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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. ", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "RNA 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. ", "description_zh": "RNA 疫苗已成为应对当前疫情及众多其他公共卫生和医学挑战的重要工具。随着 COVID-19 疫苗推广，这些合成 mRNA 已成为广泛分布于众多人群中的 RNA 类型。尽管它们十分普遍，其序列却并非总是可得；标准方法可以完成此类测序。本文提供初版 Moderna（https://pubmed.ncbi.nlm.nih.gov/32756549/）和 Pfizer/BioNTech（https://pubmed.ncbi.nlm.nih.gov/33301246/）COVID-19 疫苗 RNA 成分的实验序列信息，从而组装前者的可用序列，并确认后者此前报道的 RNA 序列。共享广泛使用的治疗制剂的序列信息，有助于采用测序方法的研究人员或临床医生迅速判断这些序列来自治疗制剂，而非宿主或感染源。本研究的 RNA 来自接种后疫苗瓶中剩余少量剂量的废弃部分；这些部分原本必须丢弃，并在 FDA 授权的研究用途下接受分析。为获得表征所需的少量 RNA，我们使用 TRIzol Reagent（Invitrogen）对疫苗残留物进行酚-氯仿提取，并在提取前后用 Agilent 2100 Bioanalyzer 评估其完整性。虽然分析主要针对废弃后尽快获取的 RNA，我们也分析了添加或未添加 EDTA、在约 4 ℃ 冷藏最长 42 天的样本。有意思的是，这些制备物中相当一部分 RNA 仍保持完整。需要说明的是，疫苗配方包含多种关键化学成分，它们在这些条件下很可能不稳定，因此这些数据绝不意味着疫苗作为生物制剂仍然稳定。不过，RNA 本身的化学稳定性并不足以排除未来开发出冷链储运要求低得多的疫苗，这一点值得关注。进一步分析时，先将 RNA 加热至 94℃ 使其片段化，用带随机六聚体尾部接头的引物引发，再采用模板切换方案（Takara SMARTerer Stranded RNA-seq kit）扩增，并使用 MiSeq 仪器（Illumina）进行双端测序，每端 78 个碱基。在特定测定中，我们纳入已知浓度和序列的 RNA（来自 MS2 噬菌体）作为参考材料。从这些数据中获得了部分链向信息，以及一组可用于组装的片段。这对 Moderna 疫苗尤为有用，因为研究进行时其原始疫苗 RNA 序列尚未公开。我们从 Moderna 和 Pfizer 数据集中组装出编码全长刺突蛋白的重叠群。Pfizer/BioNTech 数据［图 1］验证了此前报道的该疫苗序列（https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/），而 Moderna 序列［图 2］则无法与已发表的参考序列核对。疫苗配方宜使用不含 dsRNA 的 RNA 制备物，以尽量降低脊椎动物对 RNA 双链特征产生的强烈且非特异性的生物反应（https://www.nature.com/articles/nrd.2017.243）。在我们分析的序列数据中，绝大多数读段来自预期的正义链。此外，少数反义链读段似乎与正义链读段不同，缺少模板切换方案预期产生的特征性延伸。仅检查具有明显模板切换的读段（将其作为来源链的指示）时，两种疫苗产生的读段均以正义链为绝大多数（>99.99%）。独立测序及其他实验测量仍在进行，需借助这些实验判断 SmarterSeq 方案中经过模板切换的正义链读段比例，是否确实反映原始材料中的实际 dsRNA 含量。本研究对两种现已成为人类生态系统组成部分的 RNA 作出初步评估；在部分受试者可能已接种疫苗的其他高通量 RNA-seq 研究中，它们也很可能出现。初步致谢：感谢同事们的帮助与建议（Nimit Jain、Emily Greenwald、Lamia Wahba、William Wang、Amisha Kumar、Sameer Sundrani、David Lipman、Bijoyita Roy）。图 1：由 BioNTech/Pfizer BNT-162b2 疫苗组装的刺突蛋白编码重叠群。尽管包含完整编码区，所用测序与组装方法的性质决定了组装结果可能缺少 RNA 两端的部分序列。在已组装范围内，该推定序列与网上公开文档中的对应序列完全匹配，这些文档来源于制造商与世界卫生组织的通信［见 https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/ 的报道］。组装序列的 5’ 端与这些文档所述起始位点一致，但基于读段的组装结果缺少预期存在于 mRNA 中的间断 polyA 尾（A30(GCATATGACT)A70）。", "description_zh_status": "translated", "full_name": "NAalytics/Assemblies-of-putative-SARS-CoV2-spike-encoding-mRNA-sequences-for-vaccines-BNT-162b2-and-mRNA-1273", "id": "scan-350913590", "license": null, "matched_queries": [ "\"reverse engineering\" in:name,description is:public fork:false archived:false" ], "metadata_observed_at": "2026-10-16T01:34:02.369916Z", "name": "NAalytics/Assemblies-of-putative-SARS-CoV2-spike-encoding-mRNA-sequences-for-vaccines-BNT-162b2-and-mRNA-1273", "observed_at": "2026-10-16T01:35:26.950407Z", "pushed_at": "2021-12-14T23:40:15Z", "readme_api_url": "https://api.github.com/repos/NAalytics/Assemblies-of-putative-SARS-CoV2-spike-encoding-mRNA-sequences-for-vaccines-BNT-162b2-and-mRNA-1273/readme?ref=main", "readme_bytes": 7750, "readme_git_sha": "56563c6f3c9b16d57a8faa9ea121708a6c32cd80", "readme_heading_count": 1, "readme_lines": 33, "readme_path": "README.md", "readme_sha256": "0e4382b187ed30f8a5594555bd29b45f453a7d74da4f2a4b60130fdac9d02133", "readme_signals": [], "readme_url": "https://github.com/NAalytics/Assemblies-of-putative-SARS-CoV2-spike-encoding-mRNA-sequences-for-vaccines-BNT-162b2-and-mRNA-1273/blob/main/README.md", "requirements": "See upstream requirements; not manually reviewed.", "scan_status": "readme-scanned", "scope_review": { "decision": "exclude", "evidence_url": "https://github.com/NAalytics/Assemblies-of-putative-SARS-CoV2-spike-encoding-mRNA-sequences-for-vaccines-BNT-162b2-and-mRNA-1273/blob/main/README.md", "readme_sha256": "0e4382b187ed30f8a5594555bd29b45f453a7d74da4f2a4b60130fdac9d02133", "reason_code": "outside-software-biology", "reason_en": "Vaccine RNA sequencing and assembly research is outside this collection’s software and hardware reverse-engineering scope.", "reason_zh": "疫苗 RNA 测序与序列组装研究不属于本合集的软件与硬件逆向工程范围。", "reviewed_at": "2026-10-16T04:09:02Z" }, "scope_status": "excluded", "stars": 