performance-optimization

A measured process for improving software performance: establish a baseline, find the actual bottleneck, fix it, measure again, and add protection against regressions. It includes web-page metrics such as loading, interaction delay, and layout movement.

In plain words
What is it for?
Use it when there is a measured slowdown, a performance target, poor web vitals, high traffic, or evidence that a recent change caused a regression.
Why use it?
It prevents optimisation based only on guesses and checks whether a change actually improves the reported problem.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/douglance/sdlc-plugin/performance-optimization
Any agent
npx skills add douglance/sdlc-plugin --skill performance-optimization
Clone the repo
git clone --depth 1 https://github.com/douglance/sdlc-plugin

Made for: Claude Code, Codex.

Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,217 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00022 $0.02217
Opus 5 $0.00011 $0.01108
Sonnet 5 $0.00004 $0.00443
Haiku 4.5 $0.00002 $0.00222

Measured 2d ago against content hash c20e0ce74b83, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 2d 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.

.agents/skills/performance-optimization/SKILL.md · 284 lines

How it starts

The opening of the file, as written. The whole thing — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Performance Optimization

When to Use

  • Performance requirements exist in the spec (load time budgets, response time SLAs)
  • Users or monitoring report slow behavior
  • Core Web Vitals scores are below thresholds
  • You suspect a change introduced a regression
  • Building features that handle large datasets or high traffic

When NOT to use: Don't optimize before you have evidence of a problem.

Core Web Vitals Targets

Metric Good Needs Improvement Poor
LCP (Largest Contentful Paint) ≤ 2.5s ≤ 4.0s > 4.0s
INP (Interaction to Next Paint) ≤ 200ms ≤ 500ms > 500ms
CLS (Cumulative Layout Shift) ≤ 0.1 ≤ 0.25 > 0.25

The Optimization Workflow

1. MEASURE  → Establish baseline with real data
2. IDENTIFY → Find the actual bottleneck (not assumed)
3. FIX      → Address the specific bottleneck
4. VERIFY   → Measure again, confirm improvement
5. GUARD    → Add monitoring or tests to prevent regression

Step 1: Measure

Frontend:

# Lighthouse in Chrome DevTools (or CI)
# Chrome DevTools → Performance tab → Record
# Chrome DevTools MCP → Performance trace

# Web Vitals library in code
import { onLCP, onINP, onCLS } from 'web-vitals';

onLCP(console.log);
onINP(console.log);
onCLS(console.log);

Backend:

# Response time logging
# Application Performance Monitoring (APM)
# Database query logging with timing

# Simple timing
console.time('db-query');
const result = await db.query(...);
console.timeEnd('db-query');

Where to Start Measuring

Use the symptom to decide what to measure first:

What is slow?
├── First page load
│   ├── Large bundle? --> Measure bundle size, check code splitting
│   ├── Slow server response? --> Measure TTFB, check API/database
│   └── Render-blocking resources? --> Check network waterfall for CSS/JS blocking
├── Interaction feels sluggish
│   ├── UI freezes on click? --> Profile main thread, look for long tasks (>50ms)
│   ├── Form input lag? --> Check re-renders, controlled component overhead
│   └── Animation jank? --> Check layout thrashing, forced reflows
├── Page after navigation
│   ├── Data loading? --> Measure API response times, check for waterfalls
│   └── Client rendering? --> Profile component render time, check for N+1 fetches
└── Backend / API
    ├── Single endpoint slow? --> Profile database queries, check indexes
    ├── All endpoints slow? --> Check connection pool, memory, CPU
    └── Intermittent slowness? --> Check for lock contention, GC pauses, external deps

Read the full file on GitHub · 284 lines

Changes

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.

  1. 2d ago First seen · 284 lines · 22 tokens per session scan A c20e0ce74b83

Subscribe to this mod's changes

performance-optimization is a skill published in the GitHub repository douglance/sdlc-plugin (4 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 2,217 once invoked, about $0.0001 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.

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