Skill Detail

Audit performance claims with Pstack benchmark-checklist

Check tuning, bottlenecks, correctness, repeatability, and end-to-end relevance before an agent reports a measured speedup or regression.

Code Quality & ReviewMulti-Framework
Code Quality & Review Multi-Framework Security Reviewed
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COPY SKILL INSTRUCTIONS (OPTIONAL)
npx skills add agentskillexchange/skills --skill audit-performance-claims-with-pstack-benchmark-checklist Copy
Uses the third-party skills CLI, not an ASE-owned installer. Check your agent’s compatibility. This copies instructions; complete the upstream tool setup below separately.
At a glance
Tools required
Agent Skills-compatible coding agent; Node.js/npm for the documented skills installer; project benchmark harness; relevant runtime profiler and host diagnostics
Install & setup
Run npx skills add backnotprop/pstack, select benchmark-checklist in the interactive skill list, then choose your supported agent. Explicitly invoke benchmark-checklist with the performance claim, measurement script, revisions, data and run evidence. The skill is documented as standalone. Inspected source commit: fad8d01588a1b1ae803f87c8a1e0de79e28cffec. Installer/runtime behavior was not exercised.
Author
Lauren Tan (Pstack upstream); backnotprop (standalone mirror)
Publisher
Community
Last updated
Oct 26, 2026
Quick brief

Use Pstack’s benchmark-checklist skill before reporting a measured speedup, regression, or performance-based tool choice. The agent audits the claim against executable evidence and returns a verdict with the measurement’s limits.

How it works

What this skill actually does

Installation and invocation

Run npx skills add backnotprop/pstack. In the interactive selector choose benchmark-checklist and your supported agent. Upstream documents this skill as usable independently; installing the entire Pstack suite is unnecessary. Explicitly invoke the installed skill with your claim, measurement script, before/after revisions, representative data, and existing run results. Command menus vary by host.

Workflow

  1. State the exact performance claim and inspect what the harness measures and omits. Record machine load and available cores.
  2. Identify the bottleneck in a separate profiling run. Check production-equivalent settings, versions, datasets, cache conditions, and hardware limits before comparing alternatives.
  3. Verify successful outputs and confirm the intended work occurred inside the timed region. Count errors and retries.
  4. For comparisons, alternate the two configurations for at least five runs each. Report median and range; investigate differences smaller than observed variation. Connect microbenchmarks to the end-to-end user path.
  5. Return a faster, slower, no-measurable-difference, or inconclusive verdict with units, run count, spread, bottleneck evidence, and unresolved gaps. Keep profiling overhead out of reported timings.

Scope

This workflow audits a specific performance claim. It does not supply a benchmark engine, CI service, or general coding framework. Use it with the project’s existing harness and suitable profiling tools. If comparative tuning, the bottleneck, correctness, or actual execution cannot be established, disclose the gap and retain an inconclusive verdict. A requested rough estimate may use one explicitly labelled run, while still checking errors and actual execution.

Source: the inspected Pstack benchmark-checklist instructions. Metadata and documented invocation were verified; no runtime or security review is implied.