# Plan-Linter Implementation

This document summarizes the implementation of the Plan-Linter tool based on the requirements in the README.

## Architecture

The Plan-Linter has been implemented with the following components:

1. **Core Functionality**
   - `types.py`: Type definitions using Pydantic models
   - `core.py`: Rule enforcement and validation logic
   - `loader.py`: Loading and parsing schemas, plans, and policies

2. **Rules**
   - `rules/deny_sql_write.py`: Rule to prevent SQL write operations
   - `rules/no_raw_secret.py`: Rule to detect secrets in plans

3. **Reporters**
   - `reporters/cli.py`: CLI reporter using Rich for formatted output
   - `reporters/json.py`: JSON reporter for machine-readable output

4. **Command Line Interface**
   - `cli.py`: Command-line interface using Typer
   - `__main__.py`: Entry point for the package

5. **Examples**
   - `examples/price_drop.json`: Example plan with issues
   - `examples/policy.yaml`: Example policy file

6. **Tests**
   - `tests/test_core.py`: Tests for core validation logic
   - `tests/test_cli.py`: Tests for CLI functionality

## Features Implemented

- ✅ Schema validation of plan JSON
- ✅ Policy rules for tool controls
- ✅ Bounds checking for numeric parameters
- ✅ Secret detection in plan steps
- ✅ Loop detection in step dependencies
- ✅ Risk scoring based on detected issues
- ✅ Plugin rule system
- ✅ CLI and JSON output formats

## Usage

The tool can be used as follows:

```bash
# Basic usage
plan-lint path/to/plan.json

# With policy file
plan-lint path/to/plan.json --policy path/to/policy.yaml

# Output formats
plan-lint path/to/plan.json --format json
plan-lint path/to/plan.json --output results.json

# Custom risk threshold
plan-lint path/to/plan.json --fail-risk 0.7
```

## Design Decisions

1. **Pydantic for Type Safety**: We used Pydantic models for robust validation and type safety.

2. **Plugin Architecture**: The rules are implemented as separate modules that are dynamically loaded, allowing for easy extension.

3. **Rich for CLI Output**: We used the Rich library for attractive and helpful console output.

4. **Risk Scoring**: A flexible risk scoring system allows different weights for different types of issues.

5. **Modular Reporters**: The reporting system is modular, making it easy to add new output formats.

## Next Steps

1. **More Rules**: Add more predefined rule modules for common security concerns.

2. **Continuous Integration**: Provide better examples of CI integration.

3. **Documentation**: Expand documentation with more usage examples and rule creation guides.

4. **Testing**: Expand test coverage, especially for edge cases.

5. **Rule Discovery**: Implement more robust rule discovery via entry points. 