autonomous-cost-optimizer
Token and cost optimization for autonomous coding. Use when tracking token usage, optimizing API costs, managing budgets, or improving efficiency.
SKILL.md
Full skill instructions
Autonomous Cost Optimizer
Tracks and optimizes token usage and API costs during autonomous coding.
Quick Start
Track Usage
from scripts.cost_optimizer import CostOptimizer
optimizer = CostOptimizer(project_dir)
optimizer.track_usage(input_tokens=1500, output_tokens=500)
report = optimizer.get_usage_report()
print(f"Total cost: ${report.total_cost:.4f}")
Check Budget
if optimizer.is_within_budget(budget=10.00):
# Continue working
pass
else:
# Trigger cost-saving measures
await optimizer.enter_efficiency_mode()
Cost Optimization Workflow
┌─────────────────────────────────────────────────────────────┐
│ COST OPTIMIZATION │
├─────────────────────────────────────────────────────────────┤
│ │
│ TRACK │
│ ├─ Monitor token usage per request │
│ ├─ Calculate cost per feature │
│ ├─ Track cumulative session cost │
│ └─ Log usage to history │
│ │
│ ANALYZE │
│ ├─ Identify high-cost operations │
│ ├─ Compare efficiency across features │
│ ├─ Detect wasteful patterns │
│ └─ Calculate ROI per feature │
│ │
│ OPTIMIZE │
│ ├─ Compact context when approaching limits │
│ ├─ Cache repeated queries │
│ ├─ Batch similar operations │
│ └─ Prioritize high-ROI features │
│ │
│ REPORT │
│ ├─ Generate cost breakdown │
│ ├─ Show efficiency metrics │
│ └─ Recommend optimizations │
│ │
└─────────────────────────────────────────────────────────────┘
Pricing Reference
| Model | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Claude 3.5 Sonnet | $3.00 | $15.00 |
| Claude 3 Opus | $15.00 | $75.00 |
| Claude 3 Haiku | $0.25 | $1.25 |
Efficiency Metrics
@dataclass
class EfficiencyMetrics:
tokens_per_feature: float
cost_per_feature: float
features_per_dollar: float
context_utilization: float
cache_hit_rate: float
Optimization Strategies
| Strategy | Savings | Trade-off |
|---|---|---|
| Context compaction | 20-40% | Slight context loss |
| Response caching | 30-50% | Storage needed |
| Batch operations | 15-25% | Higher latency |
| Model selection | 50-90% | Capability reduction |
Integration Points
- context-compactor: Reduce context size
- memory-manager: Cache common queries
- autonomous-loop: Budget enforcement
- progress-tracker: Efficiency metrics
References
references/PRICING-GUIDE.md- Cost calculationsreferences/OPTIMIZATION-STRATEGIES.md- Strategies
Scripts
scripts/cost_optimizer.py- Core optimizerscripts/usage_tracker.py- Track token usagescripts/budget_manager.py- Budget enforcementscripts/efficiency_analyzer.py- Analyze efficiency