Consumption guardrails

Name: Consumption guardrails
Author: @agentcosts

@agentcosts/consumption-guardrails

Set and check consumption guardrails for an AI product: per-customer daily and monthly spend caps, month-end forecasts, spend spike rules, runaway agent runs, and failure rate spikes, plus a runbook for what to do when each alert fires. Runs on usage files; exit code 2 on critical alerts for cron or CI.

SkillDevOpsv1.0.0 (latest)Tested

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Content hash (sha256): 245cc15bcc7162c631246849d2742c642ad53c4a09642e7b095324ea0b9cbcf4
Signature: Signed by platform key 10aff36bedd43e66. The CLI checks the hash and signature against the published keys on install.
Published: 5 Nov 2026

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/kit add @agentcosts/consumption-guardrails

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This kit reads ./data/**, writes to ./data/guardrails.json, ./out/guardrails* and runs node scripts/guardrails.mjs. Install it?

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More from @agentcosts

Usage pricing simulator
Test pricing models for an AI product on your real usage before you change a price: per seat, per task, credits, and hybrid (platform fee plus included credits plus overage). Shows revenue, gross margin after LLM cost, and which customers' bills would jump or drop under each model.
Llm usage normalizer
Convert Anthropic and OpenAI usage exports, or your own app's token logs, into one usage schema with customers, tasks, cached and uncached tokens, and cost from a price table you edit. Fixes the classic mistake of counting cached tokens twice, and lists unmapped keys and unpriced models.
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Gross margin per customer for an AI product: revenue minus LLM cost minus allocated hosting and other cost of revenue. Flags negative and below-target customers, names the task driving their cost, and gives the price each one would need to hit your target margin.
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Turn LLM usage logs into one self-contained HTML cost dashboard for an agentic product: cost per customer, per task, per successful outcome, model mix, cache hit rate, and gross margin when you add revenue by customer. Prices blank costs from an editable price table.

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