[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFN034NJZVXBRBTMVRPHRR":3,"skill-same:01M6AFN034NJZVXBRBTMVRPHRR":119},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":64,"commitSha":65,"skillDirectory":66,"forksCount":67,"repositoryHtmlUrl":68,"repositoryHomepageUrl":69,"ownerHtmlUrl":70,"ownerBlogUrl":71,"ownerDescription":72,"updatedAt":73,"install":74,"resources":87},"01M6AFN034NJZVXBRBTMVRPHRR","performing-automated-malware-analysis-with-cape",null,"Deploy and operate the CAPEv2 malware sandbox (a Cuckoo derivative) to run samples in a monitored Windows guest VM, capturing behavioral signatures, dropped files, PCAP network traffic, and family-specific configuration extraction (e.g. Emotet, TrickBot, Cobalt Strike) via cape-parsers. Use when a suspicious file or payload needs automated dynamic analysis, anti-evasion debugger tricks, or config\u002Fpayload extraction.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",35990,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-automated-malware-analysis-with-cape\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"performing automated malware analysis with cape","analysis","malware analysis","malware","malware-analysis","payload-extraction","automated-analysis","cape","debugger","network","behavioral-analysis","Cuckoo","suspicious","file","vm","run","windows","config","cybersecurity","sandbox","2026-11-10 10:12:30",false,"---\nname: performing-automated-malware-analysis-with-cape\ndescription: Deploy and operate the CAPEv2 malware sandbox (a Cuckoo derivative) to run samples in a\n  monitored Windows guest VM, capturing behavioral signatures, dropped files, PCAP network traffic,\n  and family-specific configuration extraction (e.g. Emotet, TrickBot, Cobalt Strike) via\n  cape-parsers. Use when a suspicious file or payload needs automated dynamic analysis, anti-evasion\n  debugger tricks, or config\u002Fpayload extraction.\ndomain: cybersecurity\nsubdomain: malware-analysis\ntags:\n- cape\n- sandbox\n- automated-analysis\n- malware-analysis\n- behavioral-analysis\n- payload-extraction\n- cuckoo\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- DE.AE-02\n- RS.AN-03\n- ID.RA-01\n- DE.CM-01\nmitre_attack:\n- T1027\n- T1055\n- T1140\n- T1497\n- T1070\n---\n# Performing Automated Malware Analysis with CAPE\n\n## Overview\n\nCAPE (Config And Payload Extraction) is an open-source malware sandbox derived from Cuckoo that automates behavioral analysis, payload dumping, and configuration extraction. CAPEv2 features API hooking for behavioral instrumentation, captures files created\u002Fmodified\u002Fdeleted during execution, records network traffic in PCAP format, and includes 70+ custom configuration extractors (cape-parsers) for families like Emotet, TrickBot, Cobalt Strike, AsyncRAT, and Rhadamanthys. The signature system includes 1000+ behavioral signatures detecting evasion techniques, persistence, credential theft, and ransomware behavior. CAPE's debugger enables dynamic anti-evasion bypasses combining debugger actions within YARA signatures. Recommended deployment: Ubuntu LTS host with Windows 10 21H2 guest VM.\n\n\n## When to Use\n\n- When conducting security assessments that involve performing automated malware analysis with cape\n- When following incident response procedures for related security events\n- When performing scheduled security testing or auditing activities\n- When validating security controls through hands-on testing\n\n## Prerequisites\n\n- Ubuntu 22.04 LTS server (8+ CPU cores, 32GB+ RAM, 500GB+ SSD)\n- KVM\u002FQEMU virtualization support\n- Windows 10 21H2 guest image\n- Python 3.9+ with CAPEv2 dependencies\n- Network configuration for isolated analysis network\n\n## Workflow\n\n### Step 1: Submit and Analyze Samples via API\n\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"CAPE sandbox API client for automated malware submission and analysis.