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 aj-geddes/useful-ai-prompts --skill profiling-optimizationgit clone --depth 1 https://github.com/aj-geddes/useful-ai-promptsWrote 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/aj-geddes/useful-ai-prompts/profiling-optimization)<a href="https://agentmods.dev/skills/aj-geddes/useful-ai-prompts/profiling-optimization"><img src="https://agentmods.dev/badge/skills/aj-geddes/useful-ai-prompts/profiling-optimization/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/aj-geddes/useful-ai-prompts/profiling-optimization"><img src="https://agentmods.dev/badge/skills/aj-geddes/useful-ai-prompts/profiling-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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 | $0.00039 | $0.00566 |
| Opus 5 | $0.00019 | $0.00283 |
| Sonnet 5 | $0.00008 | $0.00113 |
| Haiku 4.5 | $0.00004 | $0.00057 |
Grade A, and why
profiling-optimization 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Profiling & Optimization
Table of Contents
Overview
Profile code execution to identify performance bottlenecks and optimize critical paths using data-driven approaches.
When to Use
- Performance optimization
- Identifying CPU bottlenecks
- Optimizing hot paths
- Investigating slow requests
- Reducing latency
- Improving throughput
Quick Start
Minimal working example:
import { performance, PerformanceObserver } from "perf_hooks";
class Profiler {
private marks = new Map<string, number>();
mark(name: string): void {
this.marks.set(name, performance.now());
}
measure(name: string, startMark: string): number {
const start = this.marks.get(startMark);
if (!start) throw new Error(`Mark ${startMark} not found`);
const duration = performance.now() - start;
console.log(`${name}: ${duration.toFixed(2)}ms`);
return duration;
}
async profile<T>(name: string, fn: () => Promise<T>): Promise<T> {
const start = performance.now();
try {
return await fn();
} finally {
// ... (see reference guides for full implementation)
Reference Guides
Detailed implementations in the references/ directory:
| Guide | Contents |
|---|---|
| Node.js Profiling | Node.js Profiling |
| Chrome DevTools CPU Profile | Chrome DevTools CPU Profile |
| Python cProfile | Python cProfile |
| Benchmarking | Benchmarking |
| Database Query Profiling | Database Query Profiling |
| Flame Graph Generation | Flame Graph Generation |
Best Practices
✅ DO
- Profile before optimizing
- Focus on hot paths
- Measure impact of changes
- Use production-like data
- Consider memory vs speed tradeoffs
- Document optimization rationale
What ships with it
8 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.
- references/benchmarking.md 2.0 KB
- references/chrome-devtools-cpu-profile.md 1.0 KB
- references/database-query-profiling.md 1.8 KB
- references/flame-graph-generation.md 307 B
- references/nodejs-profiling.md 1.1 KB
- references/python-cprofile.md 1.1 KB
- scripts/validate-api.sh 440 B runs code
- templates/api-scaffold.yaml 467 B
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 · 96 lines · 39 tokens per session scan A 40d4679d624b
profiling-optimization is a skill published in the GitHub repository aj-geddes/useful-ai-prompts (338 stars, last pushed 6mo ago), licensed MIT. It adds 39 tokens to every session and 566 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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