performance-profiling

performance-profiling is a Runtime diagnostics & profiling skill for AI agents, published by HeshamFS in the materials-simulation-skills repository on GitHub. Skillspace rates performance-profiling F (45/100) and the safety scan rated it risky; the rating is based on static analysis only.

Risky. This skill has patterns associated with unsafe behaviour. Read the source carefully before running it.

Identify computational bottlenecks, analyze parallel scaling, estimate memory requirements, and generate optimization recommendations for materials simulations — parse timing logs to find dominant phases (solver, assembly, I/O), evaluate strong and weak scaling efficiency, profile memory from mesh and field parameters, and detect bottlenecks with actionable fix suggestions. Use when a simulation is running slower than expected, investigating MPI scaling efficiency, planning HPC resource allocation, deciding whether to tune the preconditioner or reduce I/O frequency, or estimating if a problem fits in available RAM, even if the user only says "my simulation is too slow" or "how many nodes do I need."

Grade

F

Quality

45 / 100

Safety

Risky

GitHub stars

59

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

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