# mukul975/Anthropic-Cybersecurity-Skills SLOC report

> As of 2026-09-13 (commit 54a798831d22), mukul975/Anthropic-Cybersecurity-Skills contains 481,404 total lines: 213,879 code, 186,977 comments, 80,548 blank, across 3,694 files in 4 languages (top: Python 97.5%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

mukul975/Anthropic-Cybersecurity-Skills has 213,879 source lines of code out of 481,404 total lines across 3,694 files, counted from the 54a798831d2266a3ca61ce68a7acb80b81160d57 ref at commit 54a798831d22 by the OctoCounts tokei engine on 2026-09-13. The report is cached by commit, tokei version, and analysis options, so recounting the same revision returns exactly these numbers.

OctoCounts produced this report by resolving mukul975/Anthropic-Cybersecurity-Skills to commit 54a798831d22, downloading the repository source archive, and counting every source file with tokei, the open-source line counter written in Rust. The table below breaks the count down by programming language into files, total lines, code lines, comment lines, and blank lines, so the figures can be compared across languages and projects. Results are cached by commit, tokei version, and analysis options, so counting the same revision again reproduces exactly these numbers.

## Repository size insights

This is a large codebase by counted code lines. Code represents 44.4% of all lines, comments represent 38.8%, and the repository averages 58 code lines per file. Python accounts for 97.5% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 1,103 | 246,058 | 208,581 | 3,588 | 33,889 |
| JSON | 5 | 4,658 | 4,658 | 0 | 0 |
| PowerShell | 2 | 1,012 | 640 | 223 | 149 |
| Markdown | 2,584 | 229,676 | 0 | 183,166 | 46,510 |

Top language (Python 97.5%). Generated at 2026-09-13T09:14:26.769523444+00:00. Canonical report: https://octocounts.com/github/mukul975/Anthropic-Cybersecurity-Skills

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/mukul975/Anthropic-Cybersecurity-Skills/commit/54a798831d2266a3ca61ce68a7acb80b81160d57?analysis=%7B%22ignoredDirs%22%3A%5B%5D%2C%22ignoredLanguages%22%3A%5B%5D%2C%22profile%22%3A%22default%22%2C%22includeDocs%22%3Afalse%2C%22includeTests%22%3Afalse%2C%22includeGenerated%22%3Afalse%7D).

## Report FAQ

### How many lines of code does mukul975/Anthropic-Cybersecurity-Skills have?

mukul975/Anthropic-Cybersecurity-Skills has 481,404 total lines, including 213,879 code lines, 186,977 comment lines, and 80,548 blank lines.

### How was the mukul975/Anthropic-Cybersecurity-Skills line count measured?

OctoCounts resolved the public GitHub repository to commit 54a798831d22, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for mukul975/Anthropic-Cybersecurity-Skills?

This OctoCounts report was generated from 54a798831d2266a3ca61ce68a7acb80b81160d57 at commit 54a798831d22 on 2026-09-13T09:14:26.769523444+00:00.

## Similar repository reports

- [tlenenao/geostudio](https://octocounts.com/github/tlenenao/geostudio) — Python, 213,917 code lines
- [yt-dlp/yt-dlp](https://octocounts.com/github/yt-dlp/yt-dlp) — Python, 210,341 code lines
- [HKUDS/nanobot](https://octocounts.com/github/HKUDS/nanobot) — Python, 219,330 code lines
- [mohamedomarr/IB-Acquisition](https://octocounts.com/github/mohamedomarr/IB-Acquisition) — Python, 206,926 code lines
- [sqlalchemy/sqlalchemy](https://octocounts.com/github/sqlalchemy/sqlalchemy) — Python, 221,522 code lines
- [open-jarvis/OpenJarvis](https://octocounts.com/github/open-jarvis/OpenJarvis) — Python, 221,541 code lines

## Related OctoCounts pages

- [Recently analyzed repositories](https://octocounts.com/recent)
- [Popular SLOC reports](https://octocounts.com/popular)
- [Trending GitHub repositories](https://octocounts.com/trending)
- [Hall of Monoliths](https://octocounts.com/hall-of-monoliths)
- [TensorFlow vs PyTorch](https://octocounts.com/compare/tensorflow-vs-pytorch)
- [Django vs Ruby on Rails](https://octocounts.com/compare/django-vs-rails)
- [Laravel vs Django](https://octocounts.com/compare/laravel-vs-django)
- [GitHub SLOC counter guide](https://octocounts.com/docs/github-sloc-counter)
- [Counting methodology](https://octocounts.com/docs/methodology)
- [SLOC and code metrics glossary](https://octocounts.com/docs/glossary)
- [Original research: how filtering changes SLOC counts](https://octocounts.com/research)
- [OctoCounts API docs](https://octocounts.com/docs/api)
