AI Slop Code Risk Monitor

anthropics/courses

Name: anthropics/courses — 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).

Anthropic's educational courses

0.7 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for anthropics/courses, a Jupyter Notebook project with 22,957 GitHub stars. SynthScan examined 11,463 lines of code across 39 source files, recording 7 pattern matches distributed across 3 syntactic categories. The legacy adjusted pattern signal is 0.7 (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.

0.7

Adjusted Signal

0.7

Raw Pattern Score

100%

Time Factor

2026-08-28

Last Push

23.0K

Stars

Jupyter Notebook

Language

11.5K

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 2.2/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 5 finding(s).

Hallucinated Dependency & Supply Chain Risk 0.0/100 · LOW

Are imports declared and package/API names verified? 0 finding(s).

Agent Over-Engineering Ratio 1.7/100 · LOW

Are wrappers, guards, and repeated structures necessary? 3 finding(s).

PR Review Velocity Impact 3.9/100 · LOW

Which changes need focused human review? 7 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 1LOW 6

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 7 distinct pattern matches across 3 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.

Slop Phrases4 hits · 4 pts

Severity	File	Line	Snippet	Context
LOW	…engineering_interactive_tutorial/Anthropic 1P/hints.py	20	Don't forget to include the exact phrase "{TOPIC}" wherever you want the topic to be substituted in. Changing the "TOPIC	CODE
LOW	…engineering_interactive_tutorial/Anthropic 1P/hints.py	66	Sometimes the simplest way to go about this is to give Claude an example of how you want its output to look. Just don't	CODE
LOW	…ring_interactive_tutorial/AmazonBedrock/utils/hints.py	20	Don't forget to include the exact phrase "{TOPIC}" wherever you want the topic to be substituted in. Changing the "TOPIC	CODE
LOW	…ring_interactive_tutorial/AmazonBedrock/utils/hints.py	66	Sometimes the simplest way to go about this is to give Claude an example of how you want its output to look. Just don't	CODE

Excessive Try-Catch Wrapping2 hits · 3 pts

Severity	File	Line	Snippet	Context
LOW	…uations/05_prompt_foo_code_graded_animals/transform.py	5	except Exception as e:	CODE
MEDIUM	…uations/05_prompt_foo_code_graded_animals/transform.py	6	print(f"Error in get_transform: {e}")	CODE

AI Structural Patterns1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	…ompt_evaluations/07_prompt_foo_custom_graders/count.py	17		CODE