CL4R1T4S is a repository that functions as a curated archive of extracted system prompts, tool definitions, and behavioral instructions from major AI models and agent platforms. It contains no executable code, data pipelines, or analytical scripts. The entire repository consists of flat text and markdown files organized by vendor (e.g., OPENAI, ANTHROPIC, XAI, CURSOR, REPLIT), each containing what are claimed to be verbatim system prompts for models like ChatGPT, Claude, Grok, Gemini, and various coding agents like Devin, Cursor, Windsurf, and Replit. The repository's stated purpose is transparency β making visible the hidden instructions that shape AI behavior. However, from a data engineering or software architecture perspective, there is no data lineage to trace, no transformation logic to evaluate, and no code to assess for craftsmanship. The files are static documents with no programmatic relationship between them. There are no tests, no dependency manifests, no environment definitions, and no data processing logic of any kind. The collection has some research value as a comparative corpus for prompt analysis, but it is entirely unstructured as data. There are no schemas, no metadata standards, no versioning discipline, and no way to verify the authenticity or currency of the extracted prompts. The README itself contains signs of prompt injection attempts and unprofessional editorializing that undermine the project's credibility as a serious transparency effort. There are no reusable components, no engineering patterns, and no data operations present.
This repository is a curated collection of documents (PDFs, images, and Markdown) related to the graduate entrance examination (Kaoyan), recommendation-based admission (Baoyan), and doctoral application (Kaobo) for the Institute of Information Engineering, Chinese Academy of Sciences (IIE CAS). It contains historical exam papers, admission statistics, interview experiences, summer camp lists, and detailed introductions to research labs. The repository serves as a knowledge base for prospective students, but contains no executable code, data pipelines, or analytical models. It is purely an archival resource with no software engineering or data engineering components.
This repository is a curated collection of study resources for the Certified Kubernetes Security Specialist (CKSS) exam. It consists of a README.md file containing organized links to official Kubernetes documentation and third-party references covering all exam domains (cluster setup, hardening, system hardening, microservice vulnerabilities, supply chain security, and monitoring/logging/runtime security). A bookmark HTML file provides browser-accessible versions of the same links. A PDF of the CKSS curriculum is also included. There is no executable code, data pipelines, models, or software architecture. The repository functions purely as a curated study guide or knowledge base, not as a functional application or data analysis project.
This repository contains a single README.md file that lists companies hiring remotely, with links to their career pages. There is no code, no data pipeline, no analysis, and no automation. It is a static, manually maintained list with no functional or reusable components. The repository has no application logic, no data transformations, and no software architecture. It is essentially a curated bookmark collection, not a software project or data research artifact.
This repository is a static collection of cheat sheets (images and PDFs) for various machine learning and data science libraries (TensorFlow, Keras, Pandas, NumPy, Scikit-learn, PySpark, etc.). It contains no executable code, no data pipelines, no notebooks, and no analytical models. The last commit was in 2019, and the repository has been inactive since. It serves purely as a reference resource for practitioners, not as a functional software project or data research artifact. There are no reusable components, no data lineage, and no reproducibility possible. The repository is effectively a curated set of external cheat sheets, not a codebase to be analyzed for architecture or engineering quality.
This repository is a literature survey on recent advances in large language model benchmarks against data contamination, specifically the transition from static to dynamic evaluation. It contains a single README.md file that lists and categorizes numerous research papers, benchmarks, and methods. There is no executable code, data pipelines, notebooks, or any software artifacts. The repository serves as a curated reference list and taxonomy for researchers, but offers no reusable code or data assets. It is not a functional application or a data research project with ETL or model training components.
This repository is a curated collection of research papers (PDFs) organized by topic, including AI, LLMs, adversarial machine learning, data streams, explainable AI, and more. It does not contain any executable code, notebooks, or data pipelines. The checkpoint files suggest some Spark streaming was performed, but the primary value is the paper inventory. It serves as a reference library for researchers and practitioners. There is no software to evaluate for craftsmanship or reusability.
This repository is a curated 'awesome list' of papers, datasets, and resources for talking head generation, covering image-driven, audio-driven, and NeRF/3D-based approaches. It contains no executable code, data pipelines, or modelsβonly a single README.md with organized tables of references. The repo serves as a research bibliography, not a software project. There are no assets to reproduce or reuse programmatically.