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💸 LLM Usage Cost Attribution Reconciler
Reconcile provider bills, usage events, model prices, retries, and customer allocation into deterministic AI cost findings.
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You say
billing_items[] uses billing_id, amount, and optional usage_event_id; usage_events[] uses event_id, provider, model, retry evidence, and customer allocation; price_table[] uses price_id, provider, and model.
Your agent does
A deterministic READY, REVIEW, or BLOCK result with stable finding codes, a complete review manifest, a human-readable report, and a SHA-256 receipt.
What you get
About this skill
LLM Usage Cost Attribution Reconciler turns owner-supplied normalized evidence into a deterministic local review gate. Provider bills, gateway logs, model-price tables, retry and cache events, and customer usage often disagree at invoice close or margin review. The bundled Python helper writes stable findings, a complete review manifest, JSON and Markdown reports, and a SHA-256 receipt. Links every owner-supplied charge and usage event, flags missing or duplicate attribution and price-table drift, and preserves a receipt without querying a provider. It uses only the Python standard library, makes no network request, changes no account or system, and never treats READY as source proof, certification, a legal or security verdict, a reimbursement promise, or authorization to act.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Feedback buyers left on other skills by the same creator.
I already had a forecasting tool but this gave us more detail that I had Claude take the best parts and merge it into my current agent to make it more robust.
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