{
  "timestamp": "2025-10-17T01:54:21+09:00",
  "session_id": "abc123def456",
  "task_type": "typo_fix",
  "complexity": "light",
  "workflow_id": "progressive_v3_layer2",
  "layers_used": [0, 1, 2],
  "tokens_used": 650,
  "time_ms": 1800,
  "files_read": 1,
  "mindbase_used": false,
  "sub_agents": [],
  "success": true,
  "user_feedback": "satisfied",
  "notes": "Optional implementation notes"
}

ultra_light:
  keywords: ["進捗", "状況", "進み", "where", "status", "progress"]
  token_budget: "100-500"
  layers: [0, 1]

light:
  keywords: ["誤字", "typo", "fix typo", "correct", "comment"]
  token_budget: "500-2K"
  layers: [0, 1, 2]

medium:
  keywords: ["バグ", "bug", "fix", "修正", "error", "issue"]
  token_budget: "2-5K"
  layers: [0, 1, 2, 3]

heavy:
  keywords: ["新機能", "new feature", "implement", "実装"]
  token_budget: "5-20K"
  layers: [0, 1, 2, 3, 4]

ultra_heavy:
  keywords: ["再設計", "redesign", "overhaul", "migration"]
  token_budget: "20K+"
  layers: [0, 1, 2, 3, 4, 5]

session_id = generate_session_id()
workflow_metrics = {
    "timestamp": get_current_time(),
    "session_id": session_id,
    "workflow_id": "progressive_v3_layer0"
}
# Bootstrap: 150 tokens

workflow_metrics.update({
    "task_type": classify_task_type(user_request),
    "complexity": classify_complexity(user_request),
    "estimated_token_budget": get_budget(complexity)
})

workflow_metrics.update({
    "layers_used": [0, 1, 2],  # Actual layers executed
    "tokens_used": calculate_tokens(),
    "files_read": len(files_loaded)
})

workflow_metrics.update({
    "success": task_completed_successfully,
    "time_ms": execution_time_ms,
    "user_feedback": infer_user_satisfaction()
})

# Append to workflow_metrics.jsonl
with open("docs/memory/workflow_metrics.jsonl", "a") as f:
    f.write(json.dumps(workflow_metrics) + "\n")

# Group by task type and calculate averages
python scripts/analyze_workflow_metrics.py --period week

# Output:
# Task Type: typo_fix
#   Count: 12
#   Avg Tokens: 680
#   Avg Time: 1,850ms
#   Success Rate: 100%

# Compare workflow variants
python scripts/ab_test_workflows.py \
  --variant-a progressive_v3_layer2 \
  --variant-b experimental_eager_layer3 \
  --metric tokens_used

# Output:
# Variant A (progressive_v3_layer2):
#   Avg Tokens: 1,250
#   Success Rate: 95%
#
# Variant B (experimental_eager_layer3):
#   Avg Tokens: 2,100
#   Success Rate: 98%
#
# Statistical Significance: p = 0.03 (significant)
# Recommendation: Keep Variant A (better efficiency)

every_monday_morning:
  1. Run analysis: python scripts/analyze_workflow_metrics.py --period week
  2. Identify patterns:
     - Best-performing workflows per task type
     - Inefficient patterns (high tokens, low success)
     - User satisfaction trends
  3. Update recommendations:
     - Promote efficient workflows to standard
     - Deprecate inefficient workflows
     - Design new experimental variants

allocation_strategy:
  current_best: 80%  # Use best-known workflow
  experimental: 20%  # Test new variant

evaluation_criteria:
  minimum_trials: 20  # Per variant
  confidence_level: 0.95  # p < 0.05
  metrics:
    - tokens_used (primary)
    - success_rate (gate: must be ≥95%)
    - user_feedback (qualitative)

promotion_rules:
  if experimental_better:
    - Statistical significance confirmed
    - Success rate ≥ current_best
    - User feedback ≥ neutral
    → Promote to standard (80% allocation)

  if experimental_worse:
    → Deprecate variant
    → Document learning in docs/patterns/

monthly_cleanup:
  1. Identify stale workflows:
     - No usage in last 90 days
     - Success rate <80%
     - User feedback consistently negative

  2. Archive deprecated workflows:
     - Move to docs/patterns/deprecated/
     - Document why deprecated

  3. Promote new standards:
     - Experimental → Standard (if proven better)
     - Update pm.md with new best practices

  4. Generate monthly report:
     - Token efficiency trends
     - Success rate improvements
     - User satisfaction evolution

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_json("docs/memory/workflow_metrics.jsonl", lines=True)
df['date'] = pd.to_datetime(df['timestamp']).dt.date

daily_avg = df.groupby('date')['tokens_used'].mean()
plt.plot(daily_avg)
plt.title("Average Token Usage Over Time")
plt.ylabel("Tokens")
plt.xlabel("Date")
plt.show()

task_counts = df['task_type'].value_counts()
plt.pie(task_counts, labels=task_counts.index, autopct='%1.1f%%')
plt.title("Task Type Distribution")
plt.show()

workflow_efficiency = df.groupby('workflow_id').agg({
    'tokens_used': 'mean',
    'success': 'mean',
    'time_ms': 'mean'
})
print(workflow_efficiency.sort_values('tokens_used'))

token_efficiency:
  ultra_light: 750-1,050 tokens (63% reduction)
  light: 1,250 tokens (46% reduction)
  medium: 3,850 tokens (47% reduction)
  heavy: 10,350 tokens (40% reduction)

success_rates:
  all_tasks: ≥95%
  ultra_light: 100% (simple tasks)
  light: 98%
  medium: 95%
  heavy: 92%

user_satisfaction:
  satisfied: ≥70%
  neutral: ≤25%
  unsatisfied: ≤5%

warning_signs:
  - success_rate < 85% for any task type
  - tokens_used > estimated_budget by >30%
  - time_ms > 10 seconds for light tasks
  - user_feedback "unsatisfied" > 10%
  - error_recurrence > 15%

# Archive old metrics (monthly)
mv docs/memory/workflow_metrics.jsonl \
   docs/memory/archive/workflow_metrics_2025-10.jsonl

# Start fresh
touch docs/memory/workflow_metrics.jsonl

# Remove metrics older than 6 months
find docs/memory/archive/ -name "workflow_metrics_*.jsonl" \
  -mtime +180 -delete