Repository Analysis

google-gemini/cookbook

Examples and guides for using the Gemini API

11.8 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of google-gemini/cookbook, a Jupyter Notebook project with 17,595 GitHub stars. SynthScan v2.0 examined 8,362 lines of code across 58 source files, recording 46 pattern matches distributed across 8 syntactic categories. The overall adjusted score of 11.8 places this repository in the Low AI signal 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).

11.8
Adjusted Score
11.8
Raw Score
100%
Time Factor
2026-07-31
Last Push
17.6K
Stars
Jupyter Notebook
Language
8.4K
Lines of Code
58
Files
46
Pattern Hits
2026-08-02
Scan Date
0.12
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 7MEDIUM 10LOW 29

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 46 distinct pattern matches across 8 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.

Cross-Language Confusion (JS/TS)5 hits · 32 pts
SeverityFileLineSnippetContext
HIGHquickstarts-js/Get_Started.js732 return FalseCODE
HIGHquickstarts-js/Get_Started.js735 return FalseCODE
HIGHquickstarts-js/Get_Started.js736 return TrueCODE
HIGHquickstarts-js/Get_Started.js747print(f'The first 50 prime numbers are: {primes}')CODE
HIGHquickstarts-js/Get_Started.js748print(f'The sum of the first 50 prime numbers is: {sum_of_primes}')CODE
Excessive Try-Catch Wrapping16 hits · 26 pts
SeverityFileLineSnippetContext
LOWexamples/gradio_audio.py128 except Exception as e:CODE
LOWexamples/gradio_audio.py151 except Exception as e:CODE
MEDIUMexamples/gradio_audio.py152 print(f"Error in receive: {str(e)}")CODE
LOWexamples/gradio_audio.py189 except Exception as e:CODE
LOWexamples/gradio_audio.py221 except Exception as e:CODE
MEDIUMquickstarts/Get_started_LyriaRealTime.py143 print("Error: Matching enum not found.")CODE
MEDIUMquickstarts/Get_started_LyriaRealTime.py175 print(f"Error: Empty prompt text in segment '{segment_str_raw}'. Skipping this segment."CODE
MEDIUMquickstarts/Get_started_LyriaRealTime.py182 print(f"Error: Invalid weight '{weight_s}' in segment '{segment_str_raw}'. Must be a numCODE
MEDIUMquickstarts/Get_started_LyriaRealTime.py187 print(f"Error: Segment '{segment_str_raw}' is not in 'text:weight' format. Skipping this segCODE
MEDIUMquickstarts/Get_started_LyriaRealTime.py199 print("Error: Input contained ':' suggesting multi-prompt format, but no valid 'text:weight' segCODE
MEDIUMquickstarts/Get_started_LiveAPI.py267def capture_screen(self):CODE
LOWquickstarts/Get_started_LiveTranslate.py119 except Exception:CODE
LOWquickstarts/Get_started_LiveTranslate.py161 except Exception as e:CODE
LOWquickstarts/Get_started_LiveTranslate.py185 except Exception:CODE
LOWquickstarts/Get_started_LiveTranslate.py210 except Exception:CODE
LOWquickstarts/Get_started_LiveTranslate.py264 except Exception as e:CODE
Over-Commented Block10 hits · 10 pts
SeverityFileLineSnippetContext
LOWexamples/fastrtc_ui.py1# -*- coding: utf-8 -*-COMMENT
LOWexamples/gradio_audio.py1# -*- coding: utf-8 -*-COMMENT
LOWquickstarts/Get_started_LyriaRealTime.py1# -*- coding: utf-8 -*-COMMENT
LOWquickstarts/Get_started_LiveAPI.py1# -*- coding: utf-8 -*-COMMENT
LOWquickstarts/Get_started_LiveTranslate.py1# -*- coding: utf-8 -*-COMMENT
LOWquickstarts/Get_started_LiveAPI_NativeAudio.py1# -*- coding: utf-8 -*-COMMENT
LOWquickstarts/websockets/Get_started_LiveAPI.py1# -*- coding: utf-8 -*-COMMENT
LOWquickstarts/websockets/shell_websockets.sh1#!/bin/bashCOMMENT
LOWquickstarts/file-api/sample.sh1#!/bin/bashCOMMENT
LOWquickstarts/file-api/sample.py1# Copyright 2026 Google LLCCOMMENT
Magic Placeholder Names2 hits · 10 pts
SeverityFileLineSnippetContext
HIGHquickstarts/file-api/README.md28echo "GOOGLE_API_KEY='YOUR_API_KEY'" >> .envCODE
HIGHquickstarts/file-api/README.md43echo "GOOGLE_API_KEY='YOUR_API_KEY'" >> .envCODE
Redundant / Tautological Comments5 hits · 8 pts
SeverityFileLineSnippetContext
LOW…b/workflows/new_examples_links_in_table_of_content.yml33 # Check if link exists in $READMECOMMENT
LOW…b/workflows/new_examples_links_in_table_of_content.yml35 # Check if a README.md exists in the sub-folder, and if so, check for the link thereCOMMENT
LOWquickstarts/Get_started_LiveAPI.py215 # Check if the frame was read successfullyCOMMENT
LOWquickstarts/Get_started_LiveTranslate.py155 # Print output (translated) transcriptCOMMENT
LOWquickstarts/websockets/Get_started_LiveAPI.py117 # Check if the frame was read successfullyCOMMENT
Modern AI Meta-Vocabulary2 hits · 6 pts
SeverityFileLineSnippetContext
MEDIUMquickstarts/Get_started_LiveAPI.py86# Trigger tokens sent so that model does not hallucinate in long conversationsCOMMENT
MEDIUMquickstarts/Get_started_LiveAPI.py87# Sliding window to retain the context within the context window limitCOMMENT
Deep Nesting5 hits · 5 pts
SeverityFileLineSnippetContext
LOWexamples/gradio_audio.py226CODE
LOWquickstarts/Get_started_LyriaRealTime.py64CODE
LOWquickstarts/Get_started_LyriaRealTime.py88CODE
LOWquickstarts/Get_started_LiveAPI.py176CODE
LOWquickstarts/Get_started_LiveTranslate.py138CODE
AI Slop Vocabulary1 hit · 2 pts
SeverityFileLineSnippetContext
MEDIUMquickstarts-js/Audio.js253The video presents a compelling review of Google's new Gemini 2.5 Pro Experimental AI, asserting its superiority asCODE