chrome-performance
SkillDev toolsUse Chrome DevTools MCP for frontend performance work such as slow page loads, LCP, CLS, INP, Lighthouse regressions, scripting hotspots, or layout thrash. Trigger when the user asks for page speed analysis, Core Web Vitals debugging, trace-based investigation, or before-and-after performance verification.
Use chrome-performance in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add chrome-performance and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the chrome-performance skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by hashgraph-online/awesome-codex-plugins in plugins/win4r/chrome-devtools-codex-plugin/skills/chrome-performance/SKILL.md and read by Ahel’s review.
Use this skill when performance findings need evidence from Chrome instead of code inspection alone.
Default Flow
- Start from the exact URL and device profile that reproduces the problem.
- Record a baseline with
lighthouse_auditorperformance_start_traceandperformance_stop_trace. - Drill into the highest-signal issue first, then inspect network, DOM, or scripts only as needed.
- After code changes, rerun the same scenario and compare the results.
Tool Selection
lighthouse_auditfor quick page-level diagnostics and report output.performance_start_traceandperformance_stop_tracefor deeper timing analysis.performance_analyze_insightto drill into a specific DevTools insight.emulateto keep device, network, CPU, and viewport conditions consistent.
Working Rules
- Keep the scenario deterministic: one URL, one interaction path, one device profile.
- Save traces and reports to files when they are large.
- Treat LCP, CLS, and INP as user-path problems; connect each finding to the specific element, request, or script that caused it.
- Avoid mixing unrelated experiments in one trace.
Expected Output
Return the problem, the evidence, the likely cause, and the concrete code area to change before proposing fixes.
Signals
- GitHub stars
- 1k
- Forks
- 316
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
chrome-performance- Source
- github.com/hashgraph-online/awesome-codex-plugins
github.com/hashgraph-online/awesome-codex-plugins
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