Repository Analysis

charmbracelet/bubbletea

A powerful little TUI framework 🏗

3.2 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of charmbracelet/bubbletea, a Go project with 43,737 GitHub stars. SynthScan v2.0 examined 16,693 lines of code across 168 source files, recording 51 pattern matches distributed across 4 syntactic categories. The overall adjusted score of 3.2 places this repository in the Likely human-written band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

3.2
Adjusted Score
3.2
Raw Score
100%
Time Factor
2026-07-13
Last Push
43.7K
Stars
Go
Language
16.7K
Lines of Code
168
Files
51
Pattern Hits
2026-07-14
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 1LOW 50

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 51 distinct pattern matches across 4 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Over-Commented Block48 hits · 48 pts
SeverityFileLineSnippetContext
LOWraw.go1package teaCOMMENT
LOWraw.go21// for _, attr := range msg {COMMENT
LOWcolor.go41}COMMENT
LOWoptions.go41 return func(p *Program) {COMMENT
LOWoptions.go101 }COMMENT
LOWoptions.go121// return nilCOMMENT
LOWtea.go61COMMENT
LOWtea.go81COMMENT
LOWtea.go101 // breaking the unidirectional data flow of Bubble Tea.COMMENT
LOWtea.go121 // }COMMENT
LOWtea.go141 // WindowTitle sets the terminal window title. Support depends on theCOMMENT
LOWtea.go161 AltScreen boolCOMMENT
LOWtea.go181 //COMMENT
LOWtea.go201//COMMENT
LOWtea.go221// case "shift+enter":COMMENT
LOWtea.go241type KeyboardEnhancements struct {COMMENT
LOWtea.go261 // ReportAssociatedText requests the terminal to report the text associatedCOMMENT
LOWtea.go281}COMMENT
LOWtea.go381}COMMENT
LOWtea.go441 // filter supplies an event filter that will be invoked before Bubble TeaCOMMENT
LOWtea.go461 //COMMENT
LOWtea.go961// queried for various capabilities.COMMENT
LOWscreen.go21 return clearScreenMsg{}COMMENT
LOWscreen.go41//COMMENT
LOWexec.go21// For non-interactive i/o you should use a Cmd (that is, a tea.Cmd).COMMENT
LOWexec.go41// cmd := ExecProcess(c, func(err error) Msg {COMMENT
LOWkey.go201// convenient and readable to use this method when matching against keys.COMMENT
LOWkey.go221}COMMENT
LOWkey.go261COMMENT
LOWkey.go281//COMMENT
LOWkey.go301// 'A', '1', '!', etc.).COMMENT
LOWkey.go321 //COMMENT
LOWkey.go341 // Console API.COMMENT
LOWkey.go361// - altCOMMENT
LOWmouse.go41 MouseButton11COMMENT
LOWmouse.go61// case MouseMsg:COMMENT
LOWcursed_renderer.go141COMMENT
LOWcursed_renderer.go501 // just a cursor visibility change, we don't need to enterCOMMENT
LOWtermcap.go1package teaCOMMENT
LOWtermcap.go21// switch msg := msg.(type) {COMMENT
LOWenviron.go1package teaCOMMENT
LOWkeyboard.go1package teaCOMMENT
LOWkeyboard.go21 // }COMMENT
LOWcommands.go61// the entire specified duration. For example, if we're ticking for one minuteCOMMENT
LOWcommands.go81// func tickEvery() Cmd {COMMENT
LOWcommands.go121// a message containing the time at which the tick occurred.COMMENT
LOWcommands.go141// func (m model) Init() (Model, Cmd) {COMMENT
LOWexamples/progress-static/main.go1package mainCOMMENT
AI Slop Vocabulary1 hit · 3 pts
SeverityFileLineSnippetContext
MEDIUMexamples/keyboard-enhancements/main.go41 // This allows for more nuanced input handling in terminal applications.COMMENT
Example Usage Blocks1 hit · 2 pts
SeverityFileLineSnippetContext
LOWoptions.go14// Example usage:COMMENT
Slop Phrases1 hit · 2 pts
SeverityFileLineSnippetContext
LOWlogging.go15// Don't forget to close the file when you're done with it.COMMENT