\"\"\"\nimport requests\nimport json\nimport time\nimport sys\nfrom pathlib import Path\n\n\nclass CAPEClient:\n    def __init__(self, base_url=\"http:\u002F\u002Flocalhost:8000\", api_token=None):\n        self.base_url = base_url.rstrip(\"\u002F\")\n        self.headers = {}\n        if api_token:\n            self.headers[\"Authorization\"] = f\"Token {api_token}\"\n\n    def submit_file(self, filepath, options=None):\n        \"\"\"Submit a file for analysis.\"\"\"\n        url = f\"{self.base_url}\u002Fapiv2\u002Ftasks\u002Fcreate\u002Ffile\u002F\"\n        files = {\"file\": open(filepath, \"rb\")}\n        data = options or {}\n        data.setdefault(\"timeout\", 120)\n        data.setdefault(\"enforce_timeout\", False)\n\n        resp = requests.post(url, files=files, data=data, headers=self.headers)\n        resp.raise_for_status()\n        result = resp.json()\n        task_id = result.get(\"data\", {}).get(\"task_ids\", [None])[0]\n        print(f\"[+] Submitted {filepath} -> Task ID: {task_id}\")\n        return task_id\n\n    def get_status(self, task_id):\n        \"\"\"Check task analysis status.\"\"\"\n        url = f\"{self.base_url}\u002Fapiv2\u002Ftasks\u002Fstatus\u002F{task_id}\u002F\"\n        resp = requests.get(url, headers=self.headers)\n        return resp.json().get(\"data\", \"unknown\")\n\n    def wait_for_completion(self, task_id, poll_interval=15, max_wait=600):\n        \"\"\"Wait for analysis to complete.\"\"\"\n        elapsed = 0\n        while elapsed \u003C max_wait:\n            status = self.get_status(task_id)\n            if status == \"reported\":\n                print(f\"[+] Task {task_id} completed\")\n                return True\n            time.sleep(poll_interval)\n            elapsed += poll_interval\n            print(f\"  Waiting... ({elapsed}s, status: {status})\")\n        return False\n\n    def get_report(self, task_id):\n        \"\"\"Retrieve full analysis report.\"\"\"\n        url = f\"{self.base_url}\u002Fapiv2\u002Ftasks\u002Fget\u002Freport\u002F{task_id}\u002F\"\n        resp = requests.get(url, headers=self.headers)\n        return resp.json()\n\n    def get_config(self, task_id):\n        \"\"\"Get extracted malware configuration.\"\"\"\n        report = self.get_report(task_id)\n        configs = report.get(\"CAPE\", {}).get(\"configs\", [])\n        return configs\n\n    def get_dropped_files(self, task_id):\n        \"\"\"List files dropped during analysis.\"\"\"\n        report = self.get_report(task_id)\n        return report.get(\"dropped\", [])\n\n    def get_network_iocs(self, task_id):\n        \"\"\"Extract network IOCs from analysis.\"\"\"\n        report = self.get_report(task_id)\n        network = report.get(\"network\", {})\n        iocs = {\n            \"dns\": [d.get(\"request\") for d in network.get(\"dns\", [])],\n            \"http\": [h.get(\"uri\") for h in network.get(\"http\", [])],\n            \"tcp\": [f\"{h.get('dst')}:{h.get('dport')}\"\n                    for h in network.get(\"tcp\", [])],\n        }\n        return iocs\n\n    def analyze_sample(self, filepath):\n        \"\"\"Full automated analysis pipeline.\"\"\"\n        task_id = self.submit_file(filepath)\n        if not task_id:\n            return None\n\n        if self.wait_for_completion(task_id):\n            report = {\n                \"task_id\": task_id,\n                \"config\": self.get_config(task_id),\n                \"network_iocs\": self.get_network_iocs(task_id),\n                \"dropped_files\": len(self.get_dropped_files(task_id)),\n            }\n            return report\n        return None\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) \u003C 2:\n        print(f\"Usage: {sys.argv[0]} \u003Cmalware_sample> [cape_url]\")\n        sys.exit(1)\n\n    url = sys.argv[2] if len(sys.argv) > 2 else \"http:\u002F\u002Flocalhost:8000\"\n    client = CAPEClient(url)\n    result = client.analyze_sample(sys.argv[1])\n    if result:\n        print(json.dumps(result, indent=2))\n```\n\n## Validation