3355, "targets": [ "binary" ], "topics": [], "updated_at": "2026-10-12T21:14:57Z", "url": "https://github.com/NAalytics/Assemblies-of-putative-SARS-CoV2-spike-encoding-mRNA-sequences-for-vaccines-BNT-162b2-and-mRNA-1273", "verification": "automated-readme-scan" }, { "archived": false, "category": "android", "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_en": "Using Zygisk to dump il2cpp data at runtime", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "Using Zygisk to dump il2cpp data at runtime", "description_zh": "使用 Zygisk 在运行时转储 il2cpp 数据。", "description_zh_status": "translated", "full_name": "Perfare/Zygisk-Il2CppDumper", "id": "scan-277009364", "license": "MIT", "matched_queries": [ "topic:reverse-engineering is:public fork:false archived:false", "topic:il2cpp is:public fork:false archived:false" ], "metadata_observed_at": "2026-10-16T01:34:05.608218Z", "name": "Perfare/Zygisk-Il2CppDumper", "observed_at": "2026-10-16T01:35:37.483302Z", "pushed_at": "2024-08-09T18:58:59Z", "readme_api_url": "https://api.github.com/repos/Perfare/Zygisk-Il2CppDumper/readme?ref=master", "readme_bytes": 1046, "readme_git_sha": "3ec5551bca5ffb2314493e3c9524d4dd1e33890b", "readme_heading_count": 2, "readme_lines": 21, "readme_path": "README.md", "readme_sha256": "8c209f40fd9b01df6e4c8c12391f8c5a0df6b0d151294a7f8776a8055fc6d342", "readme_signals": [ "usage" ], "readme_url": "https://github.com/Perfare/Zygisk-Il2CppDumper/blob/master/README.md", "requirements": "See upstream requirements; 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Preserve this capture in the full data and omit the repeated discovery entry.", "reason_zh": "所扫描的 README 字节与保留条目 dslsdzc/rev-skills（scan-1337420546）完全相同；完整数据保留此采集记录，浏览结果省略重复发现条目。", "reviewed_at": "2026-10-20T09:57:30.269664+00:00" }, "scope_status": "excluded", "stars": 0, "targets": [ "app", "binary", "firmware", "protocol" ], "topics": [ "ai-agents", "claude-code", "ctf", "disassembly", "firmware-analysis", "malware-analysis", "mobile-security", "protocol-reverse-engineering", "reverse-engineering", "security-tools", "skills", "unpacking" ], "updated_at": "2026-10-16T18:19:15Z", "url": "https://github.com/Bastiaan5203/rev-skills", "verification": "automated-readme-scan" }, { "archived": false, "category": "game-analysis", "classification_engine_version": 4, "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_capture_policy": "bounded-upstream-v1", "description_en": "Unpacked GML source code for Undertale's Demo.", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "Unpacked GML source code for Undertale's Demo.", "description_zh": "解包得到的 Undertale 演示版 GML 源码。", "description_zh_status": "translated", "discovery_methods": [ "github-search" ], "full_name": "BioTomateDE/UndertaleDemoCode", "id": "scan-1282457730", "license": null, "matched_queries": [ "topic:unpacking is:public fork:false archived:false created:1970-01-01T00:00:00Z..2026-10-18T14:33:11Z" ], "metadata_observed_at": "2026-10-18T14:36:30.411990Z", "name": "BioTomateDE/UndertaleDemoCode", "observed_at": "2026-10-18T14:36:34.816422Z", "pushed_at": "2026-06-27T19:59:53Z", "readme_bytes": 2427, "readme_commit_sha": "760210022b8b2fb42d2bea85736ed97fd7821678", "readme_content_git_sha": "73ca1963de24cf407f31692041b4289dbe07c333", "readme_git_sha": "73ca1963de24cf407f31692041b4289dbe07c333", "readme_git_sha_matches_content": true, "readme_graphql_endpoint": "https://api.github.com/graphql", "readme_heading_count": 3, "readme_lines": 33, "readme_path": "README.md", "readme_sha256": "9f5df31caaf85803070b693c99e0f1e74a7d2aa4b0533ca40da8619a16fac9b1", "readme_signals": [ "examples" ], "readme_transport": "github-graphql-blob", "readme_url": "https://github.com/BioTomateDE/UndertaleDemoCode/blob/760210022b8b2fb42d2bea85736ed97fd7821678/README.md", "requirements": "See upstream requirements; not manually reviewed.", "scan_status": "readme-scanned", "stars": 0, "targets": [ "game" ], "topics": [ "decompilation", "decompiled", "game-assets", "gamemaker", "gm8", "gml", "tobyfox", "undertale", "unpacked", "unpacking" ], "updated_at": "2026-06-27T20:33:05Z", "url": "https://github.com/BioTomateDE/UndertaleDemoCode", "verification": "automated-readme-scan" }, { "archived": false, "category": "firmware", "classification_engine_version": 4, "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_capture_policy": "bounded-upstream-v1", "description_en": "A lightweight, custom Python GUI forensic utility featuring signature scanning, entropy randomness analysis, and file carving capabilities.", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "A lightweight, custom Python GUI forensic utility featuring signature scanning, entropy randomness analysis, and file carving capabilities.", "description_zh": "轻量级、自定义的 Python GUI 取证工具，提供特征扫描、熵与随机性分析及文件雕刻恢复功能。", "description_zh_status": "translated", "discovery_methods": [ "github-search" ], "full_name": "catiomic/forewalk", "id": "scan-1342871778", "license": null, "matched_queries": [ "topic:binwalk is:public fork:false archived:false created:1970-01-01T00:00:00Z..2026-10-18T14:33:11Z" ], "metadata_observed_at": "2026-10-18T14:36:00.249755Z", "name": "catiomic/forewalk", "observed_at": "2026-10-18T14:36:05.232910Z", "pushed_at": "2026-08-22T14:18:56Z", "readme_bytes": 2251, "readme_commit_sha": "69eb91030343c1b8e21e43180f7aa3550c4bde60", "readme_content_git_sha": "f17f20451bd804c3cc8d33fdfc74353543c57568", "readme_git_sha": "f17f20451bd804c3cc8d33fdfc74353543c57568", "readme_git_sha_matches_content": true, "readme_graphql_endpoint": "https://api.github.com/graphql", "readme_heading_count": 6, "readme_lines": 53, "readme_path": "README.md", "readme_sha256": "de0f7f76d5329d5a89d0f208a4d780dd979c86e6970234ca566f7f5a41c825fe", "readme_signals": [ "installation", "usage" ], "readme_transport": "github-graphql-blob", "readme_url": "https://github.com/catiomic/forewalk/blob/69eb91030343c1b8e21e43180f7aa3550c4bde60/README.md", "requirements": "See upstream requirements; not manually reviewed.", "scan_status": "readme-scanned", "stars": 0, "targets": [ "binary", "firmware" ], "topics": [ "binary-analysis", "binwalk", "customtkinter", "cybersecurity", "file-carving", "foremost", "forensics", "python", "windows" ], "updated_at": "2026-08-22T14:25:41Z", "url": "https://github.com/catiomic/forewalk", "verification": "automated-readme-scan" }, { "archived": false, "category": "firmware", "classification_engine_version": 4, "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_capture_policy": "bounded-upstream-v1", "description_en": "122 publishable, cross-platform AI skills for reverse engineering: malware analysis, software reverse engineering, embedded firmware, protocol reverse engineering, mobile applications, unpacking and deobfuscation, software cracking, vulnerability research, managed code, forensic intelligence and CTFs.", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "122 个逆向工程 AI 技能（可发布、跨平台）：恶意软件分析 / 软件逆向 / 固件嵌入式 / 协议逆向 / 移动应用 / 脱壳反混淆 / 软件破解 / 漏洞挖掘 / 托管代码 / 取证情报 / CTF。", "description_zh": "122 个逆向工程 AI 技能（可发布、跨平台）：恶意软件分析 / 软件逆向 / 固件嵌入式 / 协议逆向 / 移动应用 / 脱壳反混淆 / 软件破解 / 漏洞挖掘 / 托管代码 / 取证情报 / CTF。", "description_zh_status": "translated", "discovery_methods": [ "github-search" ], "full_name": "cReeves85779/rev-skills", "id": "scan-1424009703", "license": "NOASSERTION", "matched_queries": [ "topic:unpacking is:public fork:false archived:false created:1970-01-01T00:00:00Z..2026-10-18T14:33:11Z" ], "metadata_observed_at": "2026-10-18T14:36:30.411990Z", "name": "cReeves85779/rev-skills", "observed_at": "2026-10-18T14:36:34.816085Z", "pushed_at": "2026-10-18T05:10:14Z", "readme_bytes": 8195, "readme_commit_sha": "1650234ea5abe3ad23a741214e72ba573dd20dbb", "readme_content_git_sha": "d12fd4f7459237b1fc4854f8f922e0dda3c69f1f", "readme_git_sha": "d12fd4f7459237b1fc4854f8f922e0dda3c69f1f", "readme_git_sha_matches_content": true, "readme_graphql_endpoint": "https://api.github.com/graphql", "readme_heading_count": 6, "readme_lines": 