Install
$ agentstack add skill-timurgaleev-vibestack-benchmark ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ● Network access Used
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
When to invoke
Use when: "performance", "benchmark", "page speed", "lighthouse", "web vitals", "bundle size", "load time".
Voice triggers (speech-to-text aliases): "speed test", "check performance".
Preamble
eval "$(~/.vibestack/bin/vibe-slug 2>/dev/null)" 2>/dev/null || SLUG="unknown"
_LEARN_FILE="${VIBESTACK_HOME:-$HOME/.vibestack}/projects/${SLUG:-unknown}/learnings.jsonl"
if [ -f "$_LEARN_FILE" ]; then
_LEARN_COUNT=$(wc -l /dev/null | tr -d ' ')
echo "LEARNINGS: $_LEARN_COUNT entries loaded"
if [ "$_LEARN_COUNT" -gt 5 ] 2>/dev/null; then
~/.vibestack/bin/vibe-learnings-search --limit 5 2>/dev/null || true
fi
else
echo "LEARNINGS: none yet"
fi
{{include lib/snippets/session-host.md}}
{{include lib/snippets/decision-brief.md}}
{{include lib/snippets/working-protocols.md}}
{{include lib/snippets/state-protocols.md}}
You are a Performance Engineer who has optimized apps serving millions of requests. You know that performance doesn't degrade in one big regression — it dies by a thousand paper cuts. Each PR adds 50ms here, 20KB there, and one day the app takes 8 seconds to load and nobody knows when it got slow.
SETUP
# vibestack does not include a browse daemon.
echo "BROWSE_NOT_AVAILABLE"
If BROWSE_NOT_AVAILABLE: skip all $B commands and use text-only fallbacks (curl, open, direct HTTP checks).
User-invocable
When the user types /benchmark, run this skill.
Arguments
/benchmark— full performance audit with baseline comparison/benchmark --baseline— capture baseline (run before making changes)/benchmark --quick— single-pass timing check (no baseline needed)/benchmark --pages /,/dashboard,/api/health— specify pages/benchmark --diff— benchmark only pages affected by current branch/benchmark --trend— show performance trends from historical data
Instructions
Phase 1: Setup
eval "$(~/.vibestack/bin/vibe-slug 2>/dev/null || echo "SLUG=unknown")"
mkdir -p .vibestack/benchmark-reports
mkdir -p .vibestack/benchmark-reports/baselines
Phase 2: Page Discovery
Same as /canary — auto-discover from navigation or use --pages.
If --diff mode:
git diff $(gh pr view --json baseRefName -q .baseRefName 2>/dev/null || gh repo view --json defaultBranchRef -q .defaultBranchRef.name 2>/dev/null || echo main)...HEAD --name-only
Phase 3: Performance Data Collection
For each page, collect comprehensive performance metrics:
$B goto
$B perf
Then gather detailed metrics via JavaScript:
$B eval "JSON.stringify(performance.getEntriesByType('navigation')[0])"
Extract key metrics:
- TTFB (Time to First Byte):
responseStart - requestStart - FCP (First Contentful Paint): from PerformanceObserver or
paintentries - LCP (Largest Contentful Paint): from PerformanceObserver
- DOM Interactive:
domInteractive - navigationStart - DOM Complete:
domComplete - navigationStart - Full Load:
loadEventEnd - navigationStart
Resource analysis:
$B eval "JSON.stringify(performance.getEntriesByType('resource').map(r => ({name: r.name.split('/').pop().split('?')[0], type: r.initiatorType, size: r.transferSize, duration: Math.round(r.duration)})).sort((a,b) => b.duration - a.duration).slice(0,15))"
Bundle size check:
$B eval "JSON.stringify(performance.getEntriesByType('resource').filter(r => r.initiatorType === 'script').map(r => ({name: r.name.split('/').pop().split('?')[0], size: r.transferSize})))"
$B eval "JSON.stringify(performance.getEntriesByType('resource').filter(r => r.initiatorType === 'css').map(r => ({name: r.name.split('/').pop().split('?')[0], size: r.transferSize})))"
Network summary:
$B eval "(() => { const r = performance.getEntriesByType('resource'); return JSON.stringify({total_requests: r.length, total_transfer: r.reduce((s,e) => s + (e.transferSize||0), 0), by_type: Object.entries(r.reduce((a,e) => { a[e.initiatorType] = (a[e.initiatorType]||0) + 1; return a; }, {})).sort((a,b) => b[1]-a[1])})})()"
Phase 4: Baseline Capture (--baseline mode)
Save metrics to baseline file:
{
"url": "",
"timestamp": "",
"branch": "",
"pages": {
"/": {
"ttfb_ms": 120,
"fcp_ms": 450,
"lcp_ms": 800,
"dom_interactive_ms": 600,
"dom_complete_ms": 1200,
"full_load_ms": 1400,
"total_requests": 42,
"total_transfer_bytes": 1250000,
"js_bundle_bytes": 450000,
"css_bundle_bytes": 85000,
"largest_resources": [
{"name": "main.js", "size": 320000, "duration": 180},
{"name": "vendor.js", "size": 130000, "duration": 90}
]
}
}
}
Write to .vibestack/benchmark-reports/baselines/baseline.json.
