979 skills found · Page 1 of 33
Abdess / RetrobiosComplete BIOS and firmware packs for RetroArch, Batocera, Recalbox, Lakka, RetroPie, EmuDeck, RetroBat, RetroDECK, RomM. Verified checksums, 6700+ files, 300+ emulators profiled from source code.
Authenticator-Extension / AuthenticatorAuthenticator generates 2-Step Verification codes in your browser.
JonathanSalwan / TritonTriton is a dynamic binary analysis library. Build your own program analysis tools, automate your reverse engineering, perform software verification or just emulate code.
Pimzino / Claude Code Spec WorkflowAutomated workflows for Claude Code. Features spec-driven development for new features (Requirements → Design → Tasks → Implementation) and streamlined bug fix workflow for quick issue resolution (Report → Analyze → Fix → Verify).
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.
kerlomz / Captcha Trainer[验证码识别-训练] This project is based on CNN/ResNet/DenseNet+GRU/LSTM+CTC/CrossEntropy to realize verification code identification. This project is only for training the model.
uditgoenka / AutoresearchClaude Autoresearch Skill — Autonomous goal-directed iteration for Claude Code. Inspired by Karpathy's autoresearch. Modify → Verify → Keep/Discard → Repeat forever.
verus-lang / VerusVerified Rust for low-level systems code
andeya / FaygoFaygo is a fast and concise Go Web framework that can be used to develop high-performance web app(especially API) with fewer codes. Just define a struct handler, faygo will automatically bind/verify the request parameters and generate the online API doc.
tianma8023 / XposedSmsCode:lollipop: An Xposed Module which can recognize, parse verification code and copy it to clipboard when a new message arrives . / 识别短信验证码的Xposed模块，并将验证码拷贝到剪切板。
CRAnimation / CRBoxInputViewVerify code input view. Support security type for password.短信验证码输入框，支持密文模式
argotorg / SourcifySource code verification service for Ethereum smart contracts
stfalcon-studio / SmsVerifyCatcherAndroid library for phone number verification feature in your app. Automatically copies verification code from SMS right into the app. Made by Stfalcon
Copilot-Language / CopilotA stream-based runtime-verification framework for generating hard real-time C code.
bcmi / Image Harmonization Dataset IHarmony4[CVPR 2020] The first large-scale public benchmark dataset for image harmonization. The code used in our paper "DoveNet: Deep Image Harmonization via Domain Verification", CVPR2020. Useful for image harmonization, image composition, etc.
AmElmo / ProofshotGive AI coding agents eyes. Records browser sessions, captures screenshots, collects errors, and bundles proof artifacts — so humans can verify what the agent built.
lidangzzz / Goal DrivenA multi-agent system that keeps running for ~100 hours and solve a very complicated coding or math problem that can be verified
SWE-Gym / SWE GymCode for Paper: Training Software Engineering Agents and Verifiers with SWE-Gym [ICML 2025]
GrapheneOS / AuditorHardware-based attestation and intrusion detection app for Android. It provides both local verification with another Android device via QR codes and optional scheduled server-based verification with support for alert emails. It uses hardware-backed keys and attestation support as the foundation and chains trust to the app for software checks.
ushelp / EasyOCRJava OCR 识别组件（基于Tesseract OCR 引擎）。能自动完成图片清理、识别 CAPTCHA 验证码图片内容的一体化工作。Java Image cleanup, OCR recognition component (based Tesseract OCR engine, automatically cleanup image and identification CAPTCHA verification code picture content).
