Repository Analysis

disposable-email-domains/disposable-email-domains

a list of disposable email domains

39.9 Strong AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of disposable-email-domains/disposable-email-domains, a Python project with 5,333 GitHub stars. SynthScan v2.0 examined 1,907 lines of code across 13 source files, recording 51 pattern matches distributed across 6 syntactic categories. The overall adjusted score of 39.9 places this repository in the Strong 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).

39.9
Adjusted Score
39.9
Raw Score
100%
Time Factor
2026-07-14
Last Push
5.3K
Stars
Python
Language
1.9K
Lines of Code
13
Files
51
Pattern Hits
2026-07-14
Scan Date
0.08
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 1MEDIUM 16LOW 34

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 6 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.

Excessive Try-Catch Wrapping39 hits · 65 pts
SeverityFileLineSnippetContext
MEDIUMfetch_domains.py96 print(f"Error fetching {self.name} domains: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py103 print(f"Error parsing JSON from {self.name}: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py131 print(f"Error fetching {self.name} domains: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py138 print(f"Error parsing JSON from {self.name}: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py166 print(f"Error fetching {self.name} domains: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py173 print(f"Error parsing JSON from {self.name}: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py201 print(f"Error fetching {self.name} domains: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py208 print(f"Error parsing JSON from {self.name}: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py246 print(f"Error fetching {self.name} domains: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py252 print(f"Error parsing JSON from {self.name}: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py331 print(f"Error fetching {self.name} domains: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py337 print(f"Error parsing JSON from {self.name}: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py67 print(f"Error fetching {self.name} domains: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py281 print(f"Error fetching {self.name} domains (attempt {attempt + 1}): {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py446 print(f"Error loading Public Suffix List: {e}", file=sys.stderr)CODE
MEDIUMfetch_domains.py474 print(f"Error processing {fetcher.get_name()}: {e}", file=sys.stderr)CODE
LOWfetch_domains.py95 except Exception as e:CODE
LOWfetch_domains.py102 except Exception as e:CODE
LOWfetch_domains.py130 except Exception as e:CODE
LOWfetch_domains.py137 except Exception as e:CODE
LOWfetch_domains.py165 except Exception as e:CODE
LOWfetch_domains.py172 except Exception as e:CODE
LOWfetch_domains.py200 except Exception as e:CODE
LOWfetch_domains.py207 except Exception as e:CODE
LOWfetch_domains.py245 except Exception as e:CODE
LOWfetch_domains.py251 except Exception as e:CODE
LOWfetch_domains.py330 except Exception as e:CODE
LOWfetch_domains.py336 except Exception as e:CODE
LOWfetch_domains.py66 except Exception as e:CODE
LOWfetch_domains.py280 except Exception as e:CODE
LOWfetch_domains.py445 except Exception as e:CODE
LOWfetch_domains.py473 except Exception as e:CODE
LOWscripts/discover_new_domains.py294 except Exception:CODE
LOWscripts/discover_new_domains.py308 except Exception:CODE
LOWscripts/discover_new_domains.py413 except Exception as nav_error:CODE
LOWscripts/discover_new_domains.py484 except Exception as ss_err:CODE
LOWscripts/discover_new_domains.py513 except Exception as e:CODE
LOWscripts/discover_new_domains.py517 except Exception as e:CODE
LOWscripts/discover_new_domains.py576 except Exception as e:CODE
Hyper-Verbose Identifiers4 hits · 4 pts
SeverityFileLineSnippetContext
LOWfetch_domains.py15def extract_domains_from_text(text: str) -> Set[str]:CODE
LOWverify.py53def check_for_public_suffixes(filename, psl, psl_local):CODE
LOWverify.py67def check_for_invalid_level_domains(filename, psl, psl_local):CODE
LOWscripts/discover_new_domains.py230def load_screenshotted_domains(screenshot_dir):CODE
Deep Nesting5 hits · 3 pts
SeverityFileLineSnippetContext
LOWfetch_domains.py15CODE
LOWfetch_domains.py433CODE
LOWfetch_domains.py195CODE
LOWfetch_domains.py325CODE
LOWscripts/discover_new_domains.py323CODE
Cross-Language Confusion1 hit · 2 pts
SeverityFileLineSnippetContext
HIGHscripts/discover_new_domains.py393 Object.defineProperty(navigator, 'webdriver', { get: () => undefined });STRING
Unused Imports1 hit · 1 pts
SeverityFileLineSnippetContext
LOWverify.py12CODE
Modern Structural Boilerplate1 hit · 1 pts
SeverityFileLineSnippetContext
LOWscripts/discover_new_domains.py117logger = logging.getLogger(__name__)CODE