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 agentmods add skills/int2t05/engineering-skills/performancenpx skills add int2t05/engineering-skills --skill performancegit clone --depth 1 https://github.com/int2t05/engineering-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/int2t05/engineering-skills/performance)<a href="https://agentmods.dev/skills/int2t05/engineering-skills/performance"><img src="https://agentmods.dev/badge/skills/int2t05/engineering-skills/performance.svg" alt="Measured on agentmods" 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 | $0.00060 | $0.01500 |
| Opus 5 | $0.00030 | $0.00750 |
| Sonnet 5 | $0.00012 | $0.00300 |
| Haiku 4.5 | $0.00006 | $0.00150 |
Grade A, and why
performance 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 3d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimization
Measure before optimizing. Performance work without measurement is guessing — and guessing leads to premature optimization that adds complexity without improving what matters. Profile first, identify the actual bottleneck, fix it, measure again. Optimize only what measurements prove matters.
When to use
- Performance requirements exist in the spec (load time budgets, response time SLAs)
- Users or monitoring report slow behavior, or Core Web Vitals are below thresholds
- You suspect a change introduced a regression
- Building features that handle large datasets or high traffic
- Triggers on "webperf", "performance regression", "慢", "性能优化"
Not for: Don't optimize before you have evidence of a problem. Premature optimization adds complexity that costs more than the performance it gains. Diagnosing a bug (use debugging).
Steps
1. Measure — establish a baseline with real data
Two complementary approaches — use both:
- Synthetic (Lighthouse, DevTools Performance tab): controlled, reproducible. Best for CI regression detection and isolating specific issues.
- RUM (
web-vitalslibrary, CrUX): real user data in real conditions. Required to validate that a fix actually improved user experience.
Use the same command, same conditions, same fixed budget (wall-clock, sample count, or request count) every time. A baseline taken on a cold cache against a result taken on a warm one measures the cache, not your change.
2. Identify the actual bottleneck (not assumed)
Profile before proposing a fix. Let the symptom tell you where to look — the symptom→cause decision tree and per-category investigation tables (frontend LCP/CLS/INP, backend N+1/memory/CPU/latency) are in references/bottlenecks.md. Core Web Vitals "Good" thresholds (LCP ≤ 2.5s, INP ≤ 200ms, CLS ≤ 0.1) and performance budgets are there too.
3. Fix the specific bottleneck
Address what measurements proved matters. Common anti-patterns and their fixes — N+1 queries, unbounded data fetching, missing image optimization, unnecessary re-renders, large bundle size, missing caching — are cataloged with code in references/anti-patterns.md.
What ships with it
5 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.
- 3d ago First seen · 88 lines · 0 tokens per session scan A ae18130fbcd3
performance is a skill published in the GitHub repository int2t05/engineering-skills (3 stars, last pushed 15d ago), licensed MIT. It adds 60 tokens to every session and 1,500 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
writing-skills
Use when creating new skills, editing existing skills, or verifying skills work before deployment.
finishing-a-development-branch
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work.
test-driven-development
Use when implementing any feature or bugfix, before writing implementation code.
receiving-code-review
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation.
writing-plans
Use when you have a spec or requirements for a multi-step task, before touching code.