Repository Analysis

ai-boost/awesome-prompts

Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced Prompt Engineering papers.

2.7 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of ai-boost/awesome-prompts with 8,464 GitHub stars. SynthScan v2.0 examined 53,455 lines of code across 350 source files, recording 71 pattern matches distributed across 6 syntactic categories. The overall adjusted score of 2.7 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).

2.7
Adjusted Score
2.7
Raw Score
100%
Time Factor
2026-07-14
Last Push
8.5K
Stars
Language
53.5K
Lines of Code
350
Files
71
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

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

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 28LOW 42

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 71 distinct pattern matches across 6 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.

Structural Annotation Overuse33 hits · 66 pts
SeverityFileLineSnippetContext
LOWprompts/incident_response_commander.md346### Step 1: Incident Detection & DeclarationCOMMENT
LOWprompts/incident_response_commander.md352### Step 2: Structured Response & CoordinationCOMMENT
LOWprompts/incident_response_commander.md359### Step 3: Resolution & StabilizationCOMMENT
LOWprompts/incident_response_commander.md365### Step 4: Post-Mortem & Continuous ImprovementCOMMENT
LOWprompts/data_engineer.md216### Step 1: Source Discovery & Contract DefinitionCOMMENT
LOWprompts/data_engineer.md222### Step 2: Bronze Layer (Raw Ingest)COMMENT
LOWprompts/data_engineer.md228### Step 3: Silver Layer (Cleanse & Conform)COMMENT
LOWprompts/data_engineer.md234### Step 4: Gold Layer (Business Metrics)COMMENT
LOWprompts/data_engineer.md240### Step 5: Observability & OpsCOMMENT
LOWprompts/doubt_driven_development.txt52### Step 1: CLAIM — Surface what standsCOMMENT
LOWprompts/doubt_driven_development.txt65### Step 2: EXTRACT — Smallest reviewable unitCOMMENT
LOWprompts/doubt_driven_development.txt75### Step 3: DOUBT — Invoke the fresh-context reviewerCOMMENT
LOWprompts/doubt_driven_development.txt112### Step 4: RECONCILE — Fold findings backCOMMENT
LOWprompts/doubt_driven_development.txt125### Step 5: STOP — Bounded loop, not recursionCOMMENT
LOWprompts/latex_academic_expert.txt26### Step 1: Identify Source and TargetCOMMENT
LOWprompts/latex_academic_expert.txt29### Step 2: Apply Template ChangesCOMMENT
LOWprompts/latex_academic_expert.txt47### Step 3: Section ReorderingCOMMENT
LOWprompts/latex_academic_expert.txt50### Step 4: Citation and BibliographyCOMMENT
LOWprompts/latex_academic_expert.txt60### Step 5: Anonymization (Double-Blind Venues)COMMENT
LOWprompts/latex_academic_expert.txt63### Step 6: Pre-Submission ChecklistCOMMENT
LOWprompts/website_clone_architect.txt233### Step 1: ExtractCOMMENT
LOWprompts/website_clone_architect.txt302### Step 2: Write the Component Spec FileCOMMENT
LOWprompts/website_clone_architect.txt377### Step 3: Dispatch BuildersCOMMENT
LOWprompts/website_clone_architect.txt395### Step 4: MergeCOMMENT
LOWprompts/mobile_app_builder.txt344### Step 1: Platform Strategy and SetupCOMMENT
LOWprompts/mobile_app_builder.txt349### Step 2: Architecture and DesignCOMMENT
LOWprompts/mobile_app_builder.txt355### Step 3: Development and IntegrationCOMMENT
LOWprompts/mobile_app_builder.txt361### Step 4: Testing and DeploymentCOMMENT
LOWprompts/solidity_smart_contract_engineer.txt383### Step 1: Requirements & Threat ModelingCOMMENT
LOWprompts/solidity_smart_contract_engineer.txt389### Step 2: Architecture & Interface DesignCOMMENT
LOWprompts/solidity_smart_contract_engineer.txt395### Step 3: Implementation & Gas ProfilingCOMMENT
LOWprompts/solidity_smart_contract_engineer.txt401### Step 4: Testing & VerificationCOMMENT
LOWprompts/solidity_smart_contract_engineer.txt408### Step 5: Audit Preparation & DeploymentCOMMENT
Modern AI Meta-Vocabulary24 hits · 55 pts
SeverityFileLineSnippetContext
MEDIUMREADME.md71- [📄 Papers](#papers) — Foundations, Optimization, Reasoning, RAG, Agents, Multi-Agent, Safety, Self-Improving Agents, TCODE
MEDIUMREADME.md570| [**Terminal-Bench**](https://github.com/laude-institute/terminal-bench) | — | Real-terminal agent benchmark (Stanford/CODE
MEDIUMREADME.md590| [**DeepEval**](https://github.com/confident-ai/deepeval) | ![](https://img.shields.io/github/stars/confident-ai/deepevCODE
MEDIUMREADME.md685| [**OpenAI Agents SDK for JS/TS**](https://github.com/openai/openai-agents-js) | OpenAI | Official JavaScript/TypeScripCODE
