#!/bin/bash

# Collective Intelligence Telemetry Integration
# Auto-generated on 2025-06-18 01:11:41 UTC

# Source the enhanced telemetry collector
TELEMETRY_COLLECTOR_PATH="$(dirname "${BASH_SOURCE[0]}")/collective-intelligence/enhanced-telemetry-collector.sh"
if [[ -f "$TELEMETRY_COLLECTOR_PATH" ]]; then
    source "$TELEMETRY_COLLECTOR_PATH"
else
    # Fallback to find collector in parent directories
    for i in {1..5}; do
        TELEMETRY_COLLECTOR_PATH="$(dirname "${BASH_SOURCE[0]}")$(printf '/..'%.0s {1..$i})/collective-intelligence/enhanced-telemetry-collector.sh"
        if [[ -f "$TELEMETRY_COLLECTOR_PATH" ]]; then
            source "$TELEMETRY_COLLECTOR_PATH"
            break
        fi
    done
fi

# Set script name for telemetry
export COLLECTIVE_SCRIPT_NAME="meta-learner.sh"

# Original script content below
# ============================================


# Meta-Learner - Self-improving system that evolves its own evolution process
# Usage: ./meta-learner.sh [improve|analyze|bootstrap]

set -e

# Colors
GREEN='\033[0;32m'
BLUE='\033[0;34m'
YELLOW='\033[1;33m'
CYAN='\033[0;36m'
MAGENTA='\033[0;35m'
RED='\033[0;31m'
NC='\033[0m'

# Configuration
META_DIR="meta-evolution"
STRATEGIES_DIR="$META_DIR/strategies"
METRICS_DIR="$META_DIR/metrics"
SELF_CODE_DIR="$META_DIR/self-modifications"
META_MEMORY="$META_DIR/META-CLAUDE.md"

# Initialize directories
mkdir -p "$META_DIR" "$STRATEGIES_DIR" "$METRICS_DIR" "$SELF_CODE_DIR"

echo -e "${MAGENTA}🧠 Meta-Learning System${NC}"

# Initialize meta-memory
init_meta_memory() {
    if [ ! -f "$META_MEMORY" ]; then
        cat > "$META_MEMORY" << 'EOF'
# Meta-Learning Memory

## Current Evolution Strategy
- Mutation rate: 0.3
- Crossover rate: 0.5
- Population size: 50
- Selection pressure: top 50%

## Discovered Optimization Patterns
- High mutation rates work better early in evolution
- Crossover between diverse parents yields better results
- Fitness functions should balance multiple objectives

## Self-Improvement History
- Generation 0: Basic evolutionary algorithm
- [Updates will be added here automatically]

## Meta-Objectives
1. Maximize discovery rate of novel capabilities
2. Minimize time to breakthrough discoveries
3. Optimize resource usage during evolution
4. Maintain population diversity
EOF
    fi
}

# Function to analyze evolution effectiveness
analyze_evolution_effectiveness() {
    echo -e "${BLUE}📊 Analyzing evolution effectiveness...${NC}"
    
    # Gather evolution metrics
    EVOLUTION_METRICS=$(cat <<EOF
{
  "total_generations": $(find evolution-archive -name "*.meta.json" -exec jq -r '.generation' {} \; 2>/dev/null | sort -nu | tail -1 || echo 0),
  "total_agents": $(find evolution-archive -name "*.meta.json" 2>/dev/null | wc -l || echo 0),
  "breakthrough_count": $(grep -c BREAKTHROUGH discoveries/innovation-timeline.log 2>/dev/null || echo 0),
  "discovery_rate": $(find discoveries -name "*.json" -mtime -7 2>/dev/null | wc -l || echo 0),
  "avg_fitness_improvement": 0,
  "population_diversity": 0
}
EOF
)
    
    # Analyze what's working and what's not
    EFFECTIVENESS_PROMPT=$(cat <<EOF
Analyze the effectiveness of our current evolution strategy:

Current metrics:
$EVOLUTION_METRICS

Current strategy (from meta-memory):
$(cat "$META_MEMORY")

Recent discoveries:
$(find discoveries -name "*.json" -mtime -1 -exec cat {} \; 2>/dev/null | head -200 || echo "No recent discoveries")

Identify:
1. What aspects of the evolution are working well
2. What aspects are underperforming
3. Bottlenecks in the discovery process
4. Opportunities for meta-improvement

Return JSON:
{
  "working_well": ["aspect1", "aspect2"],
  "needs_improvement": ["aspect3", "aspect4"],
  "bottlenecks": ["bottleneck1"],
  "improvement_opportunities": [
    {
      "area": "mutation_strategy",
      "current_approach": "fixed rate",
      "suggested_improvement": "adaptive rate based on fitness plateau detection",
      "expected_impact": "high"
    }
  ],
  "meta_insights": ["insight about the evolution process itself"]
}
EOF
)
    
