1.3

Adjusted Signal

1.3

Raw Pattern Score

100%

Time Factor

2026-10-25

Last Push

159.4K

Stars

JavaScript

Language

159.0K

Lines of Code

487

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 5.8/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 68 finding(s).

Hallucinated Dependency & Supply Chain Risk 0.1/100 · LOW

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

Agent Over-Engineering Ratio 0.2/100 · LOW

Are wrappers, guards, and repeated structures necessary? 4 finding(s).

PR Review Velocity Impact 6.0/100 · LOW

Which changes need focused human review? 73 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 61LOW 12

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 73 distinct pattern matches across 7 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-Vocabulary59 hits · 177 pts

Severity	File	Line	Snippet	Context
MEDIUM	docs/snippets/rag-project.snippet.md	1	## ⭐️ RAG 实战项目推荐	COMMENT
MEDIUM	docs/database/mysql/mysql-to-elasticsearch-sync.md	220	![MySQL 同步 ES 的一致性保障](https://oss.javaguide.cn/github/javaguide/database/es/mysql-es-consistency-guardrails.webp)	CODE
MEDIUM	docs/roadmap/java-to-ai-roadmap.md	121	### RAG 概念引入	COMMENT
MEDIUM	docs/roadmap/java-to-ai-roadmap.md	272	## 阶段三：RAG + 知识图谱（2~3 周）	COMMENT
MEDIUM	docs/roadmap/java-to-ai-roadmap.md	306	### 向量检索：RAG 的核心引擎	COMMENT
MEDIUM	docs/roadmap/java-to-ai-roadmap.md	328	### 知识图谱与 GraphRAG：给 RAG 加个逻辑骨架	COMMENT
MEDIUM	docs/roadmap/java-to-ai-roadmap.md	344	### RAG 评估：没有指标就是盲调	COMMENT
MEDIUM	docs/roadmap/java-to-ai-roadmap.md	362	### 从 RAG 到 Agentic RAG	COMMENT
MEDIUM	docs/ai/TODO.md	43	## P1 · RAG 深水区扩展	COMMENT
MEDIUM	docs/ai/README.md	115	### RAG 检索增强生成	COMMENT
MEDIUM	docs/ai/ai-core-concepts.md	406	## RAG	COMMENT
MEDIUM	docs/ai/ai-core-concepts.md	417	### 什么是 RAG？	COMMENT
MEDIUM	docs/ai/ai-core-concepts.md	429	### RAG 工作原理	COMMENT
MEDIUM	…i/interview-questions/agent-project-interview-guide.md	194	### RAG、Memory、Context 和 State 怎么区分？	COMMENT
MEDIUM	docs/ai/interview-questions/rag-interview-questions.md	17	## RAG 基础	COMMENT
MEDIUM	docs/ai/interview-questions/rag-interview-questions.md	73	## RAG 检索优化	COMMENT
MEDIUM	docs/ai/agent/multi-agent.md	21	[AI Agent 核心概念](./agent-basis.md#multi-agent) 已经介绍过 Orchestrator-Subagent、Peer-to-Peer 等协作模式。这篇文章继续讨论这些模式落到项目以后，任务、状态、冲突	CODE
MEDIUM	docs/ai/agent/prompt-engineering.md	535	### RAG 与混合检索	COMMENT
MEDIUM	docs/ai/agent/agent-basis.md	333	![智能体工作流核心模式](https://oss.javaguide.cn/github/javaguide/ai/agent/agent-agentic-workflows.png)	CODE
MEDIUM	docs/ai/agent/agent-memory.md	108	### 长期记忆和 RAG 有什么区别？	COMMENT
MEDIUM	docs/ai/agent/context-engineering.md	233	# 3. 使用 RAG（Retrieval-Augmented Generation）策略检索相关证据或上下文信息	COMMENT
MEDIUM	docs/ai/agent/context-engineering.md	328	### 动态上下文：RAG、记忆、工具结果不要一股脑塞	COMMENT
MEDIUM	docs/ai/rag/rag-evaluation.md	15	## 什么是 RAG 评测？	COMMENT
MEDIUM	docs/ai/rag/rag-evaluation.md	35	## 为什么需要 RAG 评测？	COMMENT
MEDIUM	docs/ai/rag/rag-optimization.md	15	## RAG 优化到底在优化什么？	COMMENT
MEDIUM	docs/ai/rag/rag-optimization.md	64	## RAG 优化闭环	COMMENT
MEDIUM⚡	docs/ai/rag/rag-basis.md	15	## 什么是 RAG？	COMMENT
MEDIUM⚡	docs/ai/rag/rag-basis.md	21	## 为什么需要 RAG？	COMMENT
MEDIUM⚡	docs/ai/rag/rag-basis.md	31	## RAG 的常见用途有哪些？	COMMENT
MEDIUM⚡	docs/ai/rag/rag-basis.md	41	## 为什么有些企业还是宁愿用传统搜索而不是 RAG？	COMMENT
MEDIUM	docs/ai/rag/rag-basis.md	61	## RAG 工作原理了解吗？	COMMENT
MEDIUM	docs/ai/rag/rag-basis.md	125	## RAG 与传统搜索引擎的区别是什么？	COMMENT
MEDIUM	docs/ai/rag/rag-basis.md	137	## RAG 和微调怎么选？	COMMENT
MEDIUM	docs/ai/rag/rag-basis.md	154	## 长上下文窗口会取代 RAG 吗？	COMMENT
MEDIUM	docs/ai/rag/rag-basis.md	167	## RAG 有哪些演进阶段？	COMMENT
MEDIUM	docs/ai/rag/rag-basis.md	181	## RAG 的工程局限有哪些？	COMMENT
MEDIUM	docs/ai/rag/rag-document-processing.md	371	### 完整的多模态 RAG 链路	COMMENT
MEDIUM	docs/ai/rag/graphrag.md	15	## 什么是 RAG？	COMMENT
MEDIUM	docs/ai/rag/graphrag.md	44	## 传统向量 RAG 有什么局限性？	COMMENT
MEDIUM	docs/ai/rag/graphrag.md	89	## GraphRAG 和传统向量 RAG 的区别	COMMENT
MEDIUM	docs/ai/rag/graphrag.md	428	### 阶段一：先做好向量 RAG 基线	COMMENT
MEDIUM	docs/ai/rag/graphrag.md	481	### 阶段五：按需引入 Hybrid RAG 路由	COMMENT
MEDIUM	docs/ai/rag/graphrag.md	538	## 与其他 RAG 增强路线的对比	COMMENT
MEDIUM	docs/ai/rag/rag-vector-store.md	32	## RAG 场景为什么需要向量数据库？	COMMENT
MEDIUM	docs/ai/system-design/llm-gateway.md	100	### LLM Gateway 和 RAG、Agent、MCP 是什么关系？	COMMENT
MEDIUM	docs/ai/system-design/ai-application-architecture.md	151	### RAG、Memory、Tool：三类上下文不要混在一起	COMMENT
MEDIUM	docs/ai/system-design/ai-application-architecture.md	321	## RAG 与 Memory：共享知识和个性化记忆怎么协作	COMMENT
MEDIUM	docs/ai/system-design/ai-observability.md	201	### RAG 检索信息	COMMENT
MEDIUM	docs/ai/system-design/ai-observability.md	295	### RAG 层指标	COMMENT
MEDIUM	docs/ai/system-design/ai-observability.md	320	### RAG 返回了错误答案，应该先看哪里？	COMMENT
MEDIUM	docs/ai/system-design/ai-observability.md	720	### 如何定位一次 RAG 错误回答？	COMMENT
MEDIUM	docs/ai/system-design/llm-security.md	789	- [OWASP Top 10 for Agentic Applications 2026](https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-fo	CODE
MEDIUM	docs/ai/system-design/llm-security.md	802	- [Anthropic：Mitigate Jailbreaks and Prompt Injections](https://platform.claude.com/docs/en/test-and-evaluate/strengthen	CODE
MEDIUM	docs/ai/llm-basis/llm-evaluation.md	294	## RAG 应用怎么评测？	COMMENT
MEDIUM	docs/ai/llm-basis/llm-evaluation.md	397	### RAG 评测的两个常见陷阱	COMMENT
MEDIUM	docs/ai/llm-basis/llm-evaluation.md	884	### 4. RAG 评测为什么必须分检索和生成两段？	COMMENT
MEDIUM	docs/ai/llm-basis/llm-evaluation.md	888	### 5. Agent 评测为什么比 RAG 更复杂？	COMMENT
MEDIUM	docs/high-availability/performance-test.md	220	![全链路压测：先把护栏搭好](https://oss.javaguide.cn/github/javaguide/high-availability/full-link-performance-test-guardrails.webp)	CODE
MEDIUM	docs/ai-coding/practices/spec-coding.md	417	### 关键词索引频繁漏召回时：评估 RAG	COMMENT

