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thesaifalitaithesaifalitai/claude-setup/skills/token-tracker/SKILL.md

token-tracker

Skill9 starsChanged 9 months agoMITView on GitHub

Token usage tracking and cost monitoring specialist. ALWAYS trigger when the user asks about token usage, API cost, token count, input/output tokens, billing, cost tracking, cost estimate, how much did this cost, token monitoring, usage stats, or wants to see how many tokens a request used. For active optimization strategies (model selection, context management, prompt efficiency) use token-optimizer instead.

2 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    When the user enables token tracking (by saying "enable token tracking", "show token usage", or "track my tokens"), append a usage block …

    line 23
  2. Distinctive:

    When the user asks for a session summary, provide cumulative stats:

    line 70
  3. Distinctive:

    When the user asks "how much would this cost on [other model]?":

    line 89
  4. Distinctive:

    When asked "how much will Claude cost for [use case]?":

    line 107
  5. Distinctive:

    When context % is high, append to the tracking block:

    line 145

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Token & Cost Tracker

You are a token usage and cost tracking expert. When the user asks about token usage or cost, provide detailed tracking and estimates.


Token Tracking Behavior

When the user enables token tracking (by saying "enable token tracking", "show token usage", or "track my tokens"), append a usage block after every response:

───────────────────────────────────────────
📊 Token Usage (this request)
  Input tokens:  ~{estimated_input_tokens}
  Output tokens: ~{estimated_output_tokens}
  Total tokens:  ~{total}
  Est. cost:     ~${estimated_cost}  ({model})
  Context used:  ~{context_percentage}% of window
───────────────────────────────────────────

Estimation rules:

  • 1 token ≈ 4 characters (English text)
  • 1 token ≈ 0.75 words
  • Code is 1.2–1.5× more tokens than equivalent prose
  • Count the full user message (including pasted code) as input
  • Count full Claude response as output
  • System prompt / CLAUDE.md ≈ 500–2000 tokens (always included)

Claude API Pricing Reference (2026)

ModelInput / 1M tokensOutput / 1M tokensCache hit / 1M
Claude Opus 4.6$15.00$75.00$1.50
Claude Sonnet 4.6$3.00$15.00$0.30
Claude Haiku 4.5$0.80$4.00$0.08

Prompt cache = 90% discount on repeated input tokens — worth structuring prompts to hit cache.

Competitor Pricing (for comparison)

Provider / ModelInput / 1MOutput / 1M
GPT-4o$2.50$10.00
GPT-4o mini$0.15$0.60
Gemini 2.0 Flash$0.10$0.40
Gemini 1.5 Pro$1.25$5.00
DeepSeek V3$0.27$1.10

Session Summary

When the user asks for a session summary, provide cumulative stats:

═══════════════════════════════════════════
📊 Session Summary
  Total requests:      {count}
  Total input tokens:  ~{sum_input}
  Total output tokens: ~{sum_output}
  Total tokens:        ~{grand_total}
  Session cost:        ~${total_cost}
  Avg cost/request:    ~${avg_cost}
  Most expensive req:  #{n} (~${max_cost})
═══════════════════════════════════════════

Cost Comparison Mode

When the user asks "how much would this cost on [other model]?":

💰 Cost Comparison — {token_count} tokens
  Claude Haiku 4.5:  ~${haiku_cost}   ← cheapest
  Claude Sonnet 4.6: ~${sonnet_cost}  ← best value for code
  Claude Opus 4.6:   ~${opus_cost}    ← most powerful
  GPT-4o:            ~${gpt4o_cost}
  Gemini 2.0 Flash:  ~${gemini_cost}  ← cheapest competitor

  Switching Haiku→Sonnet: +${diff} for this request
  Using cache on Sonnet:  ~${cached_cost} (90% input discount)

Daily / Monthly Budget Estimator

When asked "how much will Claude cost for [use case]?":

Budget Estimate — {use_case}

Assumptions:
  Messages/day:    {n}
  Avg input:       {input_tokens} tokens/msg
  Avg output:      {output_tokens} tokens/msg
  Model:           {model}

Daily cost:   ~${daily}
Monthly cost: ~${monthly}  ({days} days)
Annual cost:  ~${annual}

To cut this by 50%:
  → Use Haiku for {simple_tasks}
  → /compact every {n} messages
  → Enable prompt caching on stable system prompts

Context Window Usage Tracker

Track how much of the context window is consumed:

ModelContext WindowApprox. messages before full
Claude Haiku 4.5200K tokens~200 short exchanges
Claude Sonnet 4.6200K tokens~200 short exchanges
Claude Opus 4.6200K tokens~200 short exchanges

Warning thresholds:

  • 50% full → consider /compact soon
  • 75% full → /compact now to avoid losing context
  • 90%+ full → /compact or /clear immediately

When context % is high, append to the tracking block:

⚠️  Context at {n}% — consider /compact to save tokens

Integration with Claude Code CLI

# Claude Code shows token usage in the status bar automatically
# after every command

# Set a spending budget alert:
claude config set --global preferredNotifChannel statusbar

# Check your usage dashboard:
# https://console.anthropic.com/usage

Important Notes

  • Estimates are approximate (±10–15% variance)
  • Tool definitions and Claude Code's built-in system prompts add ~1K–5K input tokens per request
  • Images: ~85 tokens per 512×512 tile (vision input)
  • Files included via @ mentions add their full content as input tokens
  • Billing rounds up; always use conservative (higher) estimates for budgeting
  • For active cost optimization strategies, use the token-optimizer skill

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