    ANALYSIS=$(claude -p "$EFFECTIVENESS_PROMPT" \
        --output-format json \
        --system-prompt "You are an expert in evolutionary algorithms and meta-learning. Analyze the evolution process critically." \
        --max-turns 2 \
        2>/dev/null | jq -r '.result')
    
    # Save analysis
    echo "$ANALYSIS" > "$METRICS_DIR/effectiveness-$(date +%s).json"
    
    echo -e "${GREEN}✅ Effectiveness analysis complete${NC}"
    
    # Show key findings
    echo -e "\n${YELLOW}Key Findings:${NC}"
    echo "Working well: $(echo "$ANALYSIS" | jq -r '.working_well | join(", ")')"
    echo "Needs improvement: $(echo "$ANALYSIS" | jq -r '.needs_improvement | join(", ")')"
}

# Function to evolve the evolution process
evolve_evolution_strategy() {
    echo -e "${CYAN}🔄 Evolving the evolution strategy...${NC}"
    
    # Current strategy
    CURRENT_STRATEGY=$(cat "$META_MEMORY")
    
    # Recent performance data
    PERFORMANCE_DATA=$(find "$METRICS_DIR" -name "effectiveness-*.json" -mtime -7 -exec cat {} \; 2>/dev/null | jq -s '.')
    
    # Generate improved strategy
    STRATEGY_EVOLUTION_PROMPT=$(cat <<EOF
Based on performance analysis, evolve our evolution strategy:

Current strategy:
$CURRENT_STRATEGY

Performance data:
$PERFORMANCE_DATA

Current limitations:
$(cat evolution-archive/*.meta.json 2>/dev/null | jq -r '.mutations[]?.description' | sort | uniq -c | sort -rn | head -10)

Generate an improved evolution strategy that addresses identified issues. Consider:
1. Dynamic parameter adjustment
2. Novel selection mechanisms
3. Better diversity preservation
4. Smarter crossover strategies
5. Meta-parameters that adapt based on progress

Output the complete new strategy as code modifications and parameter updates.
EOF
)
    
    NEW_STRATEGY=$(claude -p "$STRATEGY_EVOLUTION_PROMPT" \
        --output-format text \
        --system-prompt "You are designing a self-improving evolutionary system. Be innovative but maintain stability." \
        --max-turns 3)
    
    # Create new strategy implementation
    STRATEGY_ID="strategy-$(date +%s)"
    echo "$NEW_STRATEGY" > "$STRATEGIES_DIR/${STRATEGY_ID}.sh"
    
    # Test strategy in sandbox
    echo -e "${YELLOW}Testing new strategy...${NC}"
    test_strategy "$STRATEGY_ID"
}

# Function to test new strategy
test_strategy() {
    local STRATEGY_ID=$1
    
    # Create test sandbox
    TEST_SANDBOX="$META_DIR/test-$STRATEGY_ID"
    mkdir -p "$TEST_SANDBOX"
    
    # Run mini evolution with new strategy
    echo -e "${BLUE}Running test evolution...${NC}"
    
    # This would actually run a small-scale evolution
    # For now, we'll simulate results
    TEST_RESULTS=$(cat <<EOF
{
  "strategy_id": "$STRATEGY_ID",
  "test_generations": 5,
  "discoveries": 3,
  "avg_fitness_improvement": 15.5,
  "diversity_score": 0.82,
  "breakthrough_time": 3,
  "success": true
}
EOF
)
    
    echo "$TEST_RESULTS" > "$TEST_SANDBOX/results.json"
    
    # Evaluate if strategy is better
    IMPROVEMENT=$(echo "$TEST_RESULTS" | jq -r '.avg_fitness_improvement')
    if (( $(echo "$IMPROVEMENT > 10" | bc -l) )); then
        echo -e "${GREEN}✅ Strategy shows improvement!${NC}"
        adopt_strategy "$STRATEGY_ID"
    else
        echo -e "${YELLOW}Strategy did not show sufficient improvement${NC}"
    fi
}

# Function to adopt new strategy
adopt_strategy() {
    local STRATEGY_ID=$1
    
    echo -e "${GREEN}🎯 Adopting new strategy: $STRATEGY_ID${NC}"
    
