AI Slop Code Risk Monitor

youngyangyang04/leetcode-master

Name: youngyangyang04/leetcode-master — Shell 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).

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0.1 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for youngyangyang04/leetcode-master, a Shell project with 62,762 GitHub stars. SynthScan examined 149,713 lines of code across 315 source files, recording 21 pattern matches distributed across 2 syntactic categories. The legacy adjusted pattern signal is 0.1 (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.

0.1

Adjusted Signal

0.1

Raw Pattern Score

100%

Time Factor

2026-10-24

Last Push

62.8K

Stars

Shell

Language

149.7K

Lines of Code

315

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 0.7/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 21 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 0.2/100 · LOW

Are wrappers, guards, and repeated structures necessary? 7 finding(s).

PR Review Velocity Impact 0.7/100 · LOW

Which changes need focused human review? 21 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 0LOW 21

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 21 distinct pattern matches across 2 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.

Over-Commented Block14 hits · 14 pts

Severity	File	Line	Snippet	Context
LOW	problems/二叉树的递归遍历.md	281	}	COMMENT
LOW	problems/二叉树的递归遍历.md	321	// let res=[];	COMMENT
LOW	problems/二叉树的递归遍历.md	341	...inorderTraversal(root.right),	COMMENT
LOW	problems/0203.移除链表元素.md	621	}	COMMENT
LOW	problems/toolgithub.sh	1	#########################################################################	COMMENT
LOW	problems/toolgithub.sh	41	ex -sc '1i\|* [刷算法（两个月高强度学算法）](https://www.programmercarl.com/xunlian/xunlianying.html)' -cx $i	COMMENT
LOW	problems/0494.目标和.md	841	// 将所有元素划分为 2 个集合,	COMMENT
LOW	problems/0494.目标和.md	861	}	COMMENT
LOW	problems/0101.对称二叉树.md	341	continue;	COMMENT
LOW	problems/0005.最长回文子串.md	521	//Manacher算法,利用回文对称的性质，根据i在上一个回文中心的臂长里的位置去判断i的回文性	COMMENT
LOW	problems/0005.最长回文子串.md	541	//1.i>maxRight,找不到i',无法参考，自己算自己的	COMMENT
LOW	problems/0968.监控二叉树.md	241	// left == 1 && right == 0 左节点有摄像头，右节点无覆盖	COMMENT
LOW	problems/0968.监控二叉树.md	481	# 情况1: 左右节点都有覆盖	COMMENT
LOW	problems/kamacoder/0126.骑士的攻击astar.md	581	### TypeScript	COMMENT

Hyper-Verbose Identifiers7 hits · 7 pts

Severity	File	Line	Snippet	Context
LOW	problems/0654.最大二叉树.md	399	function constructMaximumBinaryTree(nums: number[]): TreeNode \| null {	CODE
LOW	problems/0654.最大二叉树.md	419	function constructMaximumBinaryTree(nums: number[]): TreeNode \| null {	CODE
LOW	problems/1365.有多少小于当前数字的数字.md	259	function smallerNumbersThanCurrent(nums: number[]): number[] {	CODE
LOW	problems/1365.有多少小于当前数字的数字.md	278	function smallerNumbersThanCurrent(nums: number[]): number[] {	CODE
LOW	problems/1005.K次取反后最大化的数组和.md	311	function largestSumAfterKNegations(nums: number[], k: number): number {	CODE
LOW	problems/kamacoder/0109.冗余连接II.md	409	def is_tree_after_remove_edge(edges, edge, n):	CODE
LOW	problems/kamacoder/0104.建造最大岛屿.md	420	def check_largest_connect_island(self,grid):	CODE