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what is x-high-exposure-to-obsidian-skill fr?

kimberlying/x-high-exposure-to-obsidian-skill — explained in plain English

Analysis updated 2026-05-18

31PythonAudience · writerLicense

tl;dr

A local Mac tool that finds an X account's highest viewed recent posts and saves them into Obsidian using the Web Clipper.

vibe map

mindmap
  root((X High Exposure to Obsidian))
    What it does
      Finds top viewed posts
      Saves to Obsidian
      Records post metadata
    Tech stack
      Python
      Obsidian Web Clipper
      Chrome or Dia
    Use cases
      Content swipe file
      Multi account archiving
      Codex skill integration
    Requirements
      macOS only
      Logged in browser
      Obsidian vault open

Code map

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filefunction / class

what do people make with this?

VIBE 1

Automatically archive the top viewed recent posts from a chosen X account into Obsidian.

VIBE 2

Build a swipe file of proven high-performing content ideas without manually scrolling X.

VIBE 3

Run the tool across several accounts at once to compare high exposure posts side by side.

what's the stack?

PythonObsidianChrome

how it stacks up fr

kimberlying/x-high-exposure-to-obsidian-skillafspies/clawdmojialluxio/alluxio-py
Stars313131
LanguagePythonPythonPython
Last pushed2025-09-29
MaintenanceQuiet
Setup difficultyeasymoderate
Complexity2/53/5
Audiencewritergeneraldata

Figures from each repo's GitHub metadata at analysis time.

in plain english

This is a local automation tool for content creators and researchers who want to keep a personal archive of the best performing posts from specific X, formerly Twitter, accounts. You give it a link to someone's profile, and it finds their recent posts that have already gotten a high number of views, then saves those posts into Obsidian, a note taking app, using the official Obsidian Web Clipper. It looks at a chosen account's posts from the last 30 days, and if there are not enough qualifying posts in that window it will look back as far as 90 days. By default it only keeps posts with more than 90,000 views, and saves up to the top 5 highest viewed posts per account into a folder named after that account inside your Obsidian vault. Along with the saved note, it records details like the post's view count, author, original link, and posting time. The tool works by reading your browser's existing login session in Chrome or the Dia browser, so it can only see posts you could already view yourself while logged in. It does not store your password and does not upload your cookies anywhere, everything runs locally on your Mac. You can run it in a dry run mode first to just preview which posts would be saved, before switching it to actually write files into Obsidian. It also supports processing several accounts in one run. The tool can additionally be installed as a skill for Codex, so instead of running a command directly, you can simply describe what you want in natural language. The README is explicit that this tool is meant only for accessing public content you can already reach with your own account, and warns against using it to get around access controls or scrape private accounts. It is adapted from an earlier open source project and follows the MIT license from that original work.

prompts (copy fr)

prompt 1
Walk me through setting up the dry run mode to preview high exposure posts before saving.
prompt 2
Explain how this script decides which posts count as high exposure using the view threshold.
prompt 3
Help me install this as a Codex skill so I can trigger it with natural language.
prompt 4
How does this tool read my browser login session without storing my password or cookies?

Frequently asked questions

what is x-high-exposure-to-obsidian-skill fr?

A local Mac tool that finds an X account's highest viewed recent posts and saves them into Obsidian using the Web Clipper.

What language is x-high-exposure-to-obsidian-skill written in?

Mainly Python. The stack also includes Python, Obsidian, Chrome.

Who is x-high-exposure-to-obsidian-skill for?

Mainly writer.

peek the repo → explain another one

This repo across BitVibe Labs

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