AI Slop Code Risk Monitor

PlexPt/awesome-chatgpt-prompts-zh

SlopCodeMonitor badge for PlexPt/awesome-chatgpt-prompts-zh Adjusted legacy signal; badge bands: 25 / 50 / 75 (different from dashboard bands).

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0.0 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for PlexPt/awesome-chatgpt-prompts-zh with 64,264 GitHub stars. SynthScan examined 675,994 lines of code across 679 source files, recording 21 pattern matches distributed across 1 syntactic category. The legacy adjusted pattern signal is 0.0 (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.

0.0
Adjusted Signal
0.0
Raw Pattern Score
100%
Time Factor
2026-04-28
Last Push
64.3K
Stars
—
Language
676.0K
Lines of Code
679
Files
21
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 0.0/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 0 finding(s).

Hallucinated Dependency & Supply Chain Risk 0.2/100 · LOW

Are imports declared and package/API names verified? 21 finding(s).

Agent Over-Engineering Ratio 0.0/100 · LOW

Are wrappers, guards, and repeated structures necessary? 0 finding(s).

PR Review Velocity Impact 0.2/100 · 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 1 syntactic category. 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.

Fake / Example Data21 hits · 21 pts
SeverityFileLineSnippetContext
LOWquestion/559.md823| 编辑文章 | 请根据以下文本,在不改变原意的情况下进行编辑:Lorem Ipsum 是一种印刷和排版领域常用的虚拟文字。|CODE
LOWquestion/186.md378| 文章编辑 | 下面这篇文章有语法错误,请帮忙进行修改:Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididuntCODE
LOWquestion/186.md378| 文章编辑 | 下面这篇文章有语法错误,请帮忙进行修改:Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididuntCODE
LOWquestion/233.md373|文本编辑|请将下列文章中的所有逗号转换为分号:Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed sagittis, velit vitae euismod imperCODE
LOWquestion/233.md373|文本编辑|请将下列文章中的所有逗号转换为分号:Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed sagittis, velit vitae euismod imperCODE
LOWquestion/285.md496| 编辑 | 把下面这段文字排版整齐:Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nulla in quCODE
LOWquestion/285.md496| 编辑 | 把下面这段文字排版整齐:Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nulla in quCODE
LOWquestion/509.md846| 文本翻译 | 将以下一段英文翻译成中文:“Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunCODE
LOWquestion/509.md846| 文本翻译 | 将以下一段英文翻译成中文:“Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunCODE
LOWquestion/197.md736| Editing | 下面这段文字中有五处错误,请找出来并更正:Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Donec odio. Quisque volutpat CODE
LOWquestion/197.md736| Editing | 下面这段文字中有五处错误,请找出来并更正:Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Donec odio. Quisque volutpat CODE
LOWquestion/512.md234|文本生成|请根据下面这段文字,继续生成接下来的三个段落。Lorem ipsum dolor sit amet, consectetur adipiscing elit. Cras at tincidunt leo. Phasellus eCODE
LOWquestion/512.md234|文本生成|请根据下面这段文字,继续生成接下来的三个段落。Lorem ipsum dolor sit amet, consectetur adipiscing elit. Cras at tincidunt leo. Phasellus eCODE
LOWquestion/422.md275| 编辑 | 下列这段文字中哪些部分是需要重新编辑的?"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Fusce sit amet convallis leo." |CODE
LOWquestion/422.md275| 编辑 | 下列这段文字中哪些部分是需要重新编辑的?"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Fusce sit amet convallis leo." |CODE
LOWquestion/453.md477| | Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Maecenas porttitor congue massa. FuscCODE
LOWquestion/453.md477| | Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Maecenas porttitor congue massa. FuscCODE
LOWquestion/603.md1000|编辑文章|以下是一篇短文,请根据语言习惯进行编辑: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed venenatis risus at auctor eleiCODE
LOWquestion/603.md1000|编辑文章|以下是一篇短文,请根据语言习惯进行编辑: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed venenatis risus at auctor eleiCODE
LOWquestion/643.md1002| 分类任务 | 对于下列文本,请判断它们属于哪种语言类型:1. Lorem ipsum dolor sit amet, consectetur adipiscing elit. 2. こんにちは、元気ですか? |CODE
LOWquestion/643.md1002| 分类任务 | 对于下列文本,请判断它们属于哪种语言类型:1. Lorem ipsum dolor sit amet, consectetur adipiscing elit. 2. こんにちは、元気ですか? |CODE