Cost Tracking Framework
When This Activates
This skill activates when:
- User asks about API costs or spending
- Concerns about expensive operations
- Need to optimize token usage
Token Cost Reference
Claude Pricing (Approximate)
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| Opus | ~$15 | ~$75 |
| Sonnet | ~$3 | ~$15 |
| Haiku | ~$0.25 | ~$1.25 |
Typical Operation Costs
| Operation | Tokens | Approximate Cost |
|---|---|---|
| Simple question | 500-2K | $0.01-0.05 |
| File read + analysis | 2-10K | $0.05-0.25 |
| Code generation | 5-20K | $0.15-0.50 |
| Multi-file refactor | 20-100K | $0.50-2.50 |
| Long conversation | 50-200K | $1.00-5.00 |
Cost Optimization Strategies
1. Route to Local LLM (FREE)
Use local_ask for simple tasks:
# FREE - no API cost
local_ask question="where is the login function?"
local_ask question="explain this error" mode=explain
local_review file="src/auth.ts" focus=bugsGood for local:
- Simple lookups ("where is X?")
- Code explanations
- Commit message generation
- Quick code reviews
2. Use Memory Tools First
Pre-indexed memory is instant and free:
# Instant, no API cost
memory_query "authentication flow"
memory_functions name="handleLogin"
smart_read path="src/auth.ts" detail=summary3. Reduce Context Size
- Use
smart_readwithdetail=summarybeforedetail=full - Truncate large files to relevant sections
- Clear conversation when changing topics
4. Batch Related Questions
Instead of 5 separate messages, combine:
"Can you: 1) explain the auth flow, 2) find the login
component, 3) check for security issues, and 4) suggest
improvements?"Gateway Metrics
Check current efficiency:
gateway_metrics format=summaryReturns:
- Cache hit rate
- Token savings
- Routing breakdown (local vs API)
Cost Estimation
Before expensive operations:
This refactor will touch ~20 files.
Estimated cost: $0.50-1.00
Proceed? [Y/n]Budget Awareness
Daily Patterns
- Morning: Fresh context, lower cost
- Long sessions: Context grows, higher cost
- After compaction: Reset context, lower cost
High-Cost Triggers
- "Analyze entire codebase"
- "Review all files in directory"
- "Generate comprehensive documentation"
- Very long conversations (>50 turns)
Saving Tips
- Start fresh for new topics - Don't carry irrelevant context
- Use subagents - They have focused context
- Check memory first - Summaries save full file reads
- Compress transcripts - Archived sessions are compressed
- Local for simple tasks - Ollama is free
Monitoring Commands
# Check gateway efficiency
python3 ~/.claude-dash/learning/efficiency_tracker.py --report
# View session sizes
du -sh ~/.claude-dash/sessions/*