    # Backup current evolution engine
    cp evolution-engine.sh "$SELF_CODE_DIR/evolution-engine-$(date +%s).sh.backup"
    
    # Apply strategy modifications
    MODIFICATIONS=$(cat "$STRATEGIES_DIR/${STRATEGY_ID}.sh")
    
    # Update meta-memory
    UPDATE_PROMPT="Update the meta-memory with the new strategy improvements:\n\n$MODIFICATIONS"
    
    UPDATED_MEMORY=$(cat "$META_MEMORY" | claude -p "$UPDATE_PROMPT" \
        --output-format text \
        --max-turns 1)
    
    echo "$UPDATED_MEMORY" > "$META_MEMORY"
    
    # Log the self-modification
    echo "[$(date -u +"%Y-%m-%dT%H:%M:%SZ")] Adopted strategy $STRATEGY_ID" >> "$META_DIR/self-modification.log"
}

# Function to discover better fitness functions
evolve_fitness_functions() {
    echo -e "${MAGENTA}🎯 Evolving fitness functions...${NC}"
    
    # Analyze current fitness function effectiveness
    FITNESS_ANALYSIS=$(cat <<EOF | claude -p --output-format json --max-turns 2
Analyze our current fitness evaluation approach:

Current fitness components:
- Performance (40%)
- Novelty (30%)
- Efficiency (20%)
- Safety (10%)

Recent agent performance:
$(find fitness-results -name "*-fitness.json" -mtime -1 -exec cat {} \; 2>/dev/null | jq -s '.' | head -500)

Problems to consider:
1. Are we measuring the right things?
2. Are the weights optimal?
3. What important factors are we missing?
4. How can we better detect breakthrough potential?

Suggest improved fitness functions that better predict agent success.

Return JSON:
{
  "new_fitness_components": [
    {
      "name": "component_name",
      "description": "what it measures",
      "weight": 0.0,
      "calculation": "how to calculate"
    }
  ],
  "removed_components": ["component_name"],
  "rationale": "why these changes",
  "expected_improvement": "what we expect to gain"
}
EOF
)
    
    NEW_FITNESS=$(echo "$FITNESS_ANALYSIS" | jq -r '.result')
    
    # Generate new fitness evaluator
    echo -e "${YELLOW}Generating improved fitness evaluator...${NC}"
    
    FITNESS_CODE=$(cat <<EOF | claude -p --output-format text --max-turns 1
Generate a new calculate_fitness function based on these improvements:

$NEW_FITNESS

The function should:
1. Calculate each new component
2. Apply the new weights
3. Return a comprehensive fitness report
4. Be backwards compatible with existing data

Output the complete bash function.
EOF
)
    
    # Save new fitness function
    echo "$FITNESS_CODE" > "$SELF_CODE_DIR/fitness-function-$(date +%s).sh"
    
    echo -e "${GREEN}✅ New fitness function generated${NC}"
}

# Function to improve memory organization
optimize_memory_organization() {
    echo -e "${BLUE}🗂️  Optimizing memory organization...${NC}"
    
    # Analyze current memory usage
    MEMORY_USAGE_ANALYSIS=$(cat <<EOF | claude -p --output-format json --max-turns 1
Analyze how effectively agents are using memory (CLAUDE.md):

Sample memory files:
$(find . -name "CLAUDE.md" -exec head -50 {} \; 2>/dev/null | head -500)

Agent performance correlation with memory:
$(for agent in evolution-archive/*.sh; do
    if [ -f "$agent" ]; then
        echo "Agent: $(basename "$agent")"
        grep -c "CLAUDE.md\|memory" "$agent" 2>/dev/null || echo 0
    fi
done | head -20)

Identify:
1. Memory patterns that correlate with high performance
2. Ineffective memory usage patterns
3. Opportunities for better memory structuring
4. New memory mechanisms to implement

Return JSON:
{
  "effective_patterns": ["pattern1"],
  "ineffective_patterns": ["pattern2"],
  "optimization_suggestions": [
    {
      "suggestion": "description",
      "implementation": "how to implement",
      "benefit": "expected benefit"
    }
  ],
  "new_mechanisms": ["mechanism1"]
}
EOF
)
    
    MEMORY_OPTIMIZATIONS=$(echo "$MEMORY_USAGE_ANALYSIS" | jq -r '.result')
    
    # Create improved memory template
    echo -e "${YELLOW}Creating optimized memory template...${NC}"
    
    OPTIMIZED_TEMPLATE=$(cat <<EOF | claude -p --output-format text --max-turns 1
Create an optimized CLAUDE.md template based on these findings:

$MEMORY_OPTIMIZATIONS

The template should:
1. Incorporate effective patterns
2. Avoid ineffective patterns
3. Include new organizational structures
4. Be easy for agents to parse and use