MEDIUMREADME.md686| [**GitHub Agentic Workflows (gh-aw)**](https://github.com/github/gh-aw) | GitHub | Security-first agentic workflows foCODE
MEDIUMREADME.md691| [**openai/codex**](https://github.com/openai/codex) | OpenAI | Lightweight agentic coding CLI — o3/o4-mini powered, ruCODE
MEDIUMREADME.md697| [**PraisonAI**](https://github.com/MervinPraison/PraisonAI) | Mervin Praison | Production-ready multi-agent framework CODE
MEDIUMREADME.md786| **OpenAI** | [GPT-5.2 Prompting Guide](https://cookbook.openai.com/examples/gpt-5/gpt-5-2_prompting_guide) (Dec 2025) CODE
MEDIUMREADME.md796| **Google** | [Agents Companion Whitepaper](https://www.kaggle.com/whitepaper-agent-companion) (2026) | Agents — 76-pagCODE
MEDIUMREADME.md852| [Agentic Code Reasoning (March 2026)](https://arxiv.org/abs/2603.01896) | Semi-formal reasoning using structured templCODE
MEDIUMREADME.md855| [RAGEN-2: Reasoning Collapse in Agentic RL (April 2026)](https://arxiv.org/abs/2604.06268) | Identifies "template collCODE
MEDIUMREADME.md868| [Credit Assignment in Reinforcement Learning for Large Language Models (April 2026)](https://arxiv.org/abs/2604.09459)CODE
MEDIUMREADME.md871### RAG & KnowledgeCOMMENT
MEDIUMREADME.md897| [From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents (July 2026)](https://arxiv.org/absCODE
MEDIUMREADME.md936| [Attack and Defense Landscape of Agentic AI (2026)](https://arxiv.org/abs/2603.11088) | Dawn Song (UC Berkeley) et al.CODE
MEDIUMREADME.md961| [Context Engineering: From Prompts to Corporate Multi-Agent Architecture (2026)](https://arxiv.org/abs/2603.09619) | DCODE
MEDIUMREADME.md978| [Agentic Tool Use in Large Language Models (April 2026)](https://arxiv.org/abs/2604.00835) | Comprehensive framework fCODE
MEDIUMREADME.md989| [Agent Psychometrics: Task-Level Performance Prediction (April 2026)](https://arxiv.org/abs/2604.00594) | Shifts evaluCODE
MEDIUMREADME.md1022| [Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence? (April 2026)](https://arxiv.org/abs/26CODE
MEDIUMREADME.md1066| [Awesome-Agentic-Reasoning](https://github.com/weitianxin/Awesome-Agentic-Reasoning) | Papers and resources on agenticCODE
MEDIUMprompts/technical_diagram_engineer.txt153| Async / event | #6b7280 | 1.5px | 4,2 | Non-blocking, event-driven |CODE
MEDIUMprompts/llm_architect.txt24### RAG ImplementationCOMMENT
MEDIUMprompts/llm_architect.txt94## RAG Architecture TemplateCOMMENT
MEDIUMprompts/agents_best_practices.txt192- OpenAI guardrails and human review: https://developers.openai.com/api/docs/guides/agents/guardrails-approvalsCODE
Fake / Example Data9 hits · 10 pts
SeverityFileLineSnippetContext
LOWprompts/structured_output_extractor.txt64 "name": "Jane Doe",CODE
LOWprompts/structured_output_extractor.txt72 { "name": "Jane Doe", "notes": "..." } ← "notes" not in schemaCODE
LOWprompts/structured_output_extractor.txt113Output: {"company_name":"Acme Corp","founding_year":1985,"headquarters":"Austin, TX",CODE
LOWprompts/generative_ui_architect.txt20- Avoid placeholder text like "Lorem ipsum"; use realistic, context-appropriate copy.CODE
LOWprompts/html_anything_publisher.txt58 ≥ 8 px, no emoji decoration, no Lorem ipsum, no placeholder image URLs).CODE
LOWprompts/claude_artifacts_prompt.md63 - Images from the web are not allowed, but you can use placeholder images by specifying the width and height like so <CODE
LOWprompts/claude_artifacts_prompt.md82 - Images from the web are not allowed, but you can use placeholder images by specifying the width and height like so <iCODE
LOWprompts/frontend_taste_engineer.txt117### Content & Data (The "Jane Doe" Effect)COMMENT
LOWprompts/frontend_taste_engineer.txt118* **NO Generic Names:** "John Doe", "Sarah Chan", or "Jack Su" are banned. Use highly creative, realistic-sounding namesCOMMENT
Decorative Section Separators3 hits · 9 pts
SeverityFileLineSnippetContext
MEDIUMprompts/data_engineer.md65# ── Bronze: raw ingest (append-only, schema-on-read) ─────────────────────────COMMENT
MEDIUMprompts/data_engineer.md74# ── Silver: cleanse, deduplicate, conform ────────────────────────────────────COMMENT
MEDIUMprompts/data_engineer.md93# ── Gold: aggregated business metric ─────────────────────────────────────────COMMENT
Magic Placeholder Names1 hit · 5 pts
SeverityFileLineSnippetContext
HIGHprompts/technical_writer.txt99 signal the pattern: YOUR_API_KEY, YOUR_PROJECT_ID. Never use real-looking values.CODE
Excessive Try-Catch Wrapping1 hit · 2 pts
SeverityFileLineSnippetContext
MEDIUMprompts/mobile_app_builder.txt117 print("Error loading products: \(error)")CODE