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 subhansh-dev/agent-maxxing --skill performance-patternsgit clone --depth 1 https://github.com/subhansh-dev/agent-maxxingWrote 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/subhansh-dev/agent-maxxing/performance-patterns)<a href="https://agentmods.dev/skills/subhansh-dev/agent-maxxing/performance-patterns"><img src="https://agentmods.dev/badge/skills/subhansh-dev/agent-maxxing/performance-patterns/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/subhansh-dev/agent-maxxing/performance-patterns"><img src="https://agentmods.dev/badge/skills/subhansh-dev/agent-maxxing/performance-patterns.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 | $0.00022 | $0.00477 |
| Opus 5.5 | $0.00009 | $0.00191 |
| Sonnet 5.5 | $0.00004 | $0.00095 |
| Haiku 4.5 | $0.00002 | $0.00048 |
Grade A, and why
performance-patterns 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.
What it actually says
Performance Patterns
Frontend Performance
Lazy Loading
const HeavyComponent = lazy(() => import('./HeavyComponent'))
Load components only when needed. Use for routes, modals, below-fold content.
Memoization
const memoizedValue = useMemo(() => computeExpensiveValue(a, b), [a, b])
const memoizedFn = useCallback(() => doSomething(a, b), [a, b])
Cache expensive computations and function references.
Code Splitting
Split your bundle by route or feature. Users only download what they need.
Image Optimization
- Use WebP/AVIF formats
- Lazy load below-fold images
- Use responsive images (srcset)
- Compress images (tinyPNG, Sharp)
CSS Performance
- Use
transformandopacityfor animations (GPU-accelerated) - Avoid
layouttriggers (width, height, margin, padding) - Use
will-changesparingly - Minimize repaints with
contain
Backend Performance
Caching
- Cache database queries (Redis, in-memory)
- Cache API responses (HTTP caching headers)
- Cache expensive computations (memoization)
- Cache rendered pages (SSR cache)
Database Optimization
- Add indexes for frequent queries
- Use connection pooling
- Batch inserts/updates
- Avoid N+1 queries
- Use EXPLAIN ANALYZE
Async Operations
- Don't block the event loop
- Use worker threads for CPU-intensive tasks
- Stream large responses
- Queue background jobs
Measurement
- Lighthouse — overall performance score
- Core Web Vitals — LCP, FID, CLS
- Chrome DevTools — Performance tab, Lighthouse tab
- webpack-bundle-analyzer — bundle size visualization
- ab / wrk — load testing
Common Anti-Patterns
- Loading everything upfront
- Not caching expensive operations
- Synchronous database calls in loops
- Large bundle sizes (no code splitting)
- Unoptimized images (no lazy loading, wrong format)
- Layout thrashing (reading layout then writing)
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 · 75 lines · 22 tokens per session scan A c05b69547b42
performance-patterns is a skill published in the GitHub repository subhansh-dev/agent-maxxing (2 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 477 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 2026-08-31.
Other skills, from other repositories
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
review
Diff-scoped correctness review that reads the codebase around the change — callers, contracts, and invariants — and returns line-anchored findings ranked by severity with confidence, then a merge-risk verdict. Use for reviewing a PR, diff, or named change set. Not for style review, approvals-as-rubber-stamp, or…
simplify
Review recent code changes for reuse, code quality, and efficiency, then directly apply straightforward cleanup improvements. Use when the user wants a post-implementation cleanup pass, pre-PR polish, or asks to simplify/refine recent changes. Invoke with /simplify or /simplify .
find-simplifications
Use for a periodic repo-wide sweep of qwen-code for accumulated excess surface — dead components and files, orphaned locale keys, exports nothing consumes, added-then-removed scaffolding — filing candidates on a tracking issue and landing only what a maintainer has said yes to. Repo-wide and evidence-first; every…
review-this-branch
Run the nohuman review gate (fresh-session adversarial reviewer + tamper guard) over the current branch or a GitHub pull request, with no server, no database, and no onboarding, and relay the pass/fail checklist.
fabric-spec
Starts a persistent Pi Fabric spec supervisor that audits the main session against a feature design spec and steers only when a requirement lacks verified evidence. Use for strict, unblocked spec compliance while the main agent keeps full freedom to orchestrate.