Generate weekly API cost reports from token usage logs with per-skill and per-model breakdowns.
npx skills add https://github.com/anajuliabit/aeon --skill cost-report-anajuliabit
Please help me install this Agent Skill. Skill: cost-report Source: https://github.com/anajuliabit/aeon/tree/main/skills/cost-report Command: npx skills add https://github.com/anajuliabit/aeon --skill cost-report-anajuliabit
It turns raw token-usage logs into a weekly, decision-ready cost report so you can spot anomalies, predict burn, and immediately act on concrete optimization opportunities.
Use the cost-report skill to generate a cost report covering the last 7 days by running it with N="7" days.
High-intent search queries and answers about installing and using this skill.
You can generate a weekly API cost report by reading token usage logs from a CSV file and applying model pricing tables to convert usage into dollar spend. This provides per-skill, per-model, and week-over-week cost breakdowns for monitoring.
Anomaly detection in API costs identifies unusually expensive runs and flags cost shifts compared to the prior time window. It works by analyzing token usage logs to detect sudden spend increases, helping you catch unexpected model behavior or configuration drift.
Yes, you can forecast budget burn for scheduled autonomous agent runs by projecting 30-day spend from the observed daily average token usage. This calls out high burn-rate risk to help you adjust workloads before exceeding your budget.
You calculate model pricing and API costs from token usage by applying pricing tables to input, output, cache read, and cache write token counts. This computes total dollar spend across different models and skills for accurate budget monitoring.
When token usage CSV data is missing during cost monitoring, the report generation safely skips the process. Defensive CSV parsing ensures the tool handles missing data gracefully without crashing or producing inaccurate cost forecasts.
An API cost optimization plan recommends model downgrades and cache usage improvements because they offer concrete dollar savings. Analyzing token usage logs identifies where cheaper models or better caching can reduce spend without sacrificing functionality.
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