Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced Prompt Engineering papers.
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).
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.
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.
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.
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.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | prompts/incident_response_commander.md | 346 | ### Step 1: Incident Detection & Declaration | COMMENT |
| LOW⚡ | prompts/incident_response_commander.md | 352 | ### Step 2: Structured Response & Coordination | COMMENT |
| LOW⚡ | prompts/incident_response_commander.md | 359 | ### Step 3: Resolution & Stabilization | COMMENT |
| LOW⚡ | prompts/incident_response_commander.md | 365 | ### Step 4: Post-Mortem & Continuous Improvement | COMMENT |
| LOW⚡ | prompts/data_engineer.md | 216 | ### Step 1: Source Discovery & Contract Definition | COMMENT |
| LOW⚡ | prompts/data_engineer.md | 222 | ### Step 2: Bronze Layer (Raw Ingest) | COMMENT |
| LOW⚡ | prompts/data_engineer.md | 228 | ### Step 3: Silver Layer (Cleanse & Conform) | COMMENT |
| LOW⚡ | prompts/data_engineer.md | 234 | ### Step 4: Gold Layer (Business Metrics) | COMMENT |
| LOW⚡ | prompts/data_engineer.md | 240 | ### Step 5: Observability & Ops | COMMENT |
| LOW | prompts/doubt_driven_development.txt | 52 | ### Step 1: CLAIM — Surface what stands | COMMENT |
| LOW | prompts/doubt_driven_development.txt | 65 | ### Step 2: EXTRACT — Smallest reviewable unit | COMMENT |
| LOW | prompts/doubt_driven_development.txt | 75 | ### Step 3: DOUBT — Invoke the fresh-context reviewer | COMMENT |
| LOW | prompts/doubt_driven_development.txt | 112 | ### Step 4: RECONCILE — Fold findings back | COMMENT |
| LOW | prompts/doubt_driven_development.txt | 125 | ### Step 5: STOP — Bounded loop, not recursion | COMMENT |
| LOW | prompts/latex_academic_expert.txt | 26 | ### Step 1: Identify Source and Target | COMMENT |
| LOW | prompts/latex_academic_expert.txt | 29 | ### Step 2: Apply Template Changes | COMMENT |
| LOW⚡ | prompts/latex_academic_expert.txt | 47 | ### Step 3: Section Reordering | COMMENT |
| LOW⚡ | prompts/latex_academic_expert.txt | 50 | ### Step 4: Citation and Bibliography | COMMENT |
| LOW⚡ | prompts/latex_academic_expert.txt | 60 | ### Step 5: Anonymization (Double-Blind Venues) | COMMENT |
| LOW⚡ | prompts/latex_academic_expert.txt | 63 | ### Step 6: Pre-Submission Checklist | COMMENT |
| LOW | prompts/website_clone_architect.txt | 233 | ### Step 1: Extract | COMMENT |
| LOW | prompts/website_clone_architect.txt | 302 | ### Step 2: Write the Component Spec File | COMMENT |
| LOW | prompts/website_clone_architect.txt | 377 | ### Step 3: Dispatch Builders | COMMENT |
| LOW | prompts/website_clone_architect.txt | 395 | ### Step 4: Merge | COMMENT |
| LOW⚡ | prompts/mobile_app_builder.txt | 344 | ### Step 1: Platform Strategy and Setup | COMMENT |
| LOW⚡ | prompts/mobile_app_builder.txt | 349 | ### Step 2: Architecture and Design | COMMENT |
| LOW⚡ | prompts/mobile_app_builder.txt | 355 | ### Step 3: Development and Integration | COMMENT |
| LOW⚡ | prompts/mobile_app_builder.txt | 361 | ### Step 4: Testing and Deployment | COMMENT |
| LOW⚡ | prompts/solidity_smart_contract_engineer.txt | 383 | ### Step 1: Requirements & Threat Modeling | COMMENT |
| LOW⚡ | prompts/solidity_smart_contract_engineer.txt | 389 | ### Step 2: Architecture & Interface Design | COMMENT |
| LOW⚡ | prompts/solidity_smart_contract_engineer.txt | 395 | ### Step 3: Implementation & Gas Profiling | COMMENT |
| LOW⚡ | prompts/solidity_smart_contract_engineer.txt | 401 | ### Step 4: Testing & Verification | COMMENT |
| LOW⚡ | prompts/solidity_smart_contract_engineer.txt | 408 | ### Step 5: Audit Preparation & Deployment | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | README.md | 71 | - [📄 Papers](#papers) — Foundations, Optimization, Reasoning, RAG, Agents, Multi-Agent, Safety, Self-Improving Agents, T | CODE |
| MEDIUM | README.md | 570 | | [**Terminal-Bench**](https://github.com/laude-institute/terminal-bench) | — | Real-terminal agent benchmark (Stanford/ | CODE |
| MEDIUM | README.md | 590 | | [**DeepEval**](https://github.com/confident-ai/deepeval) |  | OpenAI | Official JavaScript/TypeScrip | CODE |
| MEDIUM⚡ | README.md | 686 | | [**GitHub Agentic Workflows (gh-aw)**](https://github.com/github/gh-aw) | GitHub | Security-first agentic workflows fo | CODE |
| MEDIUM⚡ | README.md | 691 | | [**openai/codex**](https://github.com/openai/codex) | OpenAI | Lightweight agentic coding CLI — o3/o4-mini powered, ru | CODE |
