profile-tests

Original：🇺🇸 English

Translated

Profile CPU performance of tests and browser tests in elements package. Use when investigating performance issues, optimizing test execution, or when the user mentions profiling, performance analysis, hotspots, or slow tests.

46installs

Sourceeditframe/skills

Added on2026-05-01

NPX Install

npx skill4agent add editframe/skills profile-tests

SKILL.md Content

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Profiling Tests and Browser Tests

CPU profiling for elements package tests using the unified profiling library. Identifies performance bottlenecks, generates optimization recommendations, and detects anti-patterns.

Quick Commands

Profile Browser Tests (Simple)

bash

# Built-in profiling for all browser tests
./scripts/browsertest --profile

# Profile specific test file
./scripts/browsertest --profile packages/elements/src/preview/renderTimegroupToCanvas.browsertest.ts

# Profile with test pattern
./scripts/browsertest --profile -t "batch capture"

Profile Browser Tests (Detailed Analysis)

bash

# Full analysis with unified profiling library
npx tsx scripts/profile-browsertest.ts \
  packages/elements/src/preview/renderTimegroupToCanvas.browsertest.ts

# Get JSON output for LLM analysis
npx tsx scripts/profile-browsertest.ts \
  packages/elements/src/preview/renderTimegroupToCanvas.browsertest.ts \
  --json

# Focus on specific file
npx tsx scripts/profile-browsertest.ts \
  packages/elements/src/preview/renderTimegroupToCanvas.browsertest.ts \
  --focus renderToImage \
  --json

Profile Scenarios

bash

# Profile a scenario in sandbox
./scripts/ef profile EFCanvas basic --json

# With pattern detection
./scripts/ef profile EFCanvas basic --json --verbose

# Compare against baseline
./scripts/ef profile EFCanvas basic \
  --baseline .profiles/baseline.cpuprofile \
  --json

Performance Investigation Workflow

When investigating performance issues in tests:

1. Capture Initial Profile

bash

# For browser tests
npx tsx scripts/profile-browsertest.ts \
  packages/elements/src/preview/renderTimegroupToCanvas.browsertest.ts \
  --json > profile.json

# For scenarios
./scripts/ef profile EFCanvas basic --json > profile.json

2. Analyze JSON Output

Parse the profile to identify:

json

{
  "hotspots": [
    {
      "rank": 1,
      "functionName": "updateFrame",
      "file": "EFTimegroup.ts",
      "line": 234,
      "selfTimeMs": 45.2,
      "selfTimePct": 31.1,
      "callCount": 234
    }
  ],
  "byFile": [
    { "file": "EFTimegroup.ts", "timeMs": 82.1, "timePct": 56.5 }
  ],
  "recommendations": [
    "• updateFrame called 234 times - consider caching"
  ],
  "patterns": [
    {
      "name": "Hot Loop Detected",
      "severity": "high",
      "suggestion": "Review loop logic..."
    }
  ]
}

3. Prioritize Optimizations

Focus on:

Functions with
```
selfTimePct > 15%
```
(critical bottlenecks)
Functions with
```
callCount > 100
```
and
```
selfTimePct > 5%
```
(caching opportunities)
Files with
```
timePct > 40%
```
(architectural issues)
Patterns with
```
severity: "high"
```

4. Make Changes

Apply optimizations based on patterns detected.

5. Validate with Baseline Comparison

bash

# Save baseline before changes
./scripts/ef profile EFCanvas basic \
  --save .profiles/baseline-$(date +%Y%m%d-%H%M%S).cpuprofile

# After optimization, compare
./scripts/ef profile EFCanvas basic \
  --baseline .profiles/baseline-TIMESTAMP.cpuprofile \
  --json

Check comparison output:

Negative
```
selfTimeDiffMs
```
= improvement ✅
Positive
```
selfTimeDiffMs
```
= regression ⚠️

Understanding Profile Metrics

Key Metrics

Metric	Meaning	Use For
`selfTimeMs`	Time in function itself (excluding callees)	Direct optimization target
`selfTimePct`	Percentage of total profile time	Prioritization (>15% = critical)
`callCount`	Times function appears in call stacks	Caching/memoization decisions
`hitCount`	Profile samples that caught function	Loop detection (>200 = tight loop)

Interpreting Patterns

The unified profiling library detects 8 common anti-patterns:

Excessive DOM Manipulation → Batch DOM updates or use DocumentFragment Layout Thrashing → Separate layout reads from writes Hot Loop → Optimize loop body or reduce iterations Death by a Thousand Cuts → Add memoization for frequently-called functions Heavy JSON Operations → Use structured cloning or reduce serialization Frequent Style Computation → Cache computed styles Animation API Overhead → Cache animation objects File-Level Concentration → Architectural review needed

