Skill Detail

Trace Python memory allocation hotspots before leaks and spikes reach production with Memray

Lets an agent record Python allocation traces and inspect the biggest allocators, retained objects, and leak paths before memory growth turns into a production incident.

Monitoring & AlertsMulti-Framework
Monitoring & Alerts Multi-Framework Published
โญ 15k GitHub stars โฌ‡ 16.3M/wk npm
INSTALL WITH ANY AGENT
npx skills add agentskillexchange/skills --skill trace-python-memory-allocation-hotspots-before-leaks-and-spikes-reach-production-with-memray Copy
Works best when you want a reusable capability, not another fragile one-off prompt.
At a glance
Tools required
Python, pip or uv, memray CLI
Install & setup
pip install memray
Author
Bloomberg
Publisher
Organization
Last updated
Apr 17, 2026
Quick brief

Use Memray when an agent needs to answer a specific diagnostic question: where is Python memory actually being allocated, retained, or leaking? It fits incident response, performance regression triage, and pre-release investigations where heap growth is real but the cause is not yet clear.

How it works

What this skill actually does

Invoke this instead of using the product normally when the agent must capture an allocation trace, compare runs, and turn the resulting evidence into a concrete remediation path. This is skill-shaped because the workflow boundary is narrow and operational: capture memory traces, inspect hotspots, and identify leak sources. It is not a generic Python tooling card or broad observability platform listing.