in your terminal

The integration uses only Python standard library (urllib), keeping the package dependency-free.

Database

IssueDB uses a local SQLite database stored at ./issuedb.sqlite in the current directory. The database includes:

• issues table - Stores all issue data
• audit_logs table - Immutable audit trail of all changes
• Comprehensive indexes for optimal query performance

The database is automatically created on first use in each directory.

Custom Database Location

You can specify a custom database path:

issuedb-cli --db ~/custom/location/issues.db create -t "Test"

Testing

Run the test suite:

pytest

Run with coverage:

pytest --cov=issuedb

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/rodmena-limited/issue-queue
cd issuedb

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e .
pip install pytest pytest-cov ruff

Code Formatting and Linting

# Format code
ruff format .

# Check linting
ruff check .

Running Tests

# Run all tests
pytest

# Run specific test file
pytest tests/test_repository.py

# Run with verbose output
pytest -v

License

Apache License 2.0 - See LICENSE file for details

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Support

For issues, questions, or suggestions, please open an issue on GitHub.

Roadmap

• Export/import functionality
• Issue templates
• Tags/labels support
• Due dates
• Issue relationships (blocks, depends on)
• Web UI (optional)
• Backup and restore utilities