Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add hoangatg/ai-agent-toolkit --skill performance-profilinggit clone --depth 1 https://github.com/hoangatg/ai-agent-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hoangatg/ai-agent-toolkit/performance-profiling)<a href="https://agentmods.dev/skills/hoangatg/ai-agent-toolkit/performance-profiling"><img src="https://agentmods.dev/badge/skills/hoangatg/ai-agent-toolkit/performance-profiling/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hoangatg/ai-agent-toolkit/performance-profiling"><img src="https://agentmods.dev/badge/skills/hoangatg/ai-agent-toolkit/performance-profiling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.00757 |
| Opus 5 | $0.00009 | $0.00378 |
| Sonnet 5 | $0.00003 | $0.00151 |
| Haiku 4.5 | $0.00002 | $0.00076 |
Grade A, and why
performance-profiling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to performance-profiling — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Profiling
Measure, analyze, optimize - in that order.
🔧 Runtime Scripts
Execute these for automated profiling:
| Script | Purpose | Usage |
|---|---|---|
scripts/lighthouse_audit.py |
Lighthouse performance audit | python scripts/lighthouse_audit.py https://example.com |
1. Core Web Vitals
Targets
| Metric | Good | Poor | Measures |
|---|---|---|---|
| LCP | < 2.5s | > 4.0s | Loading |
| INP | < 200ms | > 500ms | Interactivity |
| CLS | < 0.1 | > 0.25 | Stability |
When to Measure
| Stage | Tool |
|---|---|
| Development | Local Lighthouse |
| CI/CD | Lighthouse CI |
| Production | RUM (Real User Monitoring) |
2. Profiling Workflow
The 4-Step Process
1. BASELINE → Measure current state
2. IDENTIFY → Find the bottleneck
3. FIX → Make targeted change
4. VALIDATE → Confirm improvement
Profiling Tool Selection
| Problem | Tool |
|---|---|
| Page load | Lighthouse |
| Bundle size | Bundle analyzer |
| Runtime | DevTools Performance |
| Memory | DevTools Memory |
| Network | DevTools Network |
3. Bundle Analysis
What to Look For
| Issue | Indicator |
|---|---|
| Large dependencies | Top of bundle |
| Duplicate code | Multiple chunks |
| Unused code | Low coverage |
| Missing splits | Single large chunk |
Optimization Actions
| Finding | Action |
|---|---|
| Big library | Import specific modules |
| Duplicate deps | Dedupe, update versions |
| Route in main | Code split |
| Unused exports | Tree shake |
4. Runtime Profiling
Performance Tab Analysis
| Pattern | Meaning |
|---|---|
| Long tasks (>50ms) | UI blocking |
| Many small tasks | Possible batching opportunity |
| Layout/paint | Rendering bottleneck |
| Script | JavaScript execution |
Memory Tab Analysis
| Pattern | Meaning |
|---|---|
| Growing heap | Possible leak |
| Large retained | Check references |
| Detached DOM | Not cleaned up |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 144 lines · 17 tokens per session scan A 5e58efdd8fd9
performance-profiling is a skill published in the GitHub repository hoangatg/ai-agent-toolkit (1 stars, last pushed 5mo ago), licensed MIT. It adds 17 tokens to every session and 757 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to performance-profiling, differing in 2 lines, and is treated as a copy.
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