126, "readme_path": "README.md", "readme_sha256": "fbce34be0aa0108b202f9485039352ca560f2aeee0e79a28a81e4c39fa7d737f", "readme_signals": [ "installation", "license-section" ], "readme_transport": "github-graphql-blob", "readme_url": "https://github.com/cReeves85779/rev-skills/blob/1650234ea5abe3ad23a741214e72ba573dd20dbb/README.md", "requirements": "See upstream requirements; not manually reviewed.", "scan_status": "readme-scanned", "scope_review": { "decision": "exclude", "evidence_url": "https://github.com/cReeves85779/rev-skills/blob/1650234ea5abe3ad23a741214e72ba573dd20dbb/README.md", "readme_sha256": "fbce34be0aa0108b202f9485039352ca560f2aeee0e79a28a81e4c39fa7d737f", "reason_code": "duplicate-readme-copy", "reason_en": "The scanned README bytes match the retained dslsdzc/rev-skills (scan-1337420546). Preserve this capture in the full data and omit the repeated discovery entry.", "reason_zh": "所扫描的 README 字节与保留条目 dslsdzc/rev-skills（scan-1337420546）完全相同；完整数据保留此采集记录，浏览结果省略重复发现条目。", "reviewed_at": "2026-10-20T09:57:30.269664+00:00" }, "scope_status": "excluded", "stars": 0, "targets": [ "app", "binary", "firmware", "protocol" ], "topics": [ "ai-agents", "claude-code", "ctf", "disassembly", "firmware-analysis", "malware-analysis", "mobile-security", "protocol-reverse-engineering", "reverse-engineering", "security-tools", "skills", "unpacking" ], "updated_at": "2026-10-18T05:10:18Z", "url": "https://github.com/cReeves85779/rev-skills", "verification": "automated-readme-scan" }, { "archived": false, "category": "android", "classification_engine_version": 4, "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_capture_policy": "bounded-upstream-v1", "description_en": "🚀 The easiest way to manage Frida on Android. Auto-setup, version matching, bypass scripts, wireless ADB.", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "🚀 The easiest way to manage Frida on Android. Auto-setup, version matching, bypass scripts, wireless ADB.", "description_zh": "🚀 管理 Android Frida 的最简方式：自动配置、版本匹配、绕过脚本及无线 ADB。", "description_zh_status": "translated", "discovery_methods": [ "github-search" ], "full_name": "CyberDemon73/F-for-Frida", "id": "scan-874819610", "license": "MIT", "matched_queries": [ "topic:frida-script is:public fork:false archived:false created:1970-01-01T00:00:00Z..2026-10-18T14:33:11Z" ], "metadata_observed_at": "2026-10-18T14:38:09.840416Z", "name": "CyberDemon73/F-for-Frida", "observed_at": "2026-10-18T14:38:14.454810Z", "pushed_at": "2026-02-06T11:24:16Z", "readme_bytes": 12248, "readme_commit_sha": "5594302763ef9bacd1141142bc7c164b72bbe864", "readme_content_git_sha": "44f03e32575130d58bb769f43b93bfd940ad6966", "readme_git_sha": "44f03e32575130d58bb769f43b93bfd940ad6966", "readme_git_sha_matches_content": true, "readme_graphql_endpoint": "https://api.github.com/graphql", "readme_heading_count": 6, "readme_lines": 516, "readme_path": "README.md", "readme_sha256": "9a734858786b066ab8aade7999e722dd3a34669af3b3b77e961c7d58b08e165d", "readme_signals": [ "installation", "usage", "requirements", "license-section" ], "readme_transport": "github-graphql-blob", "readme_url": "https://github.com/CyberDemon73/F-for-Frida/blob/5594302763ef9bacd1141142bc7c164b72bbe864/README.md", "requirements": "See upstream requirements; not manually reviewed.", "scan_status": "readme-scanned", "stars": 0, "targets": [ "app", "binary" ], "topics": [ "android-pentest", "android-pentesting", "automation", "frida", "frida-script", "frida-server", "friday-project" ], "updated_at": "2026-02-06T11:27:05Z", "url": "https://github.com/CyberDemon73/F-for-Frida", "verification": "automated-readme-scan" }, { "archived": false, "category": "native-analysis", "classification_engine_version": 4, "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_capture_policy": "bounded-upstream-v1", "description_en": "Python library for analyzing malicious binaries through emulation, pattern matching, and integration with reverse engineering tools.", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "Python library for analyzing malicious binaries