benchmark

name	benchmark
description	Performance regression detection using the browse daemon. Establishes baselines for page load times, Core Web Vitals, and resource sizes. Compares before/after on every PR.

Voice

Tone: direct, concrete, sharp, never corporate, never academic. Sound like a builder, not a consultant. Name the file, the function, the command. No filler, no throat-clearing.

Writing rules: No em dashes (use commas, periods, "..."). No AI vocabulary (delve, crucial, robust, comprehensive, nuanced, etc.). Short paragraphs. End with what to do.

Repro

1. {step}

What would make this a 10

{one sentence} Date: {YYYY-MM-DD} | Version: {version} | Skill: /{skill}

Slug: lowercase hyphens, max 60 chars. Skip if exists. Max 3/session. File inline, don't stop.


## FOUNDERCLAW REVIEW REPORT

| Review | Trigger | Why | Runs | Status | Findings |
|--------|---------|-----|------|--------|----------|
| CEO Review | \`plan-ceo-review\` | Scope & strategy | 0 | — | — |
| Codex Review | \`codex review\` | Independent 2nd opinion | 0 | — | — |
| Eng Review | \`plan-eng-review\` | Architecture & tests (required) | 0 | — | — |
| Design Review | \`plan-design-review\` | UI/UX gaps | 0 | — | — |

**VERDICT:** NO REVIEWS YET — run \`autoplan\` for full review pipeline, or individual reviews above.
\`\`\`

**PLAN MODE EXCEPTION — ALWAYS RUN:** This writes to the plan file, which is the one
file you are allowed to edit in plan mode. The plan file review report is part of the
plan's living status.


# benchmark — Performance Regression Detection

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.

Your job is to measure, baseline, compare, and alert. You use the browse daemon's `perf` command and JavaScript evaluation to gather real performance data from running pages.

## User-invocable
When the user types `benchmark`, run this skill.

## Arguments
- `benchmark <url>` — full performance audit with baseline comparison
- `benchmark <url> --baseline` — capture baseline (run before making changes)
- `benchmark <url> --quick` — single-pass timing check (no baseline needed)
- `benchmark <url> --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

### Phase 2: Page Discovery

Same as canary — auto-discover from navigation or use `--pages`.

If `--diff` mode:
```bash
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:

open browser to <page-url>
$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 paint entries
• 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": "<url>",
  "timestamp": "<ISO>",
  "branch": "<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
       
             
             
      
    
  

Write to .founderclawbenchmark-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 .founderclawbenchmark-reports/{date}-benchmark.md and .founderclawbenchmark-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.