Categorize bookmarks

[aaditkamat]

Summary by Sourcery

Automatically categorize top-level bookmark entries into folders based on simple keyword heuristics while preserving the original raw list.

New Features:

• Introduce a raw bookmark list and generate a categorized bookmark structure from it.
• Add keyword and folder-name based logic to assign uncategorized bookmark links into appropriate existing folders or an Uncategorized folder.

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[sourcery-ai]

Reviewer's Guide

Introduces a derived bookmarks structure from rawBookmarks by auto-categorizing top-level bookmark leaf nodes into existing folders using keyword-based heuristics, with fallbacks and an Uncategorized bucket.

File-Level Changes

Change	Details	Files
Rename the original bookmarks array to represent raw, uncategorized bookmark data and introduce a derived, categorized bookmarks export.	Renamed the existing bookmarks array to rawBookmarks while preserving its contents. Added a new exported bookmarks array that is the result of running a categorization function over rawBookmarks.	src/data/data.tsx
Add helper and categorization logic to group leaf bookmarks into folders using keyword heuristics and fallbacks.	Implemented findFolderByName to recursively locate folder nodes by name within a nested bookmark tree. Defined a keywordToFolder mapping object that relates keyword sets to target folder names. Implemented a categorize function that deep-copies rawBookmarks, separates folder nodes from leaf nodes, and assigns leaves to folders based on keyword matching. Added a fallback mechanism that searches for known folder names in bookmark text if keyword matching fails. Introduced an Uncategorized folder that is created and used when no suitable folder is found for a bookmark.	src/data/data.tsx

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[sourcery-ai]

Hey - I've left some high level feedback:

• In findFolderByName, the if (node.children && node.name === targetName) check means folders with an empty or undefined children array won't be matched as targets; consider matching by name regardless of children and then recursing into children when present.
• The deep copy via JSON.parse(JSON.stringify(raw)) in categorize may be unnecessarily costly and will strip any non-plain data (e.g., Dates); if possible, prefer a more explicit cloning strategy or structuredClone to better control performance and data fidelity.
• The keyword-to-folder mapping is encoded as comma-separated keyword strings in keywordToFolder, which makes the lookup logic a bit opaque; refactoring this into a clearer structure (e.g., { folderName: [keywords...] }) would improve readability and maintainability of the categorization heuristic.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- In `findFolderByName`, the `if (node.children && node.name === targetName)` check means folders with an empty or undefined `children` array won't be matched as targets; consider matching by `name` regardless of `children` and then recursing into `children` when present.
- The deep copy via `JSON.parse(JSON.stringify(raw))` in `categorize` may be unnecessarily costly and will strip any non-plain data (e.g., Dates); if possible, prefer a more explicit cloning strategy or `structuredClone` to better control performance and data fidelity.
- The keyword-to-folder mapping is encoded as comma-separated keyword strings in `keywordToFolder`, which makes the lookup logic a bit opaque; refactoring this into a clearer structure (e.g., `{ folderName: [keywords...] }`) would improve readability and maintainability of the categorization heuristic.

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