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autonomous-cost-optimizer

Token and cost optimization for autonomous coding. Use when tracking token usage, optimizing API costs, managing budgets, or improving efficiency.

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The canonical home for this skill is autonomous-cost-optimizer in fernandezbaptiste/Skrillz

SKILL.md
Quality
Evals
Security

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

ModelInput (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

StrategySavingsTrade-off
Context compaction20-40%Slight context loss
Response caching30-50%Storage needed
Batch operations15-25%Higher latency
Model selection50-90%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 calculations
  • references/OPTIMIZATION-STRATEGIES.md - Strategies

Scripts

  • scripts/cost_optimizer.py - Core optimizer
  • scripts/usage_tracker.py - Track token usage
  • scripts/budget_manager.py - Budget enforcement
  • scripts/efficiency_analyzer.py - Analyze efficiency
Repository
fernandezbaptiste/Skrillz
Path

.claude/skills/autonomous-cost-optimizer/SKILL.md

Commit

93ed392

Last updated
First committed

Canonical home

fernandezbaptiste/Skrillz
In sync

since Sep 12, 2026

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