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 The-AI-Directory-Company/agents-and-skills --skill performance-auditgit clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-skillsWrote 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/the-ai-directory-company/agents-and-skills/performance-audit)<a href="https://agentmods.dev/skills/the-ai-directory-company/agents-and-skills/performance-audit"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-skills/performance-audit/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/the-ai-directory-company/agents-and-skills/performance-audit"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-skills/performance-audit.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 | $0.00029 | $0.01328 |
| Opus 5.5 | $0.00012 | $0.00531 |
| Sonnet 5.5 | $0.00006 | $0.00266 |
| Haiku 4.5 | $0.00003 | $0.00133 |
Grade A, and why
performance-audit 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Audit
Before you start
Gather the following from the user. If anything is missing, ask before proceeding:
- What is slow? — Specific page, endpoint, query, or workflow (not "the app feels sluggish")
- How slow is it? — Current measured latency, load time, or throughput numbers
- What is the target? — Acceptable performance threshold (e.g., "page load under 2 seconds at p95")
- What is the architecture? — Frontend framework, backend services, database(s), CDN, caching layers
- What is the traffic profile? — Average and peak request volume, geographic distribution
- What has already been tried? — Previous optimization attempts and their outcomes
Audit template
1. Establish Baselines
Measure before you optimize. Record current metrics for every area under audit:
Frontend (Lighthouse, WebPageTest, or RUM): LCP, FID, CLS, TTFB, TBT, total page weight broken down by JS/CSS/images/fonts. Include Core Web Vitals targets: LCP <2500ms, FID <100ms, CLS <0.1.
Backend (APM, logs, or load testing): For each slow endpoint, record p50/p95/p99 latency, throughput (req/s), and error rate.
Database: List the top 3-5 slowest queries by total time (avg latency x call frequency), not single-execution time.
Record all baselines with timestamps, traffic level, and environment.
2. Frontend Audit
- Run bundle analyzer — identify largest modules and duplicate dependencies
- Verify code splitting: route-specific modules lazy-loaded, tree-shaking eliminating unused exports
- Identify unnecessary re-renders using React Profiler or equivalent
- Check for layout thrashing: interleaved DOM reads/writes in loops
- Verify images use modern formats (WebP/AVIF), correct sizing, and lazy loading
- Check fonts: limit weights, use
font-display: swap - Verify assets served from CDN with cache headers, no render-blocking resources in critical path
- Check for unnecessary API calls on page load
What ships with it
4 files 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.
- 1mo ago First seen · 108 lines · 29 tokens per session scan A 5a1c73ab906f
performance-audit is a skill published in the GitHub repository The-AI-Directory-Company/agents-and-skills (4 stars, last pushed 7mo ago), licensed MIT. It adds 29 tokens to every session and 1,328 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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