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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsYou can find oversized image assets and byte-for-byte duplicates in a Node.js project with a read-only scan: use filesystem metadata for file sizes, sharp for image headers and dimensions, and Node.js crypto to hash same-size candidates. Treat “oversized” as a project policy, not a universal cutoff; report findings for review rather than deleting or rewriting files.
What the audit measures
File size and image dimensions are different signals. Filesystem metadata reports a file’s byte count; image metadata reports properties such as format, width, and height. An image can be large in bytes without having especially large dimensions, or have many pixels while using an efficient encoding. Flag and report these measurements separately.
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High dimensions are not automatically a problem: source artwork, high-density displays, zoom, or print workflows may need large originals. Choose thresholds that fit the project and use the audit to identify assets for review, not to make automatic quality decisions.
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Choose directories deliberately
Scan the asset roots that matter to the project. Exclude dependencies, generated directories, caches, and build output unless those are part of the audit’s purpose. Retain paths relative to the project root so findings are easy to locate.
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Traverse regular files and do not follow symbolic links into other locations by accident. Decide explicitly whether linked files are skipped or handled as links; report the policy and any skipped entries. Files can change or become unreadable during a scan, so handle errors per file and include error and skipped counts in the final report.
Make thresholds configurable
Provide configurable byte and dimension limits, such as a maximum file size and maximum width or height. Record the applied values alongside each report. The Node.js and sharp documentation describes APIs and capabilities, not a universal definition of an oversized image, so thresholds should reflect the project’s delivery targets and use cases.
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Read image metadata without decoding pixels
With sharp, metadata() reads header information without decoding compressed pixel data. Record the detected format, width, height, and page count when present; this makes the scan useful for a first-pass inventory without performing a full image decode. See the sharp metadata API.
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sharp lists JPEG, PNG, WebP, GIF, AVIF, TIFF, and SVG as readable formats in its project documentation. Make the report’s coverage explicit: files outside the formats the implementation supports should be counted or listed as skipped rather than silently treated as audited images.
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Animated and multipage files may contain more than one frame or page. Record the page count when metadata provides it, and state how the audit treats such files. Orientation can affect how dimensions should be understood; sharp exposes orientation information and documents autoOrient metadata for orientation-adjusted dimensions. Choose whether reported width and height are stored dimensions or orientation-adjusted dimensions, and label them accordingly.
Find exact duplicates with streaming hashes
Group files by byte size first, then hash only files in groups with more than one member. Files of different sizes cannot be byte-identical, so this narrows the candidate set. Stream each candidate through Node.js crypto.createHash('sha256') rather than loading entire files into memory. Node.js documents a SHA-256 file-stream example in its crypto API documentation.
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Report matching size-and-digest groups with every relative path. A matching cryptographic digest is a practical way to identify byte-for-byte duplicates; it does not identify images that merely look alike after resizing, re-encoding, or metadata changes. Do not select a file to delete automatically: the scan identifies candidates, while a person should determine which copy, if any, is safe to remove.
Keep the first run read-only
Limit the audit to directory traversal, filesystem metadata reads, image metadata reads, hashing reads, and report output. Send the report to standard output by default, or let the user specify a destination separate from the assets being scanned. Writing a JSON report is still an output operation, even when it does not modify image files.
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Do not add deletion, renaming, compression, or rewriting to the first-run audit. Reads and hashing can still consume CPU and I/O, and files may change while the scan is underway, so the run should not be described as risk-free in every environment.
Open or read files directly and handle errors rather than checking access first. The Node.js filesystem documentation warns: “Do not use fs.access() to check for the accessibility of a file before calling fs.open(), fs.readFile() or fs.writeFile(). Doing so introduces a race condition, since other processes may change the file’s state between the two calls.”
What a useful report contains
Make the output reviewable and reproducible. Include the scan scope and applied thresholds in addition to findings.
- Summary counts for files scanned, image candidates, flagged files, duplicate groups, skipped files, and errors.
- For each flagged image: relative path, file size in bytes, dimensions, detected format, and page count when present.
- For each exact-duplicate group: all paths, byte size, and SHA-256 digest.
- The configured byte and dimension thresholds, supported-format coverage, and symbolic-link and multipage handling policies.
A JSON report can support later review or automation, but keep remediation as a separate, deliberate step. This audit provides evidence for decisions; it does not establish that an image is unsuitable or that removing a duplicate is safe.
Implementation prerequisites
Node.js supplies the filesystem and cryptographic APIs used for this workflow; sharp is an npm package for image metadata and processing. sharp’s homepage states that it can be used with runtimes providing Node-API v9, including Node.js 20.9.0 and later. Check the current sharp documentation and npm package page before choosing a package installation and runtime combination, since package compatibility information may change.
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