Repository Analysis

owainlewis/awesome-artificial-intelligence

A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.

10.7 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of owainlewis/awesome-artificial-intelligence, a Python project with 16,238 GitHub stars. SynthScan v2.0 examined 1,525 lines of code across 16 source files, recording 13 pattern matches distributed across 3 syntactic categories. The overall adjusted score of 10.7 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).

10.7
Adjusted Score
10.7
Raw Score
100%
Time Factor
2026-08-15
Last Push
16.2K
Stars
Python
Language
1.5K
Lines of Code
16
Files
13
Pattern Hits
2026-08-29
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 5LOW 8

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 13 distinct pattern matches across 3 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.

Modern AI Meta-Vocabulary5 hits · 11 pts
SeverityFileLineSnippetContext
MEDIUMREADME.md40- [AI Engineer](https://aiengineer.co/): A paid program for agentic coding and building, testing, and shipping productioCODE
MEDIUMREADME.md68- [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents): Anthropic's practical paCODE
MEDIUMREADME.md69- [A Practical Guide to Building Agents](https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-buildCODE
MEDIUMREADME.md89- [OpenAI Agents SDK](https://openai.github.io/openai-agents-python/): A small SDK for tools, handoffs, guardrails, tracCODE
MEDIUMREADME.md143### Software factories and agent orchestrationCOMMENT
Hyper-Verbose Identifiers7 hits · 4 pts
SeverityFileLineSnippetContext
LOWtests/test_validate_readme.py34 def test_duplicate_title_and_normalized_url(self):STRING
LOWtests/test_validate_readme.py48 def test_level_two_heading_resets_category(self):STRING
LOWtests/test_validate_readme.py53 def test_same_category_name_in_different_sections(self):STRING
LOWtests/test_validate_readme.py67 def test_description_needs_period(self):STRING
LOWtests/test_validate_readme.py79 def test_invalid_url_is_an_error(self):STRING
LOWtests/test_validate_readme.py85 def test_link_status_classification(self):STRING
LOWtests/test_validate_readme.py94 def test_link_exception_classification(self):STRING
Unused Imports1 hit · 1 pts
SeverityFileLineSnippetContext
LOWscripts/validate_readme.py4CODE