Token Cost

Token Cost Skill

Every token optimization should start and end with the same question: how many, at what price, how often? — and most skip all three. This skill is the measurement layer: local token estimates (two stated heuristics, averaged, no tokenizer dependencies), cost math at your model's prices (supplied, never baked in — prices change faster than repos), and the before/after comparison that turns "this feels smaller" into "saves 4,200 tokens, $1.26 per hundred calls." The honest core: a 40% saving on something sent once is a rounding error; 8% on something sent every call is real money — the --calls flag is the whole insight.

What This Skill Produces

• The estimate — chars/4 and words×4/3, both shown, averaged, with the ±15% honesty label
• The cost math — per call and across the stated call volume, at supplied prices
• The comparison — before vs. after any optimization: tokens saved, percent, dollars at volume
• The verdict frame — worth-it / not-worth-it, decided by volume × savings vs. the optimization's own complexity

Required Inputs

Ask for these if not provided:

• The content — file or text to measure; for comparisons, both versions
• The prices — the model's $/M input (and output if relevant) — from the user's provider page, today's, because baked-in prices are stale prices
• The volume — how many calls this content rides along on (a system prompt rides every call; a one-shot report rides one) — the multiplier that decides everything

Programmatic Helper

python3 scripts/token_cost.py --file context.md --price-in 3 --calls 200
python3 scripts/token_cost.py --file original.json --compare crushed.json --price-in 3 --calls 200

Deterministic, stdlib-only. Two heuristics (≈4 chars/token, ≈0.75 words/token) averaged and labeled as estimates — real tokenizers vary by model and content type, and the script says so on every run rather than cosplaying as one.

Framework: The Measurement Rules

1. Volume is the multiplier that matters: cost = tokens × price × calls — and calls is the term intuition drops. System prompts, standing context, and per-turn tool schemas ride every call; optimizing them compounds. One-shot content barely matters however big it is. Every measurement states its volume assumption.
2. Measure both sides of an optimization: the crushed version's savings minus what the optimization itself costs (a crush header, an index that must also be loaded, engineering time) — comparisons that only count the win are marketing.
3. Estimates are estimates, loudly: the ±15% label is permanent; decisions that need exact counts (billing disputes, hard context limits) need the provider's tokenizer, and the skill says so instead of faking precision. For is-this-worth-it decisions, ±15% is plenty.
4. Price the journey's stages separately: input tokens (usually cheap, high volume), output tokens (usually 3–5× the price — why output discipline like token-diet pays disproportionately), and cached-input rates where the provider offers them (stable prefixes can cost ~10% of fresh input — measurement should know which bucket content falls in).
5. The worth-it verdict is a sentence, not a spreadsheet: "saves $0.31 per hundred calls; the crush step is one pipe — worth it" or "saves 60 tokens once; skip." Every measurement ends in one, because the point of measuring was deciding.

Output Format

Token Cost: [content] — at $[X]/M × [N] calls

[Script output: both heuristics, the estimate, the cost lines]

[Comparison mode: the before/after with savings at volume]

The verdict: [worth-it / not-worth-it, in one sentence with the reasoning] Estimates ±15%; prices supplied by you, dated today; exact counts need the provider's tokenizer.

Quality Checks

• Both heuristics shown, average labeled as an estimate
• Prices came from the user, never from memory
• The call-volume assumption is explicit in every cost figure
• Comparisons subtract the optimization's own cost
• The measurement ends in a worth-it sentence

Anti-Patterns

• Do not recite model prices from memory — they change; ask for today's
• Do not present heuristic counts as tokenizer truth — the ±15% label is load-bearing
• Do not optimize unmeasured — "feels smaller" has shipped many complexity-positive savings
• Do not ignore volume — the same 500 tokens is negligible once and structural at every-call
• Do not end without the verdict — a measurement that doesn't decide anything measured nothing