Compare RunPod spot and on-demand GPU prices and log dislocations.
npx skills add https://github.com/swarm-ai-research/aeon --skill runpod-spot-pricing
Please help me install this Agent Skill. Skill: runpod-spot-pricing Source: https://github.com/swarm-ai-research/aeon/tree/main/skills/runpod-spot-pricing Command: npx skills add https://github.com/swarm-ai-research/aeon --skill runpod-spot-pricing
This skill provides a daily, automated snapshot of GPU pricing by comparing RunPod community (spot) prices to RunPod secure (on-demand) prices, highlighting dislocations that inform scheduling decisions.
Run this skill daily to fetch the latest GPU prices, update memory/topics/runpod-pricing.md with a new date section, and generate signals when notable dislocations occur.
High-intent search queries and answers about installing and using this skill.
You can monitor RunPod GPU spot pricing by comparing community spot prices to secure on-demand prices. This skill computes discount percentages for A100, H100, 4090, and 3090 GPUs and appends the daily snapshot to a markdown file.
Tracking RunPod spot price dislocations involves computing the discount percentage between spot and on-demand GPU rates. This skill evaluates the pricing data and emits a Signals subsection when notable dislocations occur to guide deferred work scheduling.
Calculating the spot discount percentage for A100 and H100 GPUs requires reading cached GPU type data. This skill reads the RunPod cache file, computes spot, on-demand, and discount_pct per GPU, and writes the results to a dated markdown section.
Yes, the RunPod pricing monitoring skill gracefully handles missing API keys or cache data. It integrates with a prefetch workflow to avoid failures when the required cache file or authentication is absent.
RunPod spot pricing snapshots might not update if the cache file is missing or unreadable. The skill expects a valid gpu-types.json cache to compute prices and append new dated sections to the runpod-pricing markdown file.
Yes, you can automate daily GPU queueing decisions using RunPod pricing data. By evaluating spot versus on-demand price dislocations across GPU types, the skill provides the daily signals needed to defer or execute compute workloads.
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