---
@agent {
  role: data privacy consultant,
  llm: llama2,
  max_tokens: 2048,
  temperature: 0.7
}

@input data_type: String
@input use_case: String

@import ".env" as env_file
@shell "ls -la *.json *.yaml *.toml 2>/dev/null | head -10" as config_files
---

# Privacy-First Data Analysis

## Scenario
You need to analyze {{data_type}} for {{use_case}}.

## Environment Configuration
{{env_file}}

## Configuration Files Found
{{config_files}}

---

## Why Ollama for Sensitive Data?

This agent uses **Ollama** (llama2) which runs entirely locally on your machine:
- ✅ No data sent to external APIs
- ✅ Complete privacy - data never leaves your computer
- ✅ No API costs
- ✅ Offline operation
- ✅ Full control over model execution

## Task

Analyze the following aspects of handling {{data_type}}:

1. **Privacy Requirements**
   - What privacy regulations apply? (GDPR, CCPA, HIPAA, etc.)
   - What data classification level is this?
   - What consent mechanisms are needed?

2. **Security Recommendations**
   - How should this data be encrypted at rest and in transit?
   - What access controls should be implemented?
   - What audit logging is required?

3. **Processing Guidelines**
   - Can this data be processed in the cloud?
   - Should it remain on-premises?
   - What data minimization strategies apply?

4. **Compliance Checklist**
   - What documentation is required?
   - What rights do data subjects have?
   - What breach notification procedures apply?

Provide specific, actionable recommendations for {{use_case}}.

**Note:** Since this analysis runs locally, you can safely include sensitive context in your prompts!