AI Slop Code Risk Monitor

ruanyf/weekly

Name: ruanyf/weekly — open-source 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).

科技爱好者周刊，每周五发布

0.1 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for ruanyf/weekly with 107,255 GitHub stars. SynthScan examined 166,419 lines of code across 429 source files, recording 8 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

2026-10-30

Last Push

107.3K

Stars

—

Language

166.4K

Lines of Code

429

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 0.6/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 8 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 0.0/100 · LOW

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

PR Review Velocity Impact 0.8/100 · LOW

Which changes need focused human review? 8 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 1MEDIUM 7LOW 0

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 8 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.

Modern AI Meta-Vocabulary7 hits · 14 pts

Severity	File	Line	Snippet	Context
MEDIUM	docs/issue-381.md	387	-- [曼纽尔·布卢姆](http://muratbuffalo.blogspot.com/2026/01/the-agentic-self-parallels-between-ai.html)（Manuel Blum），图灵奖得主	CODE
MEDIUM	docs/issue-408.md	25	我对于这些问题，以前也不是很了解，最近看了[一篇文章](https://blog.mempko.com/your-agentic-workflows-cache-keepalive-costs-8x-too-much-v2-the-inte	CODE
MEDIUM	docs/issue-408.md	49	根据[前面引用文章](https://blog.mempko.com/your-agentic-workflows-cache-keepalive-costs-8x-too-much-v2-the-interval-frontier/)的测	CODE
MEDIUM	docs/issue-388.md	249	1、[Agentic Metric](https://github.com/MrQianjinsi/agentic-metric)	CODE
MEDIUM	docs/issue-289.md	379	-- [阿联酋 AI 部长](https://fortune.com/2023/11/28/artificial-intelligence-ai-technology-regulation-policy-guardrails-uae-for	CODE
MEDIUM	docs/issue-387.md	123	1、[增强 AI 模型的一个技巧](https://simonwillison.net/guides/agentic-engineering-patterns/hoard-things-you-know-how-to-do/)（英文）	CODE
MEDIUM	docs/issue-393.md	311	-- [《ClickHouse 的 AI 编程实践》](https://clickhouse.com/blog/agentic-coding)	CODE

Synthetic Comment Markers1 hit · 5 pts

Severity	File	Line	Snippet	Context
HIGH	docs/issue-220.md	35	另一个开发者尝试生成博客文章的[插图](https://deephaven.io/blog/2022/08/08/AI-generated-blog-thumbnails/)。他有一篇 Docker 容器的介绍文章，插图描述是“一只蓝鲸背上	CODE