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 DevelopersGlobal/ai-agent-skills --skill performance-optimizationgit clone --depth 1 https://github.com/DevelopersGlobal/ai-agent-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/developersglobal/ai-agent-skills/performance-optimization)<a href="https://agentmods.dev/skills/developersglobal/ai-agent-skills/performance-optimization"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/performance-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/developersglobal/ai-agent-skills/performance-optimization"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/performance-optimization.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 | $0.00023 | $0.00667 |
| Opus 5.5 | $0.00009 | $0.00267 |
| Sonnet 5.5 | $0.00005 | $0.00133 |
| Haiku 4.5 | $0.00002 | $0.00067 |
Grade A, and why
performance-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 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Premature optimization is the root of all evil. But ignoring performance until it's a crisis is equally harmful. This skill enforces data-driven optimization: profile first, optimize the bottleneck, measure the improvement.
When to Use
- When performance issues are reported in production
- Before optimizing any code (to ensure you're optimizing the right thing)
- When reviewing changes that touch performance-sensitive paths
Process
Step 1: Measure the Baseline
- Reproduce the performance issue reliably.
- Measure current performance: latency p50/p95/p99, throughput, memory, CPU.
- Profile to find the actual bottleneck — not where you think it is.
- Write the performance test you'll use to validate improvement.
Verify: You have concrete baseline numbers, not gut feelings.
Step 2: Identify the Real Bottleneck
- Use profiling tools: flame graphs, CPU profiles, memory profiles.
- Find the top 3 hotspots by actual execution time (not lines of code).
- The bottleneck is rarely where you expect it to be. Trust the data.
Verify: Bottleneck identified by profiling data, not assumption.
Step 3: Optimize Only the Bottleneck
- Fix only the profiled bottleneck — nothing else.
- Common optimizations by type:
- CPU: Algorithmic improvement (O(n²) → O(n log n)), caching, batching
- Memory: Streaming instead of buffering, object pooling, lazy loading
- I/O: Connection pooling, N+1 query elimination, caching, async/parallel calls
- AI: Prompt caching, batch inference, smaller models for simpler tasks
Verify: Change targets the profiled bottleneck, not speculative improvements.
Step 4: Measure the Improvement
- Run the same performance test from Step 1.
- Compare before vs. after metrics.
- If improvement < 20%: the optimization may not be worth the complexity.
Verify: Improvement measured with the same test harness as baseline.
Common Rationalizations (and Rebuttals)
| Excuse | Rebuttal |
|---|---|
| "I know where the bottleneck is" | You're probably wrong. Profile first. |
| "This is clearly slow" | "Clearly slow" rarely matches profiler output. Measure. |
| "We'll optimize later" | If it's slow enough to mention, it's slow enough to measure now. |
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 · 77 lines · 23 tokens per session scan A 7132348c08a7
performance-optimization is a skill published in the GitHub repository DevelopersGlobal/ai-agent-skills (177 stars, last pushed 5mo ago), licensed MIT. It adds 23 tokens to every session and 667 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 2026-08-30.
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