AI Slop Code Risk Monitor

typelevel/cats

Name: typelevel/cats — Scala 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).

Lightweight, modular, and extensible library for functional programming.

0.4 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for typelevel/cats, a Scala project with 5,479 GitHub stars. SynthScan examined 114,264 lines of code across 915 source files, recording 20 pattern matches distributed across 5 syntactic categories. The legacy adjusted pattern signal is 0.4 (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.

0.4

Adjusted Signal

0.4

Raw Pattern Score

100%

Time Factor

2026-10-31

Last Push

5.5K

Stars

Scala

Language

114.3K

Lines of Code

915

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 0.9/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 18 finding(s).

Hallucinated Dependency & Supply Chain Risk 0.9/100 · LOW

Are imports declared and package/API names verified? 2 finding(s).

Agent Over-Engineering Ratio 0.5/100 · LOW

Are wrappers, guards, and repeated structures necessary? 12 finding(s).

PR Review Velocity Impact 1.8/100 · LOW

Which changes need focused human review? 20 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 2HIGH 0MEDIUM 2LOW 16

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 20 distinct pattern matches across 5 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.

Hallucination Indicators2 hits · 20 pts

Severity	File	Line	Snippet	Context
CRITICAL	docs/js/sponsors.js	63	var members = json.data.collective.members.nodes.sort(compareSponsors);	CODE
CRITICAL	…n/scala/cats/kernel/instances/SortedSetInstances.scala	66	cats.kernel.instances.int.catsKernelStdOrderForInt.compare(a1.size, a2.size) match {	CODE

Hyper-Verbose Identifiers12 hits · 12 pts

Severity	File	Line	Snippet	Context
LOW	core/src/main/scala/cats/syntax/either.scala	69	private[syntax] def getOrElse[BB >: B](default: => BB): BB =	CODE
LOW	core/src/main/scala/cats/data/Ior.scala	557	final def getOrElse[BB >: B](bb: => BB): BB = right.getOrElse(bb)	CODE
LOW	core/src/main/scala/cats/data/EitherT.scala	117	def getOrElse[BB >: B](default: => BB)(implicit F: Functor[F]): F[BB] = F.map(value)(_.getOrElse(default))	CODE
LOW	core/src/main/scala/cats/data/EitherT.scala	130	def getOrElseF[BB >: B](default: => F[BB])(implicit F: Monad[F]): F[BB] =	CODE
LOW	core/src/main/scala/cats/data/EitherT.scala	153	def getOrRaise[E](e: => E)(implicit F: MonadError[F, ? >: E]): F[B] =	CODE
LOW	core/src/main/scala/cats/data/IorT.scala	55	def getOrElse[BB >: B](default: => BB)(implicit F: Functor[F]): F[BB] = F.map(value)(_.getOrElse(default))	CODE
LOW	core/src/main/scala/cats/data/IorT.scala	57	def getOrElseF[BB >: B](default: => F[BB])(implicit F: Monad[F]): F[BB] =	CODE
LOW	core/src/main/scala/cats/data/IorT.scala	81	def getOrRaise[E](e: => E)(implicit F: MonadError[F, ? >: E]): F[B] =	CODE
LOW	core/src/main/scala/cats/data/OptionT.scala	288	def getOrElse[B >: A](default: => B)(implicit F: Functor[F]): F[B] =	CODE
LOW	core/src/main/scala/cats/data/OptionT.scala	301	def getOrElseF[B >: A](default: => F[B])(implicit F: Monad[F]): F[B] =	CODE
LOW	core/src/main/scala/cats/data/OptionT.scala	318	def getOrRaise[E](e: => E)(implicit F: MonadError[F, ? >: E]): F[A] =	CODE
LOW	core/src/main/scala/cats/data/Validated.scala	112	def getOrElse[B >: A](default: => B): B =	CODE

Over-Commented Block4 hits · 4 pts

Severity	File	Line	Snippet	Context
LOW	alleycats-core/src/main/scala/alleycats/std/try.scala	41	// val y = verify(0)	COMMENT
LOW	alleycats-core/src/main/scala/alleycats/std/set.scala	41	// rely on free theorems.	COMMENT
LOW	tests/shared/src/test/scala/cats/tests/EvalSuite.scala	201	//	COMMENT
LOW	kernel/src/main/scala/cats/kernel/Group.scala	61	/**	COMMENT

Slop Phrases1 hit · 3 pts

Severity	File	Line	Snippet	Context
MEDIUM	core/src/main/scala/cats/arrow/Compose.scala	28	* Here's how you can use `>>>` and `<<<`	COMMENT

AI Slop Vocabulary1 hit · 2 pts

Severity	File	Line	Snippet	Context
MEDIUM	core/src/main/scala/cats/data/IdT.scala	159	F0.map2Eval(fa.value, fb.map(_.value))(f) // if F0 has a lazy map2Eval, leverage it	CODE