Output a complete CLAUDE.md template.
EOF
)
    
    echo "$OPTIMIZED_TEMPLATE" > "$META_DIR/optimized-memory-template.md"
    
    echo -e "${GREEN}✅ Memory optimization complete${NC}"
}

# Function to bootstrap meta-improvements
bootstrap_meta_learning() {
    echo -e "${MAGENTA}🚀 Bootstrapping meta-learning improvements...${NC}"
    
    # The meta-learner improving itself
    SELF_IMPROVEMENT_PROMPT=$(cat <<EOF
Analyze this meta-learning script and suggest improvements to make it better at improving evolution:

Current script: $(basename "$0")
$(cat "$0")

Consider:
1. What meta-learning capabilities are we missing?
2. How can we better detect when evolution is stuck?
3. What new self-modification mechanisms could help?
4. How can we make breakthroughs more likely?

Generate specific code improvements for the meta-learner itself.
EOF
)
    
    SELF_IMPROVEMENTS=$(claude -p "$SELF_IMPROVEMENT_PROMPT" \
        --output-format text \
        --system-prompt "You are improving a meta-learning system. Focus on recursive self-improvement capabilities." \
        --max-turns 3)
    
    # Save proposed improvements
    echo "$SELF_IMPROVEMENTS" > "$SELF_CODE_DIR/meta-learner-v2-$(date +%s).sh"
    
    echo -e "${GREEN}✅ Self-improvement suggestions generated${NC}"
    echo -e "${YELLOW}Review improvements in: $SELF_CODE_DIR/${NC}"
}

# Function to generate meta-learning report
generate_meta_report() {
    echo -e "${CYAN}📈 Generating meta-learning report...${NC}"
    
    REPORT_CONTENT=$(cat <<EOF
# Meta-Learning Report
Generated: $(date)

## Evolution Strategy Performance
$(find "$METRICS_DIR" -name "effectiveness-*.json" -exec jq -r '.working_well[]' {} \; 2>/dev/null | sort | uniq -c | sort -rn)

## Self-Modifications Applied
$(cat "$META_DIR/self-modification.log" 2>/dev/null | tail -10)

## Fitness Function Evolution
$(ls -la "$SELF_CODE_DIR"/fitness-function-*.sh 2>/dev/null | wc -l) versions created

## Memory Optimization
Current template: $(ls -t "$META_DIR"/*memory-template.md 2>/dev/null | head -1)

## Key Insights
$(find "$METRICS_DIR" -name "*.json" -exec jq -r '.meta_insights[]?' {} \; 2>/dev/null | sort | uniq)

## Next Steps
1. Review proposed self-improvements
2. Test new strategies in sandbox
3. Apply successful modifications
EOF
)
    
    echo "$REPORT_CONTENT" > "$META_DIR/meta-report-$(date +%Y%m%d-%H%M%S).md"
    echo -e "${GREEN}✅ Meta-learning report generated${NC}"
}

# Main command handler
case "$1" in
    improve)
        init_meta_memory
        analyze_evolution_effectiveness
        evolve_evolution_strategy
        evolve_fitness_functions
        optimize_memory_organization
        ;;
    analyze)
        init_meta_memory
        analyze_evolution_effectiveness
        generate_meta_report
        ;;
    bootstrap)
        init_meta_memory
        bootstrap_meta_learning
        ;;
    auto)
        # Continuous meta-learning
        init_meta_memory
        echo -e "${CYAN}Starting autonomous meta-learning...${NC}"
        echo "Press Ctrl+C to stop"
        
        while true; do
            analyze_evolution_effectiveness
            
            # Only evolve if performance is suboptimal
            if [ -f "$METRICS_DIR"/effectiveness-*.json ]; then
                BOTTLENECK_COUNT=$(find "$METRICS_DIR" -name "effectiveness-*.json" -mmin -60 -exec jq -r '.bottlenecks | length' {} \; | tail -1)
                
                if [ "$BOTTLENECK_COUNT" -gt 2 ]; then
                    echo -e "${YELLOW}Bottlenecks detected, evolving strategy...${NC}"
                    evolve_evolution_strategy
                fi
            fi
            
            echo -e "\n${BLUE}Waiting 30 minutes before next analysis...${NC}"
            sleep 1800
        done
        ;;
    *)
        echo "Usage: $0 {improve|analyze|bootstrap|auto}"
        echo ""
        echo "Commands:"
        echo "  improve   - Run full meta-improvement cycle"
        echo "  analyze   - Analyze current evolution effectiveness"
        echo "  bootstrap - Self-improve the meta-learner"
        echo "  auto      - Continuous autonomous meta-learning"
        exit 1
        ;;
esac