# tuna/tunasync-scripts SLOC report

> As of 2026-09-16 (commit b7e131dc4a1c), tuna/tunasync-scripts contains 8,456 total lines: 6,630 code, 533 comments, 1,293 blank, across 95 files in 8 languages (top: Python 65.8%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

tuna/tunasync-scripts has 6,630 source lines of code out of 8,456 total lines across 95 files, counted from the b7e131dc4a1c4711f84cbbe6c6168f0f95ac3fbc ref at commit b7e131dc4a1c by the OctoCounts tokei engine on 2026-09-16. 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 tuna/tunasync-scripts to commit b7e131dc4a1c, 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 small codebase by counted code lines. Code represents 78.4% of all lines, comments represent 6.3%, and the repository averages 70 code lines per file. Python accounts for 65.8% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 19 | 5,335 | 4,365 | 244 | 726 |
| Shell | 58 | 2,365 | 1,743 | 180 | 442 |
| Dockerfile | 11 | 291 | 197 | 24 | 70 |
| BASH | 3 | 231 | 186 | 12 | 33 |
| JSON | 1 | 109 | 109 | 0 | 0 |
| PHP | 1 | 89 | 26 | 50 | 13 |
| Julia | 1 | 12 | 4 | 6 | 2 |
| Markdown | 1 | 24 | 0 | 17 | 7 |

Top language (Python 65.8%). Generated at 2026-09-16T22:06:44.318192760+00:00. Canonical report: https://octocounts.com/github/tuna/tunasync-scripts

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/tuna/tunasync-scripts/commit/b7e131dc4a1c4711f84cbbe6c6168f0f95ac3fbc?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 tuna/tunasync-scripts have?

tuna/tunasync-scripts has 8,456 total lines, including 6,630 code lines, 533 comment lines, and 1,293 blank lines.

### How was the tuna/tunasync-scripts line count measured?

OctoCounts resolved the public GitHub repository to commit b7e131dc4a1c, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for tuna/tunasync-scripts?

This OctoCounts report was generated from b7e131dc4a1c4711f84cbbe6c6168f0f95ac3fbc at commit b7e131dc4a1c on 2026-09-16T22:06:44.318192760+00:00.

## Similar repository reports

- [Panniantong/Agent-Reach](https://octocounts.com/github/Panniantong/Agent-Reach) — Python, 6,619 code lines
- [snakers4/silero-vad](https://octocounts.com/github/snakers4/silero-vad) — Python, 6,657 code lines
- [yoheinakajima/babyagi](https://octocounts.com/github/yoheinakajima/babyagi) — Python, 6,714 code lines
- [FareedKhan-dev/train-llm-from-scratch](https://octocounts.com/github/FareedKhan-dev/train-llm-from-scratch) — Python, 6,540 code lines
- [MxUserBot/MXUserbot](https://octocounts.com/github/MxUserBot/MXUserbot) — Python, 6,745 code lines
- [ArThirtyFour/MXUserbot](https://octocounts.com/github/ArThirtyFour/MXUserbot) — Python, 6,745 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)