Common Optimization Patterns

High Call Count (>100) + Moderate Time (5-15%)

Problem: Function called frequently with small individual cost Solution: Memoization or caching

typescript

// Before
function expensiveCalculation(x: number) {
  return /* complex math */;
}

// After
const cache = new Map<number, number>();
function expensiveCalculation(x: number) {
  if (cache.has(x)) return cache.get(x)!;
  const result = /* complex math */;
  cache.set(x, result);
  return result;
}

High Self Time (>30%) + Low Call Count (<10)

Problem: Single function doing too much work Solution: Reduce complexity or defer work

typescript

// Before
function processAll() {
  // All work done synchronously
  loadData();
  transformData();
  validateData();
  saveData();
}

// After  
function processAll() {
  loadData(); // Only critical work
  queueMicrotask(() => {
    transformData();
    validateData();
    saveData();
  });
}

DOM Manipulation Pattern Detected

Problem: Multiple DOM updates causing reflows Solution: Batch updates

typescript

// Before
for (const item of items) {
  container.appendChild(createItem(item)); // Multiple reflows
}

// After
const fragment = document.createDocumentFragment();
for (const item of items) {
  fragment.appendChild(createItem(item));
}
container.appendChild(fragment); // Single reflow

Output Formats

Text Output (Human-Readable)

bash

./scripts/ef profile EFCanvas basic

Shows:

Top hotspots by self time
Time aggregated by file
Automated recommendations
Detected patterns (with --verbose)

JSON Output (LLM/Script-Friendly)

bash

./scripts/ef profile EFCanvas basic --json

Structured data for programmatic analysis:

Ranked hotspots with metrics
File-level aggregations
Recommendations array
Pattern detection results

Verbose Mode (Enhanced Analysis)

bash

./scripts/ef profile EFCanvas basic --json --verbose

Adds:

Call count information
Anti-pattern detection
Detailed suggestions

Specialized Profiling Tools

Profile Playback Performance

bash

npx tsx scripts/profile-playback.ts \
  --project improv-edit \
  --duration 5000 \
  --json

Profile Export Performance

bash

npx tsx scripts/profile-export.ts \
  --project design-catalog \
  --json

Profile Page Load

bash

npx tsx scripts/profile-load.ts \
  --project improv-edit \
  --json

Troubleshooting

"Profiling functions not available"

Ensure browser is controlled by Playwright (use
```
ef open
```
, not regular browser)

"Profile data is empty"

Scenario/test may execute too quickly (<10ms)
Try longer-running tests or increase test complexity

"High numbers don't make sense"

Check if times are in microseconds vs milliseconds
Unified library returns milliseconds
Legacy interfaces may use microseconds

"Call counts seem wrong"

Call count is approximate based on call stack traversal
Use
```
hitCount
```
for sampling-based frequency instead

Best Practices

Always save baselines before making changes
Focus on self time for direct impact (total time includes callees)
Start with top 3 hotspots for biggest impact
Use JSON output for programmatic analysis
Check patterns before manual analysis - they suggest specific fixes
Validate changes by comparing before/after profiles
Watch for regressions in baseline comparisons

Additional Resources

Full profiling guide:
```
elements/PROFILING.md
```
LLM-specific guide:
```
elements/LLM_PROFILING_GUIDE.md
```

Implementation details:

elements/PROFILING_IMPLEMENTATION_SUMMARY.md

Library source:
```
packages/elements/src/profiling/
```

profile-tests

NPX Install

Tags

SKILL.md Content

Profiling Tests and Browser Tests

Quick Commands

Profile Browser Tests (Simple)

Profile Browser Tests (Detailed Analysis)

Profile Scenarios

Performance Investigation Workflow

1. Capture Initial Profile

2. Analyze JSON Output

3. Prioritize Optimizations

4. Make Changes

5. Validate with Baseline Comparison

Understanding Profile Metrics

Key Metrics

Interpreting Patterns

Common Optimization Patterns

High Call Count (>100) + Moderate Time (5-15%)

High Self Time (>30%) + Low Call Count (<10)

DOM Manipulation Pattern Detected

Output Formats

Text Output (Human-Readable)

JSON Output (LLM/Script-Friendly)

Verbose Mode (Enhanced Analysis)

Specialized Profiling Tools

Profile Playback Performance

Profile Export Performance

Profile Page Load

Troubleshooting

Best Practices

Additional Resources