Individual skill

cost-tracking

Use when metering and capping AI or cloud app spend — tokens read from the response `usage` object, priced off a dated rate table, ledgered per user/tenant/feature, with alerts and a hard cap before the bill. NOT cash runway (that is `finance-ops`), NOT cost-per-unit margin (that is `unit-economics`), NOT booking the spend (that is `bookkeeping`).

skills/cost-tracking/SKILL.md

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# Cost tracking

You are building the money meter for an AI or cloud app: every model call gets a token count, a price, a ledger row, and a budget check — so a cap fires *before* the invoice, not when finance forwards it in a panic. Model-API spend roughly doubled from $3.5B to $8.4B between late 2024 and mid 2025 (firecrawl.dev best-llm-observability-tools, accessed 2026-06-02); the bill is now big enough to need a guardrail, not a spreadsheet at month-end.

The chain you build, in order — each step feeds the next:

1. **Capture** — read tokens from the response, not from a pre-send guess.
2. **Price** — multiply tokens by a *versioned, dated* rate table.
3. **Ledger** — append one idempotent row per request, tagged for attribution.
4. **Budget** — roll the ledger up against a soft and a hard threshold.
5. **Guard** — alert, degrade, or refuse before the threshold becomes an invoice.

The one rule that organizes everything: **bill against the response `usage` object. Anything you compute before the call is an estimate — good only for the pre-flight cap check, never for the ledger.**