explaingit

xinian-dada/fuck_my_shit_mountain

Analysis updated 2026-05-18

56HTMLAudience · developerComplexity · 2/5LicenseSetup · easy

TLDR

This is a prompt-driven skill package for AI coding assistants that runs structured, scored code audits across 15 dimensions like security and performance.

Mindmap

mindmap
  root((Audit Skill))
    What it does
      Structured code audits
      Scored reports
      Multiple audit modes
    Tech stack
      Prompt templates
      Markdown reports
      HTML reports
    Use cases
      Security review
      Performance review
      Maintainability review
    Audience
      Developers using AI tools

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Run a full 19-dimension audit of a codebase and get a scored report.

USE CASE 2

Run a focused audit mode, such as security or performance, on demand.

USE CASE 3

Generate an HTML report with sidebar navigation and color-coded score bars.

USE CASE 4

Install the skill into Claude Code, GitHub Copilot, Codex, or Gemini CLI.

What is it built with?

MarkdownHTML

How does it compare?

xinian-dada/fuck_my_shit_mountain2202alejandro/originlab-originpro-workflow-templatesachilles-0/red-giant-trapcode-toolkit-archive
Stars565656
LanguageHTMLHTMLHTML
Setup difficultyeasymoderateeasy
Complexity2/53/51/5
Audiencedeveloperresearchergeneral

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

How do you get it running?

Difficulty · easy Time to first run · 5min

Copy the skill directory into your AI tool's skills folder, results should be combined with human review.

MIT license: free to use, modify, and distribute, including commercially, as long as you keep the copyright notice.

In plain English

This repository is a skill package for AI coding assistants, designed to run structured code audits and produce detailed reports. The README is written primarily in Chinese. The core idea is a prompt-driven framework that you install into a supported AI tool, then invoke to analyze a codebase and get back a scored report organized by category. The framework supports 15 named audit modes that can be used individually or combined. These include modes focused on security, performance, testing quality, code maintainability, type safety, frontend state management, and backend API design, among others. A full mode runs all 19 dimensions at once. Each audit produces scores on a 0 to 10 scale per dimension, where 10 means clean code and lower scores indicate increasing problems. The output format can be plain Markdown, an HTML page with sidebar navigation and color-coded score bars, or both. The HTML demo page included in the repository is labeled clearly as a fictional example rather than a real audit result. The skill is structured as a directory containing a main entry file, 15 mode-specific prompt files, scoring rubrics defining severity levels and evidence standards, and report templates. Installation involves copying this directory into the skills folder of whichever AI tool you use: the README gives instructions for Claude Code, GitHub Copilot, Codex, and Gemini CLI. After loading, you invoke it by name and specify which mode, language, and output format you want. The project description says the audits are evidence-based and structured, and the disclaimer at the top of the README notes that results are for reference only and should always be combined with human review and real-world testing. The project is released under the MIT license.

Copy-paste prompts

Prompt 1
Install this audit skill package into my Claude Code skills folder and run a full audit.
Prompt 2
Run the security-focused audit mode on my repository and summarize the top issues.
Prompt 3
Generate an HTML audit report with color-coded score bars for my project.
Prompt 4
Which of the 15 audit modes should I run to check frontend state management?

Frequently asked questions

What is fuck_my_shit_mountain?

This is a prompt-driven skill package for AI coding assistants that runs structured, scored code audits across 15 dimensions like security and performance.

What language is fuck_my_shit_mountain written in?

Mainly HTML. The stack also includes Markdown, HTML.

What license does fuck_my_shit_mountain use?

MIT license: free to use, modify, and distribute, including commercially, as long as you keep the copyright notice.

How hard is fuck_my_shit_mountain to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is fuck_my_shit_mountain for?

Mainly developer.

Open on GitHub → Explain another repo

This repo across BitVibe Labs

Verify against the repo before relying on details.