through emulation, pattern matching, and integration with reverse engineering tools.", "description_zh": "用于分析恶意二进制文件的 Python 库，结合模拟、模式匹配及逆向工程工具集成。", "description_zh_status": "translated", "discovery_methods": [ "github-search" ], "full_name": "deja666/azazel", "id": "scan-1206193775", "license": "MIT", "matched_queries": [ "topic:ghidra-plugin is:public fork:false archived:false created:1970-01-01T00:00:00Z..2026-10-18T14:33:11Z" ], "metadata_observed_at": "2026-10-18T14:37:43.317646Z", "name": "deja666/azazel", "observed_at": "2026-10-18T14:37:48.586517Z", "pushed_at": "2026-04-10T06:53:02Z", "readme_bytes": 7895, "readme_commit_sha": "8f8f1ea601de901730451a7016e0ac424ff17d1b", "readme_content_git_sha": "02ae94d0ab47e82ceabc59a0900c854875759589", "readme_git_sha": "02ae94d0ab47e82ceabc59a0900c854875759589", "readme_git_sha_matches_content": true, "readme_graphql_endpoint": "https://api.github.com/graphql", "readme_heading_count": 6, "readme_lines": 302, "readme_path": "README.md", "readme_sha256": "e966fba5e8ea47cdace3669d67193122e8b446192b62a48ef98c095021b8e110", "readme_signals": [ "installation", "usage", "requirements", "examples", "license-section" ], "readme_transport": "github-graphql-blob", "readme_url": "https://github.com/deja666/azazel/blob/8f8f1ea601de901730451a7016e0ac424ff17d1b/README.md", "requirements": "See upstream requirements; not manually reviewed.", "scan_status": "readme-scanned", "stars": 0, "targets": [ "binary" ], "topics": [ "binary", "ghidra-plugin", "library", "malware-analysis", "pattern-matching", "python", "reverse-engineering", "rich", "yara-rules" ], "updated_at": "2026-04-10T06:56:01Z", "url": "https://github.com/deja666/azazel", "verification": "automated-readme-scan" }, { "archived": false, "category": "firmware", "classification_engine_version": 4, "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_capture_policy": "bounded-upstream-v1", "description_en": "Reverse engineering the firmware of my Jensen CAR710W head unit to locate and modifiy UI assets such as boot animations and backgrounds.", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "Reverse engineering the firmware of my Jensen CAR710W head unit to locate and modifiy UI assets such as boot animations and backgrounds.", "description_zh": "对作者的 Jensen CAR710W 车机固件进行逆向工程，以定位和修改启动动画、背景等界面资源。", "description_zh_status": "translated", "discovery_methods": [ "github-search" ], "full_name": "DerrickDW/car710w-research", "id": "scan-1178344702", "license": null, "matched_queries": [ "topic:binwalk is:public fork:false archived:false created:1970-01-01T00:00:00Z..2026-10-18T14:33:11Z" ], "metadata_observed_at": "2026-10-18T14:36:00.249755Z", "name": "DerrickDW/car710w-research", "observed_at": "2026-10-18T14:36:05.233751Z", "pushed_at": "2026-03-11T00:52:03Z", "readme_bytes": 5572, "readme_commit_sha": "06a16de57d4bd3aee58a8ad221784cd8be02e0fc", "readme_content_git_sha": "52d0de3c1ae83534dbb3e10ab1f27fe64912057b", "readme_git_sha": "52d0de3c1ae83534dbb3e10ab1f27fe64912057b", "readme_git_sha_matches_content": true, "readme_graphql_endpoint": "https://api.github.com/graphql", "readme_heading_count": 6, "readme_lines": 307, "readme_path": "README.md", "readme_sha256": "b889c8a4d395e24d5dff857644e305b4999344ea748f3635bd02298b27e52722", "readme_signals": [], "readme_transport": "github-graphql-blob", "readme_url": "https://github.com/DerrickDW/car710w-research/blob/06a16de57d4bd3aee58a8ad221784cd8be02e0fc/README.md", "requirements": "See upstream requirements; not manually reviewed.", "scan_status": "readme-scanned", "stars": 0, "targets": [ "firmware" ], "topics": [ "binwalk", "busybox", "car-audio", "car-radio", "decode-im", "ffmpeg", "ffprobe", "headunit", "linux", "qt", "reverse-engineering", "squashfs" ], "updated_at": "2026-03-11T00:54:12Z", "url": "https://github.com/DerrickDW/car710w-research", "verification": "automated-readme-scan" }, { "archived": false, "category": "web-analysis", "classification_engine_version": 4, "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_capture_policy": "bounded-upstream-v1", "description_en": "Assembler for the 6502. 