cass-memory (cm)

Cross-agent learning system. Extracts patterns, rules, and anti-patterns from past sessions.

When This Applies

Primary Command

Before starting any non-trivial task:

cm context "your task description" --json

This returns:

• •Relevant rules from the playbook
• •Historical context from past sessions
• •Anti-patterns to avoid
• •Suggested searches for deeper investigation

Example Usage

# Before implementing authentication
cm context "implement OAuth login" --json

# Before database work
cm context "add new database migration" --json

# Before refactoring
cm context "refactor the payment module" --json

What You Get Back

{
  "rules": ["Always use parameterized queries", "..."],
  "context": ["Previous auth implementation used JWT...", "..."],
  "anti_patterns": ["Don't store tokens in localStorage", "..."],
  "suggested_searches": ["cass search 'OAuth implementation' --robot"]
}

Health Check

cm doctor

How It Works

The system learns automatically from your sessions:

1. •Sessions are indexed by CASS
2. •cm extracts patterns, rules, and anti-patterns
3. •cm context retrieves relevant learned knowledge

You don't need to:

• •Manually run reflection/learning steps
• •Manually add rules to the playbook
• •Worry about the learning pipeline

When to Use cm vs CASS

Quick Reference

cm context "task description" --json   # Get context before task
cm doctor                              # Health check

Best Practices

See Also

• •cass-search/ — Raw session search with CASS
• •project-memory/ — Session context retrieval skill