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Track LLM API spend per session and task. Estimate token usage across providers. Warn before you blow your budget.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | cost-tracker |
| version | 1.0.0 |
| author | Polycat |
| tags | ["cost","tokens","budget","llm","monitoring","api"] |
| license | MIT |
| platform | universal |
| description | Track LLM API spend per session and task. Estimate token usage across providers. Warn before you blow your budget. |
Compatible with Claude Code, Codex CLI, Cursor, Windsurf, and any SKILL.md-compatible agent.
Track what your AI sessions actually cost. Estimate token usage, cumulative spend, and warn you before you hit budget thresholds — across OpenAI, Anthropic, Google, and other major providers.
Activate this skill when:
Use these rates to estimate costs. All prices are per 1M tokens (input / output).
| Model | Input | Output |
|---|---|---|
| claude-opus-4 | $15.00 | $75.00 |
| claude-sonnet-4 | $3.00 | $15.00 |
| claude-haiku-4 | $0.80 | $4.00 |
| claude-opus-3 | $15.00 | $75.00 |
| claude-sonnet-3.5 | $3.00 | $15.00 |
| claude-haiku-3.5 | $0.80 | $4.00 |
| Model | Input | Output |
|---|---|---|
| gpt-4o | $2.50 | $10.00 |
| gpt-4o-mini | $0.15 | $0.60 |
| gpt-4-turbo | $10.00 | $30.00 |
| gpt-4 | $30.00 | $60.00 |
| gpt-3.5-turbo | $0.50 | $1.50 |
| o1 | $15.00 | $60.00 |
| o1-mini | $3.00 | $12.00 |
| o3-mini | $1.10 | $4.40 |
| Model | Input | Output |
|---|---|---|
| gemini-2.0-flash | $0.075 | $0.30 |
| gemini-2.0-pro | $1.25 | $5.00 |
| gemini-1.5-pro | $1.25 | $5.00 |
| gemini-1.5-flash | $0.075 | $0.30 |
| Model | Input | Output |
|---|---|---|
| mistral-large | $3.00 | $9.00 |
| mistral-small | $0.20 | $0.60 |
| llama-3.3-70b (Groq) | $0.59 | $0.79 |
| deepseek-r1 | $0.55 | $2.19 |
⚠️ Prices change frequently. Always verify at the provider's pricing page before making financial decisions.
When activated, maintain a running cost ledger in the conversation context:
SESSION COST LEDGER
===================
Model: claude-sonnet-4
Started: [timestamp]
Turn | Input tok | Output tok | Cost
------|-----------|------------|------
1 | 2,340 | 450 | $0.0134
2 | 4,120 | 890 | $0.0259
3 | 1,870 | 340 | $0.0107
------|-----------|------------|------
Total | 8,330 | 1,680 | $0.0500
Budget: $2.00 | Used: $0.05 (2.5%) | Remaining: $1.95
When you can't read token counts directly from the API response, estimate:
Quick estimates (rough, for planning):
File size estimates:
wc -c then divide by 4Pre-task estimate command:
# Estimate tokens in a file
wc -c myfile.py | awk '{printf "~%d tokens\n", $1/4}'
# Estimate tokens in entire codebase
find . -name "*.py" -o -name "*.ts" -o -name "*.js" | xargs wc -c 2>/dev/null | tail -1 | awk '{printf "~%d tokens (input)\n", $1/4}'
# Count words as rough proxy
wc -w myfile.txt | awk '{printf "~%d tokens\n", $1*1.3}'
Issue warnings at these thresholds:
Before any task involving large files or long conversations, estimate upfront:
📊 PRE-TASK ESTIMATE
====================
Task: Refactor entire codebase
Files to read: 23 files (~180,000 chars)
Estimated input: ~45,000 tokens
Expected output: ~8,000 tokens (code changes + explanation)
Model: claude-sonnet-4
Estimated cost: $0.255
Input: 45,000 × $3.00/M = $0.135
Output: 8,000 × $15.00/M = $0.120
Proceed? This is ~13% of your $2.00 budget.
💰 This session: ~$0.05 (8,330 tokens in / 1,680 out) | Budget: $1.95 remaining
╔══════════════════════════════════════╗
║ SESSION COST REPORT ║
╠══════════════════════════════════════╣
║ Model: claude-sonnet-4 ║
║ Duration: 23 minutes ║
╠══════════════════════════════════════╣
║ INPUT TOKENS ║
║ Turns: 12 ║
║ Total tokens: 42,840 ║
║ Cost: $0.1285 ║
╠══════════════════════════════════════╣
║ OUTPUT TOKENS ║
║ Total tokens: 8,920 ║
║ Cost: $0.1338 ║
╠══════════════════════════════════════╣
║ TOTAL COST: $0.2623 ║
║ Budget used: 13.1% of $2.00 ║
║ Remaining: $1.74 ║
╚══════════════════════════════════════╝
If a session spans multiple models or providers:
MULTI-MODEL SESSION SUMMARY
============================
gpt-4o → 12,000 in / 2,400 out → $0.054
claude-haiku-4 → 45,000 in / 8,000 out → $0.068
gemini-flash → 8,000 in / 1,200 out → $0.001
────────────────────────────────────────────────
TOTAL → 65,000 in / 11,600 out → $0.123
Calculate the tokens in the most recent exchange, apply the current model's rates, and report inline.
find . -type f \( -name "*.py" -o -name "*.ts" -o -name "*.js" -o -name "*.md" \) \
| xargs wc -c 2>/dev/null | tail -1 \
| awk '{
tokens = $1/4
cost_sonnet = (tokens/1000000) * 3.00
cost_haiku = (tokens/1000000) * 0.80
cost_gpt4o = (tokens/1000000) * 2.50
printf "Repo size: ~%.0f tokens\n", tokens
printf "claude-sonnet-4: $%.4f\n", cost_sonnet
printf "claude-haiku-4: $%.4f\n", cost_haiku
printf "gpt-4o: $%.4f\n", cost_gpt4o
}'
Acknowledge the budget, start tracking, and proactively warn at 50%, 80%, and 95% thresholds. If the budget would be exceeded by a planned task, warn before proceeding.
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