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Originally written for my online assembler and chiplab at https://www.emulationonline.com/systems/chiplab/6502/assembler/", "description_zh": "6502 汇编器，最初为作者的在线汇编器和 chiplab 编写：https://www.emulationonline.com/systems/chiplab/6502/assembler/", "description_zh_status": "translated", "discovery_methods": [ "github-search" ], "full_name": "EmulationOnline/6502_asm", "id": "scan-862726332", "license": "BSD-3-Clause", "matched_queries": [ "topic:wasm topic:reverse-engineering is:public fork:false archived:false created:1970-01-01T00:00:00Z..2026-10-18T14:33:11Z" ], "metadata_observed_at": "2026-10-18T14:38:20.561025Z", "name": "EmulationOnline/6502_asm", "observed_at": "2026-10-18T14:38:25.248923Z", "pushed_at": "2024-09-25T06:58:42Z", "readme_bytes": 1158, "readme_commit_sha": "4c9e949979f6a953dfbb914c9f07245806393074", "readme_content_git_sha": "5a83570cc56a0af438a5d550100f066af2a29b83", "readme_git_sha": "5a83570cc56a0af438a5d550100f066af2a29b83", "readme_git_sha_matches_content": true, "readme_graphql_endpoint": "https://api.github.com/graphql", "readme_heading_count": 5, "readme_lines": 35, "readme_path": "README.md", "readme_sha256": "56ffdd4b43a97006d03d67d696cf3241a2bb9700fb7f29c4b7b5043f4c97bdb5", "readme_signals": [], "readme_transport": "github-graphql-blob", "readme_url": "https://github.com/EmulationOnline/6502_asm/blob/4c9e949979f6a953dfbb914c9f07245806393074/README.md", "requirements": "See upstream requirements; not manually reviewed.", "scan_status": "readme-scanned", "stars": 0, "targets": [ "binary", "website" ], "topics": [ "6502", "assembly", "retrocomputing", "reverse-engineering", "rust", "wasm" ], "updated_at": "2025-08-24T13:15:16Z", "url": "https://github.com/EmulationOnline/6502_asm", "verification": "automated-readme-scan" }, { "archived": false, "category": "firmware", "classification_engine_version": 4, "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_capture_policy": "bounded-upstream-v1", "description_en": "122 publishable, cross-platform AI skills for reverse engineering: malware analysis, software reverse engineering, embedded firmware, protocol reverse engineering, mobile applications, unpacking and deobfuscation, software cracking, vulnerability research, managed code, forensic intelligence and CTFs.", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "122 个逆向工程 AI 技能（可发布、跨平台）：恶意软件分析 / 软件逆向 / 固件嵌入式 / 协议逆向 / 移动应用 / 脱壳反混淆 / 软件破解 / 漏洞挖掘 / 托管代码 / 取证情报 / CTF。", "description_zh": "122 个逆向工程 AI 技能（可发布、跨平台）：恶意软件分析 / 软件逆向 / 固件嵌入式 / 协议逆向 / 移动应用 / 脱壳反混淆 / 软件破解 / 漏洞挖掘 / 托管代码 / 取证情报 / CTF。", "description_zh_status": "translated", "discovery_methods": [ "github-search" ], "full_name": "Evans0081/rev-skills", "id": "scan-1424079077", "license": "NOASSERTION", "matched_queries": [ "topic:unpacking is:public fork:false archived:false created:1970-01-01T00:00:00Z..2026-10-18T14:33:11Z" ], "metadata_observed_at": "2026-10-18T14:36:25.904582Z", "name": "Evans0081/rev-skills", "observed_at": "2026-10-18T14:36:30.411395Z", "pushed_at": "2026-10-18T06:53:03Z", "readme_bytes": 8195, "readme_commit_sha": "348912fe2d6a784282162047100da7b16f532849", "readme_content_git_sha": "d12fd4f7459237b1fc4854f8f922e0dda3c69f1f", "readme_git_sha": "d12fd4f7459237b1fc4854f8f922e0dda3c69f1f", "readme_git_sha_matches_content": true, "readme_graphql_endpoint": "https://api.github.com/graphql", "readme_heading_count": 6, "readme_lines": 126, "readme_path": "README.md", "readme_sha256": "fbce34be0aa0108b202f9485039352ca560f2aeee0e79a28a81e4c39fa7d737f", "readme_signals": [ "installation", "license-section" ], "readme_transport": "github-graphql-blob", "readme_url": "https://github.com/Evans0081/rev-skills/blob/348912fe2d6a784282162047100da7b16f532849/README.md", "requirements": "See upstream requirements; not manually