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 sanky369/vibe-building-skills --skill performance-optimizationgit clone --depth 1 https://github.com/sanky369/vibe-building-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/sanky369/vibe-building-skills/performance-optimization)<a href="https://agentmods.dev/skills/sanky369/vibe-building-skills/performance-optimization"><img src="https://agentmods.dev/badge/skills/sanky369/vibe-building-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/sanky369/vibe-building-skills/performance-optimization"><img src="https://agentmods.dev/badge/skills/sanky369/vibe-building-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 | $0.00155 | $0.02158 |
| Opus 5 | $0.00077 | $0.01079 |
| Sonnet 5 | $0.00031 | $0.00432 |
| Haiku 4.5 | $0.00015 | $0.00216 |
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 10d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimization
Make the interface feel instant and measure honestly: find where users wait, decide per wait whether to eliminate it (optimistic UI, preloading, batching) or mask it (skeletons, progressive loading), and implement the fix. The prime directive: perceived latency outranks actual latency — a 3-second operation that responds instantly to the click beats a 1-second one that freezes — but never fake what you can't deliver (no invented progress numbers, no optimistic UI where rollback would burn the user). Deliver an audit ranked by user impact plus working code.
When to use / when not to
Use for: sluggish-feeling interactions, optimistic UI, Core Web Vitals remediation, preloading/anticipatory loading, request batching/dedup, layout-shift fixes, perceived-performance strategy.
Hand off instead when the real need is:
- Choosing/designing the loading indicators themselves →
skills/frontend-design/loading-states - Animation jank from non-compositor properties →
skills/frontend-design/interaction-physics(motion spec) orskills/frontend-design/design-engineer-mindset(rendering pipeline, layout thrashing) - The failure/rollback messaging your optimistic UI needs →
skills/frontend-design/error-handling-recovery - Backend/query latency itself — flag it, but that's outside this skill's frontend scope
Step 0 — Inspect the codebase, then ask only what's left
- Find the data layer: raw
fetch/axiosvs a data library (TanStack Query, SWR, Apollo, RTK Query). This decides implementation strategy — libraries get optimistic updates and dedup through their APIs; hand-rolled fetching may need those primitives added. - Find waiting patterns: mutations that disable the UI until the server responds, serial
awaitchains that could parallelize, duplicate fetches of the same resource, effects re-fetching on every render. - Find layout-shift sources: images without dimensions/
aspect-ratio, content injected above existing content, fonts swapping without fallback metrics, ad/embed slots without reserved space. - Look for existing measurement:
web-vitalspackage, Lighthouse CI, RUM/analytics. If the project runs in this environment, get real numbers (Lighthouse or a profile) before and after — never quote scores you didn't measure. - Check bundle signals when load speed is the complaint: build output sizes, missing route-level code splitting, heavyweight dependencies imported globally.
What ships with it
1 file 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.
- 10d ago First seen · 126 lines · 155 tokens per session scan A 3cd64dda35cf
performance-optimization is a skill published in the GitHub repository sanky369/vibe-building-skills (30 stars, last pushed 2mo ago), licensed MIT. It adds 155 tokens to every session and 2,158 once invoked, about $0.0008 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 2026-08-30.
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