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-optimization-guidegit 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-optimization-guide)<a href="https://agentmods.dev/skills/the-ai-directory-company/agents-and-skills/performance-optimization-guide"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-skills/performance-optimization-guide/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-optimization-guide"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-skills/performance-optimization-guide.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 | $0.00034 | $0.01585 |
| Opus 5 | $0.00017 | $0.00792 |
| Sonnet 5 | $0.00007 | $0.00317 |
| Haiku 4.5 | $0.00003 | $0.00159 |
Grade A, and why
performance-optimization-guide 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 9d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimization Guide
Before you start
Gather the following from the user. If anything is missing, ask before proceeding:
- What is slow? — Specific page, endpoint, query, or user action with measured latency
- What is the target? — Quantified goal (e.g., "LCP under 2s", "API p95 under 200ms")
- What does the profile show? — Existing flame graphs, traces, or Lighthouse reports (if available)
- What is the stack? — Frontend framework, backend runtime, database, hosting/CDN
- What has been tried? — Previous optimization attempts and their measured results
This skill assumes a performance audit has already identified the bottleneck. If the bottleneck is unknown, run a performance audit first.
Optimization procedure
1. Reproduce and Baseline
Before changing anything:
- Reproduce the slow behavior in a consistent environment (same data, same traffic, same hardware)
- Record the baseline metric with the exact tool you will use to verify the fix
- Run the measurement 3 times minimum — single measurements are unreliable due to variance
- Document: metric name, value, tool, timestamp, conditions
Never optimize without a reproducible baseline. If you cannot measure it, you cannot verify the fix.
2. Profile to Find the Root Cause
Use the appropriate profiler for the layer:
Frontend rendering:
- Chrome DevTools Performance panel — record the slow interaction, look for long tasks (>50ms)
- React Profiler / Vue Devtools — identify components re-rendering unnecessarily
- Lighthouse — automated scoring for LCP, CLS, TBT, TTFB
JavaScript bundle:
- Webpack Bundle Analyzer,
next build --analyze, or equivalent — find largest modules - Source map explorer — trace bundle size to specific imports
- Check for duplicate dependencies bundled multiple times
Backend latency:
- APM tool (Datadog, New Relic, OpenTelemetry) — distributed trace of the slow request
- Language profiler (Node.js
--prof, PythoncProfile, Gopprof) — CPU time per function - Log timestamps at each stage of the request to find where time accumulates
What ships with it
3 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.
- 9d ago First seen · 136 lines · 34 tokens per session scan A 4443e5c14870
performance-optimization-guide is a skill published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 1,585 once invoked, about $0.0002 per session on Opus 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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