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wtf is fastmoss-rpa-skills?

liangdabiao/fastmoss-rpa-skills — explained in plain English

Analysis updated 2026-05-18

19PythonAudience · dataComplexity · 3/5Setup · moderate

TL;DR

A Python toolkit that automates a real logged in browser to pull FastMoss TikTok Shop data into CSV files and generate Markdown analysis reports.

Mindmap

mindmap
  root((fastmoss-rpa-skills))
    What it does
      Scrapes FastMoss rankings
      Reuses your browser login
      Generates CSV and reports
    Tech stack
      Python
      Kimi WebBridge
      Chrome or Edge
    Use cases
      Track TikTok Shop product rankings
      Compare creator rankings across countries
      Monitor shop sales trends
    Audience
      Ecommerce analysts
      TikTok Shop sellers
    Setup
      Kimi WebBridge daemon running
      Logged in FastMoss account
      No headless browser needed

Code map

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Why would anyone build with this?

REASON 1

Pull FastMoss product, creator, and shop rankings into CSV files.

REASON 2

Compare TikTok Shop rankings across sixteen different countries at once.

REASON 3

Track a single shop's new product launches over multiple days.

REASON 4

Generate a Markdown analysis report summarizing trends across rankings.

What's in the stack?

PythonKimi WebBridgeChromeEdge

How it stacks up

liangdabiao/fastmoss-rpa-skills16nic/comfyui-agnes-ai6c696e68/gpt_signup_hybrid
Stars191919
LanguagePythonPythonPython
Setup difficultymoderatemoderatehard
Complexity3/52/54/5
Audiencedatavibe coderdeveloper

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

How do you spin it up?

Difficulty · moderate Time to first run · 30min

Requires installing Kimi WebBridge, an active FastMoss login in a real browser, and Python 3.8+.

Wtf does this do

fastmoss-rpa-skills is a collection of automation tools that pull data out of FastMoss, a data analysis platform for TikTok Shop, and turn it into spreadsheet files and written reports. It covers all seven of FastMoss's core sections: products, creators, shops, livestreams, videos and creative assets, an advertising engine, and overall market data. Instead of logging in on its own or working around FastMoss's login system, the tool drives the user's own already logged in Chrome or Edge browser through something called Kimi WebBridge, a connector that lets scripts control a real browser session. This means a person only has to sign into FastMoss once in their normal browser, and every script afterward reuses that same session rather than needing separate credentials, tokens, or captcha solving. Six of the seven modules work by reading data directly out of the web page's HTML tables, and are built to adapt automatically if FastMoss changes its page layout, since they read column headers dynamically rather than relying on fixed positions. The seventh module, covering overall market data, instead calls FastMoss's own JSON data endpoints directly from within the browser page, which the README says is faster and more reliable since it skips waiting for the page to render. Each module is organized as a self contained skill folder containing a documentation file, Python scripts for scraping and analysis, and reference notes. Every ranking script can pull data for multiple countries or product categories in a single run, covering sixteen markets including the United States, Indonesia, Thailand, and Malaysia, and can output both raw CSV files and a combined Markdown analysis report summarizing things like country distribution, category breakdowns, and products that repeat across multiple rankings. Running the tool requires Python 3.8 or newer, a modern Chrome or Edge browser, the Kimi WebBridge daemon and its browser extension installed and running, and an existing FastMoss account, since the depth of data available depends on the account's subscription level. The README states that no Node.js, Selenium, Playwright, Puppeteer, or headless browser software is needed, since the real browser handles all the page rendering itself.

Yoink these prompts

Prompt 1
Help me set up Kimi WebBridge and connect it to my browser for fastmoss-rpa-skills.
Prompt 2
Explain how the fastmoss-market skill differs from the other six DOM scraping skills.
Prompt 3
Show me how to run fastmoss_filtered.py to compare product rankings across countries.
Prompt 4
Walk me through generating a combined analysis.md report from multiple FastMoss CSV outputs.

Frequently asked questions

wtf is fastmoss-rpa-skills?

A Python toolkit that automates a real logged in browser to pull FastMoss TikTok Shop data into CSV files and generate Markdown analysis reports.

What language is fastmoss-rpa-skills written in?

Mainly Python. The stack also includes Python, Kimi WebBridge, Chrome.

How hard is fastmoss-rpa-skills to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is fastmoss-rpa-skills for?

Mainly data.

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