Next.js Performance Audit & Fix Plan

    1

    Executable audit of your actual Next.js codebase: a bundled read-only scanner maps performance findings to files and lines, ranked by impact, then a staged fix plan and a before/after VERIFY pass.

    $12

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    Next.js Performance Audit & Fix Plan

    Example session with this skill installed

    Audit the Next.js app in ./web. App Router, Next 14, hosted on Vercel. LCP is our problem. Audit only, no patches.

    • Read your context and instructions
    • Compiled the next js performance
    • Generated the document

    Next.js Performance Audit - fixture project (real scanner run on a teaching fixture)
    Router: app | Files scanned: 4 | Findings: 6 (high 3, medium 2, low 1)

    1. [HIGH] wide-client-boundary - app/layout.tsx:1 - root layout is a client component; the whole segment ships as client JS. Direction: move interactivity to a small leaf client component.
    2. [HIGH] barrel-import - app/layout.tsx:2 - root import of lodash in the root layout lands in every route's bundle. Direction: deep import or optimizePackageImports.
    3. [HIGH] barrel-import - app/components/Chart.tsx:1 - root import of @mui/material.
    4. [MEDIUM] raw-img-tag - app/page.tsx:12 - hero <img> bypasses next/image (also the likely LCP element).
    5. [MEDIUM] missing-optimize-package-imports - next.config.js.
    6. [LOW] possible-waterfall - app/page.tsx:6 - 3 consecutive awaits (heuristic, low confidence; check dependencies).

    No savings figures claimed - sizes are measured in VERIFY, not promised in AUDIT.

    layout.tsx

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Best-practice guides tell your agent what good code looks like; this skill inspects the code you actually have. It opens with a 5-question Intake (router, versions, hosting, main pain, desired output) that changes which checks run and which reference applies, then runs a bundled Python scanner (standard library only, fully offline, read-only) over your Next.js project. The scanner detects barrel imports from heavy packages, overly wide 'use client' boundaries, raw tags, missing optimizePackageImports, eager heavy imports, and possible sequential-await waterfalls (labeled low-confidence heuristics). AUDIT mode delivers a findings report mapped to files and lines, ranked by impact. FIX PLAN mode turns findings into staged, minimal patch plans with risk, order, verification method, and rollback for each item - patches are only ever applied after your explicit per-batch approval. VERIFY mode re-runs the scanner and compares build output before/after, reporting an honest delta: what cleared, what remains, what is new, and what the evidence does and does not prove. Covers App Router and Pages Router, Vercel and self-hosted. No unmeasured savings claims, ever: sizes and timings appear only when measured.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Security scan passed 4 days ago

    Requires

    Python 3 (standard library only) to run the bundled scanner
    A local Next.js project directory the agent may read

    Listed4 days ago

    What's inside

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