Repository Analysis

adongwanai/AgentGuide

https://adongwanai.github.io/AgentGuide | AI Agent开发指南 | LangGraph实战 | 高级RAG | 转行大模型 | 大模型面试 | 算法工程师 | 面试题库 | 强化学习|数据合成

4.6 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of adongwanai/AgentGuide, a HTML project with 6,977 GitHub stars. SynthScan v2.0 examined 117,151 lines of code across 215 source files, recording 198 pattern matches distributed across 9 syntactic categories. The overall adjusted score of 4.6 places this repository in the Likely human-written band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

4.6
Adjusted Score
4.6
Raw Score
100%
Time Factor
2026-07-09
Last Push
7.0K
Stars
HTML
Language
117.2K
Lines of Code
215
Files
198
Pattern Hits
2026-07-14
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 1MEDIUM 174LOW 23

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 198 distinct pattern matches across 9 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. 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 diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Modern AI Meta-Vocabulary144 hits · 416 pts
SeverityFileLineSnippetContext
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MEDIUMresources/tools.md262### 场景1:开发 RAG AgentCOMMENT
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MEDIUMresources/rag/papers/README.md1# RAG PapersCOMMENT
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MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md36#### Athina AI RAG Cookbooks ⭐⭐⭐⭐⭐COMMENT
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md42#### IBM Granite Agentic RAGCOMMENT
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md47#### NVIDIA Agentic RAGCOMMENT
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md48- **GitHub**: https://github.com/NVIDIA/workbench-example-agentic-ragCODE
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md56#### Azure GPT-RAG Agentic OrchestratorCOMMENT
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md57- **GitHub**: https://github.com/Azure/gpt-rag-agenticCODE
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md61#### Hierarchical Multi-Agent RAGCOMMENT
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md62- **GitHub**: https://github.com/lorenzejay/agentic-rag-practical-exampleCODE
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md70#### Hugging Face Agentic RAGCOMMENT
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md71- **GitHub**: https://github.com/aymericroucher/agentic-rag-query-reformulationCODE
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md92#### Redis Agentic RAGCOMMENT
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md93- **GitHub**: https://github.com/redis-developer/agentic-ragCODE
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md125#### Agentic RAG Survey (2025.01) ⭐⭐⭐⭐⭐COMMENT
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md147#### Self-RAG (ICLR 2024 Oral) ⭐⭐⭐⭐⭐COMMENT
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md558- Building Agentic RAG with LlamaIndex: https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamaindex/CODE
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md559- AI Agentic Design Patterns with AutoGen: https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autCODE
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md572- LlamaIndex Agentic RAG: https://www.llamaindex.ai/blog/agentic-rag-with-llamaindex-2721b8a49ff6CODE
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md579### Agentic RAG SurveyCOMMENT
MEDIUMresources/rag/papers/agentic_rag/agentic_rag.md591### Self-RAGCOMMENT
MEDIUMresources/rag/papers/agentic_rag/README.md1# Agentic RAG 论文汇总COMMENT
MEDIUMresources/rag/papers/agentic_rag/README.md15## RAG 与推理(RAG-Reasoning)COMMENT
MEDIUMresources/rag/papers/agentic_rag/README.md72## RAG 系统性思考COMMENT
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MEDIUMresources/rag/papers/agentic_rag/README.md152##### RAG 风险COMMENT
MEDIUMresources/rag/papers/multimodal_rag/README.md1# Multimodal RAG PapersCOMMENT
MEDIUMresources/multimodal/tools-and-projects.md1# 多模态 RAG 工具与项目清单COMMENT
MEDIUMresources/multimodal/tools-and-projects.md36## 一体化多模态 RAGCOMMENT
MEDIUMresources/multimodal/tools-and-projects.md38### RAG-AnythingCOMMENT
MEDIUMresources/multimodal/evaluation-checklist.md1# 多模态 RAG 评测清单COMMENT
MEDIUMresources/multimodal/multimodal-rag-pipeline.md1# 多模态 RAG 管线COMMENT
MEDIUMresources/multimodal/README.md1# Multimodal RAG 与文档智能资源COMMENT
MEDIUMresources/multimodal/README.md25### 项目 2:财报多模态 RAGCOMMENT
MEDIUMdocs/03-practice/02-high-availability-rag.md1# 高可用 RAG 系统实战COMMENT
MEDIUMdocs/03-practice/03-agent-security.md109## RAG / 文档 Agent 安全COMMENT
MEDIUMdocs/archive/2.md87### 5. RAGCOMMENT
MEDIUMdocs/04-interview/06-development-specialized.md32### 高并发 RAG 系统设计COMMENT
MEDIUMdocs/04-interview/06-development-specialized.md37- 标签:#系统设计 #高并发 #RAGCODE
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MEDIUMdocs/04-interview/06-development-specialized.md147- 标签:#SaaS #多租户 #RAGCODE
MEDIUMdocs/04-interview/06-development-specialized.md183- 标签:#知识库 #增量更新 #RAGCODE
MEDIUMdocs/04-interview/06-development-specialized.md207- 标签:#混合检索 #RAG #系统设计CODE
MEDIUMdocs/04-interview/06-development-specialized.md224- 标签:#性能优化 #延迟 #RAGCODE
MEDIUMdocs/04-interview/06-development-specialized.md248- 标签:#向量检索 #优化 #RAGCODE
84 more matches not shown…
Decorative Section Separators28 hits · 88 pts
SeverityFileLineSnippetContext
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Structural Annotation Overuse10 hits · 20 pts
SeverityFileLineSnippetContext
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LOWCONTRIBUTING.md126### Step 7: 提交 Pull RequestCOMMENT
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LOW…02-tech-stack/22-parlant-agent-compliance-deep-dive.md258#### Step 3: 响应验证(可选)COMMENT
Excessive Try-Catch Wrapping8 hits · 8 pts
SeverityFileLineSnippetContext
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Magic Placeholder Names1 hit · 5 pts
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Hyper-Verbose Identifiers3 hits · 2 pts
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LOWexternal/InterviewGuide/src/lib/path.ts54function normalizeCompanyRouteName(name: string): string {CODE
Over-Commented Block2 hits · 2 pts
SeverityFileLineSnippetContext
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Unused Imports1 hit · 1 pts
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Deep Nesting1 hit · 1 pts
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LOWscripts/generate_resources.py95CODE