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    cass-memory

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    SKILL.md

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    About

    Contextual learning system that remembers patterns and rules from past sessions. Use to get relevant context before tasks, record outcomes, and build a personal playbook of coding patterns.

    SKILL.md

    CASS Memory - Contextual Learning System

    Build and use a personal playbook of coding patterns learned from your sessions.

    Prerequisites

    The cm CLI should be available (part of cass-memory system).

    Initialize:

    cm init
    # Or with a starter playbook
    cm init --starter typescript
    cm init --starter react
    cm init --starter python
    cm init --starter go
    

    CLI Reference

    Get Context for a Task

    # THE main command - get relevant rules before starting work
    cm context "Description of your task" --json
    

    This returns:

    • Relevant rules from your playbook
    • Anti-patterns to avoid
    • History snippets from similar past work

    Reflection (Extract Patterns)

    # Run reflection on recent sessions
    cm reflect --json
    
    # Specify lookback period
    cm reflect --days 7 --json
    cm reflect --days 30 --json
    

    Playbook Management

    # List all rules
    cm playbook list --json
    
    # Get specific rule details
    cm playbook get b-8f3a2c --json
    
    # Add a new rule
    cm playbook add "Always use optional chaining for nested object access" --json
    

    Feedback on Rules

    # Mark rule as helpful
    cm mark b-8f3a2c --helpful --json
    cm mark b-8f3a2c --helpful --reason "Prevented null error" --json
    
    # Mark rule as harmful
    cm mark b-8f3a2c --harmful --json
    cm mark b-8f3a2c --harmful --reason "Caused false positive" --json
    

    Record Session Outcomes

    # Record success
    cm outcome --status success --json
    cm outcome --status success --rules "b-8f3a2c,b-4d2e1f" --json
    
    # Record failure
    cm outcome --status failure --text "Build failed due to type error" --json
    
    # Mixed results
    cm outcome --status mixed --text "Partial completion" --json
    

    Statistics

    # Get playbook stats
    cm stats --json
    

    Top Rules

    # Show most effective rules
    cm top --json
    cm top 5 --json
    cm top 20 --json
    

    Health Check

    # Check system health
    cm doctor --json
    
    # Auto-fix issues
    cm doctor --fix --json
    

    Find Stale Rules

    # Rules without recent feedback
    cm stale --json
    cm stale --days 30 --json
    cm stale --days 60 --json
    

    Validate Rules

    # Validate a proposed rule against history
    cm validate "Proposed rule text" --json
    

    Explain Rule Origin

    # Show evidence and reasoning for a rule
    cm why b-8f3a2c --json
    

    Usage Statistics

    cm usage --json
    

    Starter Playbooks

    # List available starters
    cm starters --json
    

    Workflow Patterns

    Session Start

    # Get context before starting a task
    cm context "Implement user authentication with JWT" --json
    

    During Work

    When a rule helps:

    cm mark b-8f3a2c --helpful --json
    

    When a rule leads astray:

    cm mark b-8f3a2c --harmful --reason "Not applicable to this framework" --json
    

    Session End

    # Record outcome
    cm outcome --status success --rules "b-8f3a2c,b-4d2e1f" --json
    

    Periodic Maintenance

    # Weekly: Run reflection to extract new patterns
    cm reflect --days 7 --json
    
    # Monthly: Review stale rules
    cm stale --days 30 --json
    
    # Check system health
    cm doctor --json
    

    Building Your Playbook

    # Manually add a pattern you've learned
    cm playbook add "Use React.memo() for components receiving complex objects as props" --json
    
    # After adding, use it in context queries
    cm context "Create a list component with filtering" --json
    

    Rule Lifecycle

    1. Creation - Rules emerge from reflection or manual addition
    2. Usage - Rules surface in context queries
    3. Feedback - Mark as helpful/harmful based on experience
    4. Evolution - High-feedback rules rise, low-feedback rules become stale
    5. Retirement - Stale rules get reviewed and pruned

    Best Practices

    1. Always get context first - Run cm context "task" before starting work
    2. Provide feedback - Mark rules as helpful/harmful
    3. Record outcomes - Track session success/failure
    4. Run reflection regularly - Weekly reflection extracts new patterns
    5. Review stale rules - Don't let old rules accumulate
    6. Add rules manually - When you learn something important

    Integration Tips

    Pre-Task Context

    Before any significant coding task:

    CONTEXT=$(cm context "Your task description" --json)
    # Use context to inform your approach
    

    Post-Session Recording

    At end of coding session:

    cm outcome --status success --text "Completed feature X" --json
    
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    johnlindquist/claude
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