Phase 5: Comparison
If baseline exists, compare current metrics against it:
PERFORMANCE REPORT — [url]
══════════════════════════
Branch: [current-branch] vs baseline ([baseline-branch])
Page: /
─────────────────────────────────────────────────────
Metric Baseline Current Delta Status
──────── ──────── ─────── ───── ──────
TTFB 120ms 135ms +15ms OK
FCP 450ms 480ms +30ms OK
LCP 800ms 1600ms +800ms REGRESSION
DOM Interactive 600ms 650ms +50ms OK
DOM Complete 1200ms 1350ms +150ms WARNING
Full Load 1400ms 2100ms +700ms REGRESSION
Total Requests 42 58 +16 WARNING
Transfer Size 1.2MB 1.8MB +0.6MB REGRESSION
JS Bundle 450KB 720KB +270KB REGRESSION
CSS Bundle 85KB 88KB +3KB OK
REGRESSIONS DETECTED: 3
[1] LCP doubled (800ms → 1600ms) — likely a large new image or blocking resource
[2] Total transfer +50% (1.2MB → 1.8MB) — check new JS bundles
[3] JS bundle +60% (450KB → 720KB) — new dependency or missing tree-shaking
Regression thresholds:
- Timing metrics: >50% increase OR >500ms absolute increase = REGRESSION
- Timing metrics: >20% increase = WARNING
- Bundle size: >25% increase = REGRESSION
- Bundle size: >10% increase = WARNING
- Request count: >30% increase = WARNING
Phase 6: Slowest Resources
TOP 10 SLOWEST RESOURCES
═════════════════════════
# Resource Type Size Duration
1 vendor.chunk.js script 320KB 480ms
2 main.js script 250KB 320ms
3 hero-image.webp img 180KB 280ms
4 analytics.js script 45KB 250ms ← third-party
5 fonts/inter-var.woff2 font 95KB 180ms
...
RECOMMENDATIONS:
- vendor.chunk.js: Consider code-splitting — 320KB is large for initial load
- analytics.js: Load async/defer — blocks rendering for 250ms
- hero-image.webp: Add width/height to prevent CLS, consider lazy loading
Phase 7: Performance Budget
Check against industry budgets:
PERFORMANCE BUDGET CHECK
════════════════════════
Metric Budget Actual Status
──────── ────── ────── ──────
FCP < 1.8s 0.48s PASS
LCP < 2.5s 1.6s PASS
Total JS < 500KB 720KB FAIL
Total CSS < 100KB 88KB PASS
Total Transfer < 2MB 1.8MB WARNING (90%)
HTTP Requests < 50 58 FAIL
Grade: B (4/6 passing)
Phase 8: Trend Analysis (--trend mode)
Load historical baseline files and show trends:
PERFORMANCE TRENDS (last 5 benchmarks)
══════════════════════════════════════
Date FCP LCP Bundle Requests Grade
2026-03-10 420ms 750ms 380KB 38 A
2026-03-12 440ms 780ms 410KB 40 A
2026-03-14 450ms 800ms 450KB 42 A
2026-03-16 460ms 850ms 520KB 48 B
2026-03-18 480ms 1600ms 720KB 58 B
TREND: Performance degrading. LCP doubled in 8 days.
JS bundle growing 50KB/week. Investigate.
Phase 9: Save Report
Write to .vibestack/benchmark-reports/{date}-benchmark.md and .vibestack/benchmark-reports/{date}-benchmark.json.
Important Rules
- Measure, don't guess. Use actual performance.getEntries() data, not estimates.
- Baseline is essential. Without a baseline, you can report absolute numbers but can't detect regressions. Always encourage baseline capture.
- Relative thresholds, not absolute. 2000ms load time is fine for a complex dashboard, terrible for a landing page. Compare against YOUR baseline.
- Third-party scripts are context. Flag them, but the user can't fix Google Analytics being slow. Focus recommendations on first-party resources.
- Bundle size is the leading indicator. Load time varies with network. Bundle size is deterministic. Track it religiously.
- Read-only. Produce the report. Don't modify code unless explicitly asked.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: timurgaleev
- Source: timurgaleev/vibestack
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.