Structural Annotation Overuse4 hits · 9 pts

Severity	File	Line	Snippet	Context
LOW⚡	docs/high-performance/data-cold-hot-separation.md	146	// Step 1: 标记为迁移中（热库事务）	COMMENT
LOW⚡	docs/high-performance/data-cold-hot-separation.md	149	// Step 2: 读取热库数据并写入冷库（需切换数据库连接）	COMMENT
LOW⚡	docs/high-performance/data-cold-hot-separation.md	153	// Step 3: 标记为已迁移（热库事务）	COMMENT
LOW⚡	docs/high-performance/data-cold-hot-separation.md	156	// Step 4: 延迟删除热库数据（可选，确认冷库数据无误后执行）	COMMENT

Decorative Section Separators2 hits · 6 pts

Severity	File	Line	Snippet	Context
MEDIUM	docs/database/redis/redis-persistence.md	633	# ── Redis 持久化相关告警 ────────────────────────────────────────	COMMENT
MEDIUM	docs/database/redis/redis-persistence.md	674	# ── 节点级资源告警 ─────────────────────────────────────────────	COMMENT

Over-Commented Block4 hits · 4 pts

Severity	File	Line	Snippet	Context
LOW	docs/database/redis/redis-persistence.md	121	```bash	COMMENT
LOW	docs/java/jvm/jvm-parameters-intro.md	181	# --- 推荐的基础配置 ---	COMMENT
LOW	docs/java/basis/java-basic-questions-02.md	781	```java	COMMENT
LOW	docs/.vuepress/config.ts	21	["meta", { name: "robots", content: "all" }],	COMMENT

Fake / Example Data2 hits · 2 pts

Severity	File	Line	Snippet	Context
LOW	docs/system-design/security/jwt-intro.md	107	"name": "John Doe",	CODE
LOW	docs/ai-coding/practices/spec-coding.md	545	{ "id": "uuid", "email": "user@example.com", "created_at": "..." }	CODE

Hyper-Verbose Identifiers1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	…ai-coding/principles/claude-code-context-management.md	136	function getEffectiveContextWindowSize(	CODE

Excessive Try-Catch Wrapping1 hit · 0 pts

Severity	File	Line	Snippet	Context
LOW	docs/ai/rag/rag-knowledge-update.md	366	except Exception as e:	STRING

Snailclimb/JavaGuide

Open-Source Code Quality, Security and Technical Debt Review