dynamic-cursor-rules 1.2.4

Generate and manage documentation and task tracking for Cursor IDE projects

Cursor Rules

A high-performance framework for managing custom instructions for AI assistants.

Installation

pip install dynamic-cursor-rules

For LLM integration features:

pip install dynamic-cursor-rules[llm]

Usage

from cursor_rules import RuleSet, Rule, parse_file, RuleExecutor

# Create a rule set
ruleset = RuleSet(name="My Rules", description="Custom rules for my assistant")

# Add some rules
ruleset.add_rule(Rule(
    content="Always respond in a friendly tone.",
    id="tone_friendly",
    priority=10,
    tags=["tone"]
))

# Save rules to a file
ruleset.save("my_rules.json")

# Load rules from different formats
markdown_ruleset = parse_file("rules.md")

# Apply rules in a production environment
executor = RuleExecutor(ruleset)
context = {
    "user_input": "Tell me about Python",
    "tags": ["programming"]
}
result = executor.apply_rules(context)
print(result["instructions"])

Features

• Parse rules from Markdown, YAML, and JSON formats
• Prioritize and tag rules for contextual application
• Validate rule sets to ensure correctness
• Apply rules within AI assistant workflows
• Generate .cursorrules files for Cursor IDE integration
• Extract and track tasks from project initialization documents
• Generate complete documentation suites from initialization documents
• Simple and elegant API
• Integration with LLM providers (OpenAI, Anthropic)

Rule Management

Markdown Format

# My Rules

Rules for my AI assistant

## Be Concise

Keep responses short and to the point.

## Use Examples

Provide concrete examples when explaining concepts.

YAML

name: My Rules
description: Rules for my AI assistant
rules:
  - id: be_concise
    content: Keep responses short and to the point.
    priority: 10
    tags: [style]
  - id: use_examples
    content: Provide concrete examples when explaining concepts.
    priority: 5
    tags: [teaching]

Task Tracking

Cursor Rules can extract and manage tasks from project initialization documents:

from cursor_rules import extract_action_plan_from_doc, synchronize_with_cursorrules

# Extract tasks from a markdown project document
action_plan = extract_action_plan_from_doc("project_init.md")

# List tasks by phase
for phase in action_plan.phases:
    print(f"Phase: {phase.title}")
    for task in phase.tasks:
        print(f"- {task.title} [Status: {task.status.name}]")

# Update task status
task = action_plan.get_task_by_id("task_id")
if task:
    task.status = TaskStatus.COMPLETED

# Save the action plan to a file
action_plan.save_to_file("project_tasks.json")

# Load an action plan from a file
loaded_plan = ActionPlan.load_from_file("project_tasks.json")

# Sync with a ruleset
ruleset = RuleSet.load("project_rules.json")
synchronize_with_cursorrules(action_plan, ruleset)

Command Line Interface

You can also manage tasks from the command line:

# Generate an action plan from a markdown file
cursor-tasks generate project_init.md -o project_tasks.json

# List all tasks
cursor-tasks list -f project_tasks.json

# List tasks by phase
cursor-tasks list -f project_tasks.json -p "Backend Development"

# Update task status
cursor-tasks update -f project_tasks.json -t task_id -s completed

# Sync with a ruleset
cursor-tasks sync -f project_tasks.json -r project_rules.json

Document Generation

Cursor Rules can generate a complete set of project documentation from a single initialization document:

from cursor_rules import DocumentGenerator

# Create a document generator
generator = DocumentGenerator("project_init.md")

# Generate all documents
generated_files = generator.generate_all_documents()

# Print the paths to generated files
for doc_name, file_path in generated_files.items():
    print(f"{doc_name}: {file_path}")

The generated documentation includes:

• Product Requirements Document: Contains project vision, target users, user stories, and requirements
• Technical Stack Document: Details the technology stack, architecture, and development environment
• .cursorrules file: Provides project-specific rules for Cursor IDE
• Action Items: Tasks and subtasks organized by phase (in JSON and Markdown formats)

Command Line Interface

You can also generate documents from the command line:

# Generate all documentation from an initialization document
cursor-rules documents project_init.md

This creates:

• A .cursor directory at the root with the .cursorrules file
• A documentation directory with all project documents (PRD, Technical Stack, Tasks)

License

MIT

Examples

The package includes several example scripts to demonstrate its functionality:

# Complete workflow example demonstrating all features
python examples/complete_workflow_example.py

# Document generation from initialization document
python examples/document_generation_example.py

# Task manager example
python examples/task_manager_example.py

# Rule generation example
python examples/rule_generation_example.py

These examples demonstrate key features like:

• Generating .cursorrules files from markdown documents
• Creating complete documentation suites (PRD, Technical Stack, Tasks)
• Extracting and managing tasks
• Versioning .cursorrules files
• Monitoring codebase changes
• Working with LLM providers

API Key Configuration

For features that use LLM integration, you need to configure your API keys:

# Set up OpenAI API key
cursor-rules llm config --provider openai --api-key your_key_here

# Set up Anthropic API key
cursor-rules llm config --provider anthropic --api-key your_key_here

# List configured providers
cursor-rules llm list

# Test your configuration
cursor-rules llm test

You can also use environment variables:

# Linux/macOS
export OPENAI_API_KEY=your_key_here
export ANTHROPIC_API_KEY=your_key_here

# Windows PowerShell
$env:OPENAI_API_KEY = "your_key_here"
$env:ANTHROPIC_API_KEY = "your_key_here"

For detailed instructions, see the API Key Setup Guide.