AI Slop Code Risk Monitor

norvig/pytudes

Name: norvig/pytudes — Jupyter Notebook Code Quality & Security Review | AI Slop Code Risk Monitor
Creator: AI Slop Code Risk Monitor

Adjusted legacy signal; badge bands: 25 / 50 / 75 (different from dashboard bands).

Python programs, usually short, of considerable difficulty, to perfect particular skills.

0.6 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for norvig/pytudes, a Jupyter Notebook project with 24,416 GitHub stars. SynthScan examined 87,088 lines of code across 47 source files, recording 48 pattern matches distributed across 7 syntactic categories. The legacy adjusted pattern signal is 0.6 (Low pattern signal); it is not an authorship estimate.

Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.

0.6

Adjusted Signal

0.6

Raw Pattern Score

100%

Time Factor

2026-10-28

Last Push

24.4K

Stars

Jupyter Notebook

Language

87.1K

Lines of Code

Files

Pattern Hits

2026-10-31

Scan Date

0.00

HC Hit Rate

Risk Review · Payload v1

Use each vector to guide manual verification of the underlying findings.

Payload schema: 1.0.0 · Engine: 2.0.0 · Scoring: heuristic-density-v1 · Scan date: 2026-10-31.

Cognitive Ownership Risk 1.4/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 22 finding(s).

Hallucinated Dependency & Supply Chain Risk 1.3/100 · LOW

Are imports declared and package/API names verified? 23 finding(s).

Agent Over-Engineering Ratio 0.3/100 · LOW

Are wrappers, guards, and repeated structures necessary? 4 finding(s).

PR Review Velocity Impact 2.9/100 · LOW

Which changes need focused human review? 48 finding(s).

What These Metrics Mean

Adjusted Signal: Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score: Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor: A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits: Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate: High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files: Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.

Severity Breakdown

Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.

CRITICAL 0HIGH 0MEDIUM 2LOW 46

Directory Score Breakdown

This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.

Pattern Findings

The scanner identified 48 distinct pattern matches across 7 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. 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 configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.

Unused Imports22 hits · 22 pts

Severity	File	Line	Context
LOW	py/pal.py	1	CODE
LOW	py/pal.py	2	CODE
LOW	py/lispytest.py	1	CODE
LOW	py/lispytest.py	118	CODE
LOW	py/lispytest.py	120	CODE
LOW	py/yaptu.py	64	CODE
LOW	py/yaptu.py	66	CODE
LOW	py/yaptu.py	66	CODE
LOW	py/py2html.py	8	CODE
LOW	py/docex.py	75	CODE
LOW	py/pal2.py	1	CODE
LOW	py/parse.py	1	CODE
LOW	py/testaccum.py	1	CODE
LOW	py/testaccum.py	2	CODE
LOW	py/testaccum.py	4	CODE
LOW	py/beal.py	4	CODE
LOW	py/beal.py	4	CODE
LOW	py/ibol.py	1	CODE
LOW	py/lettercount.py	25	CODE
LOW	py/lettercount.py	26	CODE
LOW	py/lettercount.py	122	CODE
LOW	ipynb/portman.py	5	CODE

Deep Nesting19 hits · 19 pts

Severity	File	Line	Context
LOW	py/yaptu.py	71	CODE
LOW	py/lispy.py	61	CODE
LOW	py/lispy.py	93	CODE
LOW	py/lispy.py	181	CODE
LOW	py/lispy.py	220	CODE
LOW	py/lispy.py	63	CODE
LOW	py/py2html.py	40	CODE
LOW	py/lis.py	110	CODE
LOW	py/docex.py	108	CODE
LOW	py/docex.py	147	CODE
LOW	py/pal2.py	78	CODE
LOW	py/pal2.py	137	CODE
LOW	py/parse.py	24	CODE
LOW	py/sudoku.py	60	CODE
LOW	py/beal.py	13	CODE
LOW	py/beal.py	93	CODE
LOW	py/ibol.py	106	CODE
LOW	py/ibol.py	137	CODE
LOW	ipynb/portman.py	116	CODE

Excessive Try-Catch Wrapping3 hits · 4 pts

Severity	File	Line	Snippet	Context
LOW	py/lispytest.py	109	except Exception as e:	CODE
MEDIUM	py/yaptu.py	138	print('Something went wrong in %sing {%s}' % (why, what))	CODE
LOW	py/lispy.py	117	except Exception as e:	CODE

AI Slop Vocabulary1 hit · 3 pts

Severity	File	Line	Snippet	Context
MEDIUM	ipynb/euler_data.py	1	#### Project Euler: data and problem running harness	COMMENT

Redundant / Tautological Comments1 hit · 2 pts

Severity	File	Line	Snippet	Context
LOW	py/lettercount.py	29	#### Read files in Books-Ngram format; convert to a dict	COMMENT

Hyper-Verbose Identifiers1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	py/SET.py	72	def tally_initial_layout_no_prior_sets(N, sizes=(12, 15)):	CODE

Fake / Example Data1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	ipynb/evs.html	1258	var mc_highmaps = {"map_data_source":"803","legend_title":"Net Approval (approval minus disapproval)","color_stops":[{"c	CODE