| MEDIUM⚡ | README.md | 697 | | [**PraisonAI**](https://github.com/MervinPraison/PraisonAI) | Mervin Praison | Production-ready multi-agent framework | CODE |
| MEDIUM | README.md | 786 | | **OpenAI** | [GPT-5.2 Prompting Guide](https://cookbook.openai.com/examples/gpt-5/gpt-5-2_prompting_guide) (Dec 2025) | CODE |
| MEDIUM | README.md | 796 | | **Google** | [Agents Companion Whitepaper](https://www.kaggle.com/whitepaper-agent-companion) (2026) | Agents — 76-pag | CODE |
| MEDIUM | README.md | 852 | | [Agentic Code Reasoning (March 2026)](https://arxiv.org/abs/2603.01896) | Semi-formal reasoning using structured templ | CODE |
| MEDIUM | README.md | 855 | | [RAGEN-2: Reasoning Collapse in Agentic RL (April 2026)](https://arxiv.org/abs/2604.06268) | Identifies "template coll | CODE |
| MEDIUM | README.md | 868 | | [Credit Assignment in Reinforcement Learning for Large Language Models (April 2026)](https://arxiv.org/abs/2604.09459) | CODE |
| MEDIUM | README.md | 871 | ### RAG & Knowledge | COMMENT |
| MEDIUM | README.md | 897 | | [From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents (July 2026)](https://arxiv.org/abs | CODE |
| MEDIUM | README.md | 936 | | [Attack and Defense Landscape of Agentic AI (2026)](https://arxiv.org/abs/2603.11088) | Dawn Song (UC Berkeley) et al. | CODE |
| MEDIUM | README.md | 961 | | [Context Engineering: From Prompts to Corporate Multi-Agent Architecture (2026)](https://arxiv.org/abs/2603.09619) | D | CODE |
| MEDIUM | README.md | 978 | | [Agentic Tool Use in Large Language Models (April 2026)](https://arxiv.org/abs/2604.00835) | Comprehensive framework f | CODE |
| MEDIUM | README.md | 989 | | [Agent Psychometrics: Task-Level Performance Prediction (April 2026)](https://arxiv.org/abs/2604.00594) | Shifts evalu | CODE |
| MEDIUM | README.md | 1022 | | [Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence? (April 2026)](https://arxiv.org/abs/26 | CODE |
| MEDIUM | README.md | 1066 | | [Awesome-Agentic-Reasoning](https://github.com/weitianxin/Awesome-Agentic-Reasoning) | Papers and resources on agentic | CODE |
| MEDIUM | prompts/technical_diagram_engineer.txt | 153 | | Async / event | #6b7280 | 1.5px | 4,2 | Non-blocking, event-driven | | CODE |
| MEDIUM | prompts/llm_architect.txt | 24 | ### RAG Implementation | COMMENT |
| MEDIUM | prompts/llm_architect.txt | 94 | ## RAG Architecture Template | COMMENT |
| MEDIUM | prompts/agents_best_practices.txt | 192 | - OpenAI guardrails and human review: https://developers.openai.com/api/docs/guides/agents/guardrails-approvals | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | prompts/structured_output_extractor.txt | 64 | "name": "Jane Doe", | CODE |
| LOW | prompts/structured_output_extractor.txt | 72 | { "name": "Jane Doe", "notes": "..." } ← "notes" not in schema | CODE |
| LOW | prompts/structured_output_extractor.txt | 113 | Output: {"company_name":"Acme Corp","founding_year":1985,"headquarters":"Austin, TX", | CODE |
| LOW | prompts/generative_ui_architect.txt | 20 | - Avoid placeholder text like "Lorem ipsum"; use realistic, context-appropriate copy. | CODE |
| LOW | prompts/html_anything_publisher.txt | 58 | ≥ 8 px, no emoji decoration, no Lorem ipsum, no placeholder image URLs). | CODE |
| LOW | prompts/claude_artifacts_prompt.md | 63 | - Images from the web are not allowed, but you can use placeholder images by specifying the width and height like so < | CODE |
| LOW | prompts/claude_artifacts_prompt.md | 82 | - Images from the web are not allowed, but you can use placeholder images by specifying the width and height like so <i | CODE |
| LOW | prompts/frontend_taste_engineer.txt | 117 | ### Content & Data (The "Jane Doe" Effect) | COMMENT |
| LOW | prompts/frontend_taste_engineer.txt | 118 | * **NO Generic Names:** "John Doe", "Sarah Chan", or "Jack Su" are banned. Use highly creative, realistic-sounding names | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | prompts/data_engineer.md | 65 | # ── Bronze: raw ingest (append-only, schema-on-read) ───────────────────────── | COMMENT |
| MEDIUM | prompts/data_engineer.md | 74 | # ── Silver: cleanse, deduplicate, conform ──────────────────────────────────── | COMMENT |
| MEDIUM | prompts/data_engineer.md | 93 | # ── Gold: aggregated business metric ───────────────────────────────────────── | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | prompts/technical_writer.txt | 99 | signal the pattern: YOUR_API_KEY, YOUR_PROJECT_ID. Never use real-looking values. | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | prompts/mobile_app_builder.txt | 117 | print("Error loading products: \(error)") | CODE |