1,410 skills found · Page 1 of 47
K-Dense-AI / Claude Scientific SkillsA set of ready to use Agent Skills for research, science, engineering, analysis, finance and writing.
muratcankoylan / Agent Skills For Context EngineeringA comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.
kedro-org / KedroKedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
thewhiteh4t / SeekerAccurately Locate Smartphones using Social Engineering
chaosblade-io / ChaosbladeAn easy to use and powerful chaos engineering experiment toolkit.（阿里巴巴开源的一款简单易用、功能强大的混沌实验注入工具）
promptslab / PromptifyPrompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
ModelEngine-Group / NexentNexent is a zero-code platform for auto-generating production-grade AI agents using Harness Engineering principles — unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes.
NAalytics / Assemblies Of Putative SARS CoV2 Spike Encoding MRNA Sequences For Vaccines BNT 162b2 And MRNA 1273RNA vaccines have become a key tool in moving forward through the challenges raised both in the current pandemic and in numerous other public health and medical challenges. With the rollout of vaccines for COVID-19, these synthetic mRNAs have become broadly distributed RNA species in numerous human populations. Despite their ubiquity, sequences are not always available for such RNAs. Standard methods facilitate such sequencing. In this note, we provide experimental sequence information for the RNA components of the initial Moderna (https://pubmed.ncbi.nlm.nih.gov/32756549/) and Pfizer/BioNTech (https://pubmed.ncbi.nlm.nih.gov/33301246/) COVID-19 vaccines, allowing a working assembly of the former and a confirmation of previously reported sequence information for the latter RNA. Sharing of sequence information for broadly used therapeutics has the benefit of allowing any researchers or clinicians using sequencing approaches to rapidly identify such sequences as therapeutic-derived rather than host or infectious in origin. For this work, RNAs were obtained as discards from the small portions of vaccine doses that remained in vials after immunization; such portions would have been required to be otherwise discarded and were analyzed under FDA authorization for research use. To obtain the small amounts of RNA needed for characterization, vaccine remnants were phenol-chloroform extracted using TRIzol Reagent (Invitrogen), with intactness assessed by Agilent 2100 Bioanalyzer before and after extraction. Although our analysis mainly focused on RNAs obtained as soon as possible following discard, we also analyzed samples which had been refrigerated (~4 ℃) for up to 42 days with and without the addition of EDTA. Interestingly a substantial fraction of the RNA remained intact in these preparations. We note that the formulation of the vaccines includes numerous key chemical components which are quite possibly unstable under these conditions-- so these data certainly do not suggest that the vaccine as a biological agent is stable. But it is of interest that chemical stability of RNA itself is not sufficient to preclude eventual development of vaccines with a much less involved cold-chain storage and transportation. For further analysis, the initial RNAs were fragmented by heating to 94℃, primed with a random hexamer-tailed adaptor, amplified through a template-switch protocol (Takara SMARTerer Stranded RNA-seq kit), and sequenced using a MiSeq instrument (Illumina) with paired end 78-per end sequencing. As a reference material in specific assays, we included RNA of known concentration and sequence (from bacteriophage MS2). From these data, we obtained partial information on strandedness and a set of segments that could be used for assembly. This was particularly useful for the Moderna vaccine, for which the original vaccine RNA sequence was not available at the time our study was carried out. Contigs encoding full-length spikes were assembled from the Moderna and Pfizer datasets. The Pfizer/BioNTech data [Figure 1] verified the reported sequence for that vaccine (https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/), while the Moderna sequence [Figure 2] could not be checked against a published reference. RNA preparations lacking dsRNA are desirable in generating vaccine formulations as these will minimize an otherwise dramatic biological (and nonspecific) response that vertebrates have to double stranded character in RNA (https://www.nature.com/articles/nrd.2017.243). In the sequence data that we analyzed, we found that the vast majority of reads were from the expected sense strand. In addition, the minority of antisense reads appeared different from sense reads in lacking the characteristic extensions expected from the template switching protocol. Examining only the reads with an evident template switch (as an indicator for strand-of-origin), we observed that both vaccines overwhelmingly yielded sense reads (>99.99%). Independent sequencing assays and other experimental measurements are ongoing and will be needed to determine whether this template-switched sense read fraction in the SmarterSeq protocol indeed represents the actual dsRNA content in the original material. This work provides an initial assessment of two RNAs that are now a part of the human ecosystem and that are likely to appear in numerous other high throughput RNA-seq studies in which a fraction of the individuals may have previously been vaccinated. ProtoAcknowledgements: Thanks to our colleagues for help and suggestions (Nimit Jain, Emily Greenwald, Lamia Wahba, William Wang, Amisha Kumar, Sameer Sundrani, David Lipman, Bijoyita Roy). Figure 1: Spike-encoding contig assembled from BioNTech/Pfizer BNT-162b2 vaccine. Although the full coding region is included, the nature of the methodology used for sequencing and assembly is such that the assembled contig could lack some sequence from the ends of the RNA. Within the assembled sequence, this hypothetical sequence shows a perfect match to the corresponding sequence from documents available online derived from manufacturer communications with the World Health Organization [as reported by https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/]. The 5’ end for the assembly matches the start site noted in these documents, while the read-based assembly lacks an interrupted polyA tail (A30(GCATATGACT)A70) that is expected to be present in the mRNA.
shubhamgrg04 / Awesome DiagrammingA curated collection of diagramming tools used by leading software engineering teams
sevagas / Macro Packmacro_pack is a tool by @EmericNasi used to automatize obfuscation and generation of Office documents, VB scripts, shortcuts, and other formats for pentest, demo, and social engineering assessments. The goal of macro_pack is to simplify exploitation, antimalware bypass, and automatize the process from malicious macro and script generation to final document generation. It also provides a lot of helpful features useful for redteam or security research.
