Repository Analysis

p-e-w/heretic

Fully automatic censorship removal for language models

5.7 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of p-e-w/heretic, a Python project with 26,263 GitHub stars. SynthScan v2.0 examined 7,128 lines of code across 30 source files, recording 37 pattern matches distributed across 6 syntactic categories. The overall adjusted score of 5.7 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).

5.7
Adjusted Score
5.7
Raw Score
100%
Time Factor
2026-07-07
Last Push
26.3K
Stars
Python
Language
7.1K
Lines of Code
30
Files
37
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 36

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 37 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.

Hyper-Verbose Identifiers11 hits · 11 pts
SeverityFileLineSnippetContext
LOWsrc/heretic/system.py50def get_nvidia_driver_version() -> str | None:CODE
LOWsrc/heretic/system.py64def get_amdgpu_driver_version() -> str | None:CODE
LOWsrc/heretic/system.py240def get_accelerator_info_dict() -> dict[str, Any]:CODE
LOWsrc/heretic/config.py552 def settings_customise_sources(CODE
LOWsrc/heretic/reproduce.py115def load_reproduction_information(path: str) -> dict[str, Any]:CODE
LOWsrc/heretic/reproduce.py151def get_package_mismatch_severity(package_name: str) -> MismatchSeverity:CODE
LOWsrc/heretic/reproduce.py175def format_version_information(version_information: dict[str, Any]) -> str:CODE
LOWsrc/heretic/model.py451 def get_abliterable_components(self) -> list[str]:CODE
LOWsrc/heretic/utils.py351def generate_requirements_txt() -> str:CODE
LOWsrc/heretic/utils.py376def generate_reproduce_readme(CODE
LOWsrc/heretic/evaluator.py128 def get_dataset_specifications(self) -> list[DatasetSpecification]:CODE
Deep Nesting9 hits · 9 pts
SeverityFileLineSnippetContext
LOWsrc/heretic/system.py26CODE
LOWsrc/heretic/system.py64CODE
LOWsrc/heretic/system.py423CODE
LOWsrc/heretic/reproduce.py35CODE
LOWsrc/heretic/model.py189CODE
LOWsrc/heretic/model.py461CODE
LOWsrc/heretic/utils.py168CODE
LOWsrc/heretic/utils.py376CODE
LOWsrc/heretic/main.py178CODE
Excessive Try-Catch Wrapping8 hits · 8 pts
SeverityFileLineSnippetContext
LOWsrc/heretic/system.py99 except Exception:CODE
LOWsrc/heretic/system.py175 except Exception:CODE
LOWsrc/heretic/model.py151 except Exception as error:CODE
LOWsrc/heretic/utils.py200 except Exception as error:CODE
LOWsrc/heretic/plugin.py126 except Exception:CODE
LOWsrc/heretic/main.py142 except Exception:CODE
LOWsrc/heretic/main.py446 except Exception as error:CODE
LOWsrc/heretic/main.py1455 except Exception as error:CODE
Over-Commented Block5 hits · 5 pts
SeverityFileLineSnippetContext
LOWconfig.default.toml1# Rename this file to config.toml, place it in the working directoryCOMMENT
LOWconfig.default.toml101COMMENT
LOWconfig.default.toml201[scorer.KeywordRate.prompts]COMMENT
LOWsrc/heretic/model.py521 # different model configurations, and PEFT employs differentCOMMENT
LOWsrc/heretic/main.py621 direction_index = NoneCOMMENT
Redundant / Tautological Comments3 hits · 4 pts
SeverityFileLineSnippetContext
LOWtests/run_tests.py15 # Read the file in 64 kB blocks.COMMENT
LOWsrc/heretic/model.py270 # Check if we need special handling for quantized modelsCOMMENT
LOWsrc/heretic/utils.py624 # Read the file in 64 kB blocks.COMMENT
AI Slop Vocabulary1 hit · 3 pts
SeverityFileLineSnippetContext
MEDIUMsrc/heretic/model.py195 # This is more robust than splitting component keys (e.g. "attn.o_proj" -> "o_proj")COMMENT