reviewed.", "scan_status": "readme-scanned", "scope_review": { "decision": "exclude", "evidence_url": "https://github.com/Evans0081/rev-skills/blob/348912fe2d6a784282162047100da7b16f532849/README.md", "readme_sha256": "fbce34be0aa0108b202f9485039352ca560f2aeee0e79a28a81e4c39fa7d737f", "reason_code": "duplicate-readme-copy", "reason_en": "The scanned README bytes match the retained dslsdzc/rev-skills (scan-1337420546). Preserve this capture in the full data and omit the repeated discovery entry.", "reason_zh": "所扫描的 README 字节与保留条目 dslsdzc/rev-skills（scan-1337420546）完全相同；完整数据保留此采集记录，浏览结果省略重复发现条目。", "reviewed_at": "2026-10-20T09:57:30.269664+00:00" }, "scope_status": "excluded", "stars": 0, "targets": [ "app", "binary", "firmware", "protocol" ], "topics": [ "ai-agents", "claude-code", "ctf", "disassembly", "firmware-analysis", "malware-analysis", "mobile-security", "protocol-reverse-engineering", "reverse-engineering", "security-tools", "skills", "unpacking" ], "updated_at": "2026-10-18T06:53:43Z", "url": "https://github.com/Evans0081/rev-skills", "verification": "automated-readme-scan" }, { "archived": false, "category": "dynamic-analysis", "classification_engine_version": 4, "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_capture_policy": "bounded-upstream-v1", "description_en": "AI-agent skill system for mobile reverse engineering. Features Frida hooks, one-click unpacking, detection bypasses, in-memory DEX dumping, and symbol/structure recovery through Ghidra MCP.", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "AI Agent 驱动的移动端逆向技能集：Frida hook、一键脱壳、反检测绕过、内存 DEX dump、Ghidra MCP 符号/结构恢复。AI-agent skill system for mobile reverse engineering.", "description_zh": "AI 智能体驱动的移动端逆向技能集：Frida hook、一键脱壳、反检测绕过、内存 DEX 转储，以及通过 Ghidra MCP 恢复符号和结构。", "description_zh_status": "translated", "discovery_methods": [ "github-search" ], "full_name": "fkrylov351/MobileRE-Skill", "id": "scan-1423746310", "license": "NOASSERTION", "matched_queries": [ "topic:jadx is:public fork:false archived:false created:1970-01-01T00:00:00Z..2026-10-18T14:33:11Z", "topic:unpacking is:public fork:false archived:false created:1970-01-01T00:00:00Z..2026-10-18T14:33:11Z" ], "metadata_observed_at": "2026-10-18T14:35:55.266775Z", "name": "fkrylov351/MobileRE-Skill", "observed_at": "2026-10-18T14:35:59.948972Z", "pushed_at": "2026-10-17T21:12:36Z", "readme_bytes": 15221, "readme_commit_sha": "3db376f0480034dea089d429d7998d1b5d963e40", "readme_content_git_sha": "e85c63069f3938b300197fde507649982fa2fd5d", "readme_git_sha": "e85c63069f3938b300197fde507649982fa2fd5d", "readme_git_sha_matches_content": true, "readme_graphql_endpoint": "https://api.github.com/graphql", "readme_heading_count": 6, "readme_lines": 247, "readme_path": "README.md", "readme_sha256": "6d7ae893970a5fdee0729a75ff5c58c862b7b17e14e34e6f449a90bc778c5078", "readme_signals": [ "usage", "requirements", "license-section" ], "readme_transport": "github-graphql-blob", "readme_url": "https://github.com/fkrylov351/MobileRE-Skill/blob/3db376f0480034dea089d429d7998d1b5d963e40/README.md", "requirements": "See upstream requirements; not manually reviewed.", "scan_status": "readme-scanned", "scope_review": { "decision": "exclude", "evidence_url": "https://github.com/fkrylov351/MobileRE-Skill/blob/3db376f0480034dea089d429d7998d1b5d963e40/README.md", "readme_sha256": "6d7ae893970a5fdee0729a75ff5c58c862b7b17e14e34e6f449a90bc778c5078", "reason_code": "duplicate-readme-copy", "reason_en": "The scanned README bytes match the retained index-login/MobileRE-Skill (scan-1255951226). Preserve this capture in the full data and omit the repeated discovery entry.", "reason_zh": "所扫描的 README 字节与保留条目 index-login/MobileRE-Skill（scan-1255951226）完全相同；完整数据保留此采集记录，浏览结果省略重复发现条目。", "reviewed_at": "2026-10-20T09:57:30.269664+00:00" }, "scope_status": "excluded", "stars": 0, "targets": [ "app", "binary" ], "topics": [ "agent-skills", "ai-agent", "android-security", "anti-debug", "dex", "dynamic-instrumentation", "frida", "frida-scripts", "frida-skills", "ghidra", "ios-security", "jadx", "llm", "mcp", "mcp-server", "mobile-security", "reverse-engineering", "unicorn-engine", "unpacking" ], "updated_at": "2026-10-17T21:13:15Z", "url": "https://github.com/fkrylov351/MobileRE-Skill", "verification": "automated-readme-scan" }, { "archived": false, "category": "firmware", "classification_engine_version": 4, "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_capture_policy": "bounded-upstream-v1", "description_en": "122 publishable, cross-platform AI skills for reverse engineering: malware analysis, software reverse engineering, embedded firmware, protocol reverse engineering, mobile applications, unpacking and deobfuscation, software cracking, vulnerability research, managed code, forensic intelligence and CTFs.", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "122 个逆向工程 AI 技能（可发布、跨平台）：恶意软件分析 / 软件逆向 / 固件嵌入式 / 协议逆向 / 移动应用 / 脱壳反混淆 / 软件破解 / 漏洞挖掘 / 托管代码 / 取证情报 / CTF。", "description_zh": "122 个逆向工程 AI 技能（可发布、跨平台）：恶意软件分析 / 软件逆向 / 固件嵌入式 / 协议逆向 / 移动应用 / 脱壳反混淆 / 软件破解 / 漏洞挖掘 / 托管代码 / 取证情报 / CTF。", "description_zh_status": "translated", "discovery_methods": [ "github-search" ], "full_name": "ginoue26398/rev-skills", "id": "scan-1422525301", "license": "NOASSERTION", "matched_queries": [ "topic:unpacking is:public fork:false archived:false created:1970-01-01T00:00:00Z..2026-10-18T14:33:11Z" ], "metadata_observed_at": "2026-10-18T14:36:20.246217Z", "name": "ginoue26398/rev-skills", "observed_at": "2026-10-18T14:36:25.633084Z", "pushed_at": "2026-10-16T18:28:25Z", "readme_bytes": 8195, "readme_commit_sha": "a1ee1319028f4a77ee7df1362051a0c54961a168", "readme_content_git_sha": "d12fd4f7459237b1fc4854f8f922e0dda3c69f1f", "readme_git_sha": "d12fd4f7459237b1fc4854f8f922e0dda3c69f1f", "readme_git_sha_matches_content": true, "readme_graphql_endpoint": "https://api.github.com/graphql", "readme_heading_count": 6, "readme_lines": 126, "readme_path": "README.md", "readme_sha256": "fbce34be0aa0108b202f9485039352ca560f2aeee0e79a28a81e4c39fa7d737f", "readme_signals": [ "installation", "license-section" ], "readme_transport": "github-graphql-blob", "readme_url": "https://github.com/ginoue26398/rev-skills/blob/a1ee1319028f4a77ee7df1362051a0c54961a168/README.md", "requirements": "See upstream requirements; not manually reviewed.", "scan_status": "readme-scanned", "scope_review": { "decision": "exclude", "evidence_url": "https://github.com/ginoue26398/rev-skills/blob/a1ee1319028f4a77ee7df1362051a0c54961a168/README.md", "readme_sha256": "fbce34be0aa0108b202f9485039352ca560f2aeee0e79a28a81e4c39fa7d737f", "reason_code": "duplicate-readme-copy", "reason_en": "The scanned README bytes match the retained dslsdzc/rev-skills (scan-1337420546). Preserve this capture in the full data and omit the repeated discovery entry.", "reason_zh": "所扫描的 README 字节与保留条目 dslsdzc/rev-skills（scan-1337420546）完全相同；完整数据保留此采集记录，浏览结果省略重复发现条目。", "reviewed_at": "2026-10-20T09:57:30.269664+00:00" }, "scope_status": "excluded", "stars": 0, "targets": [ "app", "binary", "firmware", "protocol" ], "topics": [ "ai-agents", "claude-code", "ctf", "disassembly", "firmware-analysis", "malware-analysis", "mobile-security", "protocol-reverse-engineering", "reverse-engineering", "security-tools", "skills", "unpacking" ], "updated_at": "2026-10-16T18:28:31Z", "url": "https://github.com/ginoue26398/rev-skills", "verification": "automated-readme-scan" }, { "archived": false, "category": "web-analysis", "classification_engine_version": 4, "classification_origin": "automatic-metadata", "classification_reference_ids": [], "description_capture_policy": "bounded-upstream-v1", "description_en": "Opens Olympus and Grundig dictation recordings on any computer, a format locked since 1994. Replaced a paid licence and a chain of Windows machines; the fix is going upstream into FFmpeg.", "description_language": "bilingual", "description_localization": "ai-assisted-translation", "description_origin": "github-repository-description", "description_original": "Opens Olympus and Grundig dictation recordings on any computer, a format locked since 1994. 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