Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/textura-agency/next16-claude-starternpx agentmods add skills/textura-agency/next16-claude-starter/optimize-performanceWrote 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/textura-agency/next16-claude-starter/optimize-performance)<a href="https://agentmods.dev/skills/textura-agency/next16-claude-starter/optimize-performance"><img src="https://agentmods.dev/badge/skills/textura-agency/next16-claude-starter/optimize-performance/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/textura-agency/next16-claude-starter/optimize-performance"><img src="https://agentmods.dev/badge/skills/textura-agency/next16-claude-starter/optimize-performance.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 | $0.00205 | $0.03906 |
| Opus 5.5 | $0.00082 | $0.01562 |
| Sonnet 5.5 | $0.00041 | $0.00781 |
| Haiku 4.5 | $0.00020 | $0.00391 |
Grade A, and why
optimize-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 11d 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 — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimise page performance
This starter is built for animation-heavy marketing sites: springs on scroll,
text engines, Lenis smooth scroll, full-bleed imagery, often a WebGL scene. That
combination has characteristic failures, and they are not the ones people go
looking for. This skill was rewritten from scroll passes on ~50 production
sites built from this starter; each lever says whether it is a rule (held on
≥ 3 sites) or observed (1–2). Read obsidian/knowledge/fix-catalog.md and
obsidian/knowledge/pitfalls.md before fixing.
The loop is the skill. Build → measure → attribute → fix one thing → re-measure. Never skip to the fix; never report a win you did not measure.
If the project renders a three.js / WebGL scene (
threeinpackage.json, or a canvas with a render loop) — hard rule 14 sends you tooptimize-3d-scenefirst. Come back here for the page around the scene.
The bar
ideal on PC and mobile, cold and warm: no frame over 50 ms, ≤ 1 % dropped
frames, p99 ≤ 33 ms (budgets against 60 Hz even on a 120 Hz panel). smooth is
no frame > 100 ms and ≤ 3 % dropped; anything else is janky. A 50 ms frame is
three dropped frames at 60 Hz — where a hitch stops being subliminal.
0. Rules of measurement
- Measure the build, never
next dev.yarn build && yarn start. Kill the old server by port first; start the new one only if the build succeeded. - Baseline before you touch anything.
- 3 runs, medians; a
1/3freeze may be noise — re-run before fixing. A result near the 1 % line is a draw: A/B the two builds back to back (interleaved, ≥ 2 rounds) and compare per-frame work, not one record. - Throttle the CPU (4×) on the phone pass, and emulate 4G — localhost
hides lazy-load freezes; any
fetchedduring a pass is a freeze waiting for a slower link. - Change one thing at a time, same config before and after.
- Tracing perturbs what it measures. Use a trace or profile to explain a freeze, never to measure one.
- A busy machine fakes regressions. Above a load average of ~15 a scroll verdict is unreliable (GPU contention reads as janky) — re-run, don't act. Probes use a plain Chrome UA (the default puppeteer UA gets the robot form).
What ships with it
2 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.
- 11d ago Changed · +86 lines · +41 tokens per session 7c1aa174cd15
- 1mo ago First seen · 164 lines · 164 tokens per session scan A 4a70aabeaa37
optimize-performance is a skill published in the GitHub repository textura-agency/next16-claude-starter (137 stars, last pushed 3d ago), licensed Unlicense. It adds 205 tokens to every session and 3,906 once invoked, about $0.0008 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 2026-09-18.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
diagnose
Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.
repro-admin
Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
deck-retro
Run a fully local agent-deck retrospective over the user's own transcripts, Recall index and logs. Use when a user wants to find recurring failures or take their own finding from investigation through synthetic reproduction, a user-filed issue, a test-first fix and contributor PR handoff. Also use for weekly usage…