Learn-skills.dev autonomous-cost-optimizer
Token and cost optimization for autonomous coding. Use when tracking token usage, optimizing API costs, managing budgets, or improving efficiency.
install
source · Clone the upstream repo
git clone https://github.com/NeverSight/learn-skills.dev
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/NeverSight/learn-skills.dev "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/skills-md/adaptationio/skrillz/autonomous-cost-optimizer" ~/.claude/skills/neversight-learn-skills-dev-autonomous-cost-optimizer && rm -rf "$T"
manifest:
data/skills-md/adaptationio/skrillz/autonomous-cost-optimizer/SKILL.mdsource content
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
- Cost calculationsreferences/PRICING-GUIDE.md
- Strategiesreferences/OPTIMIZATION-STRATEGIES.md
Scripts
- Core optimizerscripts/cost_optimizer.py
- Track token usagescripts/usage_tracker.py
- Budget enforcementscripts/budget_manager.py
- Analyze efficiencyscripts/efficiency_analyzer.py