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/douglance/sdlc-plugin/performance-optimizationnpx skills add douglance/sdlc-plugin --skill performance-optimizationgit clone --depth 1 https://github.com/douglance/sdlc-pluginWhat 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 | $0.00022 | $0.02217 |
| Opus 5 | $0.00011 | $0.01108 |
| Sonnet 5 | $0.00004 | $0.00443 |
| Haiku 4.5 | $0.00002 | $0.00222 |
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.
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
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.
- 2d ago First seen · 284 lines · 22 tokens per session scan A c20e0ce74b83
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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…