Diagnose and Improve Full-Stack Performance
Full-stack teams often lack a repeatable way to measure performance and locate bottlenecks. This skill provides testing workflows, diagnostic commands, optimization guidance, and report structures.
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Review the Skillstore skill "perf-fullstack-engineer" from https://skillstore.io/skills/zl2023github-perf-fullstack-engineer.md and its manifest at https://skillstore.io/api/skills/zl2023github-perf-fullstack-engineer/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
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Using "perf-fullstack-engineer". Define a baseline for a checkout API expecting 200 requests per second.
Expected outcome:
Target 200 requests per second with P95 below 500 milliseconds and errors below 0.1 percent. Measure CPU, memory, database latency, and saturation.
Using "perf-fullstack-engineer". Diagnose rising P99 latency while CPU remains below 50 percent.
Expected outcome:
Using "perf-fullstack-engineer". Outline a performance report after an optimization.
Expected outcome:
Compare the same workload before and after the change. Show latency percentiles, throughput, errors, resource use, remaining bottlenecks, and release recommendations.
The skill is readable Markdown guidance with no executable package, prompt injection, credential collection, obfuscation, or covert data transfer. Static alerts primarily misread Markdown backticks, Chinese text, placeholders, localhost URLs, and documented developer commands as dangerous behavior.
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https://skillstore.io/skills/zl2023github-perf-fullstack-engineer/audits/4?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report[](https://skillstore.io/skills/zl2023github-perf-fullstack-engineer?utm_source=security_passport_badge)<a href="https://skillstore.io/skills/zl2023github-perf-fullstack-engineer?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/zl2023github-perf-fullstack-engineer/security.svg" alt="Skillstore security assessment" loading="lazy"></a><iframe src="https://skillstore.io/embed/skills/zl2023github-perf-fullstack-engineer.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>zl2023github. (2026). perf-fullstack-engineer security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/zl2023github-perf-fullstack-engineer/audits/4@techreport{zl2023github-zl2023github-perf-fullstack-engineer-2026,
author = {zl2023github},
title = {perf-fullstack-engineer security audit report (audit version 4)},
institution = {Skillstore},
year = {2026},
number = {4},
url = {https://skillstore.io/skills/zl2023github-perf-fullstack-engineer/audits/4},
note = {Author version unspecified}
}cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "perf-fullstack-engineer security audit report (audit version 4)"
version: "unspecified"
type: report
authors:
- name: "zl2023github"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/zl2023github-perf-fullstack-engineer/audits/4"
identifiers:
- type: other
value: "skillstore:zl2023github-perf-fullstack-engineer:audit:4"
description: "Skillstore immutable audit report identifier"
Define service targets, select test scenarios, and produce a repeatable baseline before a release.
Correlate latency, resource, runtime, database, and application signals to prioritize likely bottlenecks.
Model peak traffic, identify system limits, and organize scaling recommendations with measurable acceptance criteria.
Create a performance baseline for [service]. Use [traffic profile], [latency target], [error target], and [environment]. List assumptions and required metrics.
Design a [tool] load test for [workflow]. Include stages, data preparation, checks, thresholds, monitoring, cleanup, and a safe execution sequence.
Analyze these performance results: [results]. Correlate application, host, database, runtime, and network evidence. Rank hypotheses and propose verification steps.
Create a capacity plan for [system] at [forecast load]. Estimate limits, identify architecture risks, compare scaling options, and define regression gates.
Author
zl2023githubLicense
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
88a8e9a07f4c54ab105c1c41b6267c287146b07b
Maintenance freshness
7/26/2026
Usage
3 downloads · 1 views
File structure
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