TryCatchHCF / CloakifyCloakifyFactory - Data Exfiltration & Infiltration In Plain Sight; Convert any filetype into list of everyday strings, using Text-Based Steganography; Evade DLP/MLS Devices, Defeat Data Whitelisting Controls, Social Engineering of Analysts, Evade AV Detection
C0nw0nk / Nginx Lua Anti DDoSA Anti-DDoS script to protect Nginx web servers using Lua with a HTML Javascript based authentication puzzle inspired by Cloudflare I am under attack mode an Anti-DDoS authentication page protect yourself from every attack type All Layer 7 Attacks Mitigating Historic Attacks DoS DoS Implications DDoS All Brute Force Attacks Zero day exploits Social Engineering Rainbow Tables Password Cracking Tools Password Lists Dictionary Attacks Time Delay Any Hosting Provider Any CMS or Custom Website Unlimited Attempt Frequency Search Attacks HTTP Basic Authentication HTTP Digest Authentication HTML Form Based Authentication Mask Attacks Rule-Based Search Attacks Combinator Attacks Botnet Attacks Unauthorized IPs IP Whitelisting Bruter THC Hydra John the Ripper Brutus Ophcrack unauthorized logins Injection Broken Authentication and Session Management Sensitive Data Exposure XML External Entities (XXE) Broken Access Control Security Misconfiguration Cross-Site Scripting (XSS) Insecure Deserialization Using Components with Known Vulnerabilities Insufficient Logging & Monitoring Drupal WordPress Joomla Flash Magento PHP Plone WHMCS Atlassian Products malicious traffic Adult video script avs KVS Kernel Video Sharing Clip Bucket Tube sites Content Management Systems Social networks scripts backends proxy proxies PHP Python Porn sites xxx adult gaming networks servers sites forums vbulletin phpbb mybb smf simple machines forum xenforo web hosting video streaming buffering ldap upstream downstream download upload rtmp vod video over dl hls dash hds mss livestream drm mp4 mp3 swf css js html php python sex m3u zip rar archive compressed mitigation code source sourcecode chan 4chan 4chan.org 8chan.net 8ch 8ch.net infinite chan 8kun 8kun.net anonymous anon tor services .onion torproject.org nginx.org nginx.com openresty.org darknet dark net deepweb deep web darkweb dark web mirror vpn reddit reddit.com adobe flash hackthissite.org dreamhack hack hacked hacking hacker hackers hackerz hackz hacks code coding script scripting scripter source leaks leaked leaking cve vulnerability great firewall china america japan russia .gov government http1 http2 http3 quic q3 litespeedtech litespeed apache torrents torrent torrenting webtorrent bittorrent bitorrent bit-torrent cyberlocker cyberlockers cyber locker cyberbunker warez keygen key generator free irc internet relay chat peer-to-peer p2p cryptocurrency crypto bitcoin miner browser xmr monero coinhive coin hive coin-hive litecoin ethereum cpu cycles popads pop-ads advert advertisement networks banner ads protect ovh blazingfast.io amazon steampowered valve store.steampowered.com steamcommunity thepiratebay lulzsec antisec xhamster pornhub porn.com pornhub.com xhamster.com xvideos xvdideos.com xnxx xnxx.com popads popcash cpm ppc
rojo-rbx / RojoRojo enables Roblox developers to use professional-grade software engineering tools
curiousily / Get Things Done With Prompt Engineering And LangChainLangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading and indexing data, creating prompt templates, CSV agents, and using retrieval QA chains to query the custom data. Projects for using a private LLM (Llama 2) for chat with PDF files, tweets sentiment analysis.
NVIDIA-Merlin / NVTabularNVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
Viralmaniar / I See YouISeeYou is a Bash and Javascript tool to find the exact location of the users during social engineering or phishing engagements. Using exact location coordinates an attacker can perform preliminary reconnaissance which will help them in performing further targeted attacks.
mrphrazer / Reverser AIProvides automated reverse engineering assistance through the use of local large language models (LLMs) on consumer hardware.
JonathanSalwan / Tigress ProtectionPlaying with the Tigress software protection. Break some of its protections and solve their reverse engineering challenges. Automatic deobfuscation using symbolic execution, taint analysis and LLVM.
panaverse / Learn Generative AILearn Cloud Applied Generative AI Engineering (GenEng) using OpenAI, Gemini, Streamlit, Containers, Serverless, Postgres, LangChain, Pinecone, and Next.js
ashishpatel26 / Amazing Feature EngineeringFeature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
