adaptationio/autonomous-cost-optimizer
.claude/skills/autonomous-cost-optimizer/SKILL.md
Token and cost optimization for autonomous coding. Use when tracking token usage, optimizing API costs, managing budgets, or improving efficiency.
$ install --global
skillsauthnpx skillsauth add adaptationio/skrillz autonomous-cost-optimizerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Mar 30, 2026, 11:24 PM55.6s3 files scanned
SKILL.md
- name:
- autonomous-cost-optimizer
- description:
- Token and cost optimization for autonomous coding. Use when tracking token usage, optimizing API costs, managing budgets, or improving efficiency.
- version:
- 1.0.0
- category:
- autonomous-coding
- layer:
- orchestration
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
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