Criteria\n\n- Samples submitted and analyzed within configured timeout\n- Behavioral signatures triggered for known malware families\n- Malware configurations extracted by cape-parsers\n- Network traffic captured and IOCs extracted\n- Dropped files and payloads collected for further analysis\n- Anti-evasion bypasses effective against sandbox-aware malware\n\n## References\n\n- [CAPEv2 GitHub](https:\u002F\u002Fgithub.com\u002Fkevoreilly\u002FCAPEv2)\n- [CAPE Sandbox Documentation](https:\u002F\u002Fcapev2.readthedocs.io\u002F)\n- [Automating Malware Analysis with CAPE](https:\u002F\u002Fendsec.au\u002Fblog\u002Fbuilding-an-automated-malware-sandbox-using-cape\u002F)\n- [Installing CAPEv2 on Ubuntu](https:\u002F\u002Fmedium.com\u002F@rizqisetyokus\u002Fbuilding-capev2-automated-malware-analysis-sandbox-part-1-da2a6ff69cdb)\n","# Performing Automated Malware Analysis with CAPE\n\n## Overview\n\nCAPE (Config And Payload Extraction) is an open-source malware sandbox derived from Cuckoo that automates behavioral analysis, payload dumping, and configuration extraction. CAPEv2 features API hooking for behavioral instrumentation, captures files created\u002Fmodified\u002Fdeleted during execution, records network traffic in PCAP format, and includes 70+ custom configuration extractors (cape-parsers) for families like Emotet, TrickBot, Cobalt Strike, AsyncRAT, and Rhadamanthys. The signature system includes 1000+ behavioral signatures detecting evasion techniques, persistence, credential theft, and ransomware behavior. CAPE's debugger enables dynamic anti-evasion bypasses combining debugger actions within YARA signatures. Recommended deployment: Ubuntu LTS host with Windows 10 21H2 guest VM.\n\n\n## When to Use\n\n- When conducting security assessments that involve performing automated malware analysis with cape\n- When following incident response procedures for related security events\n- When performing scheduled security testing or auditing activities\n- When validating security controls through hands-on testing\n\n## Prerequisites\n\n- Ubuntu 22.04 LTS server (8+ CPU cores, 32GB+ RAM, 500GB+ SSD)\n- KVM\u002FQEMU virtualization support\n- Windows 10 21H2 guest image\n- Python 3.9+ with CAPEv2 dependencies\n- Network configuration for isolated analysis network\n\n## Workflow\n\n### Step 1: Submit and Analyze Samples via API\n\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"CAPE sandbox API client for automated malware submission and analysis.\"\"\"\nimport requests\nimport json\nimport time\nimport sys\nfrom pathlib import Path\n\n\nclass CAPEClient:\n    def __init__(self, base_url=\"http:\u002F\u002Flocalhost:8000\", api_token=None):\n        self.base_url = base_url.rstrip(\"\u002F\")\n        self.headers = {}\n        if api_token:\n            self.headers[\"Authorization\"] = f\"Token {api_token}\"\n\n    def submit_file(self, filepath, options=None):\n        \"\"\"Submit a file for analysis.\"\"\"\n        url = f\"{self.base_url}\u002Fapiv2\u002Ftasks\u002Fcreate\u002Ffile\u002F\"\n        files = {\"file\": open(filepath, \"rb\")}\n        data = options or {}\n        data.setdefault(\"timeout\", 120)\n        data.setdefault(\"enforce_timeout\", False)\n\n        resp = requests.post(url, files=files, data=data, headers=self.headers)\n        resp.raise_for_status()\n        result = resp.json()\n        task_id = result.get(\"data\", {}).get(\"task_ids\", [None])[0]\n        print(f\"[+] Submitted {filepath} -> Task ID: {task_id}\")\n        return task_id\n\n    def get_status(self, task_id):\n        \"\"\"Check task analysis status.\"\"\"\n        url = f\"{self.base_url}\u002Fapiv2\u002Ftasks\u002Fstatus\u002F{task_id}\u002F\"\n        resp = requests.get(url, headers=self.headers)\n        return resp.json().get(\"data\", \"unknown\")\n\n    def wait_for_completion(self, task_id, poll_interval=15, max_wait=600):\n        \"\"\"Wait for analysis to complete.\"\"\"\n        elapsed = 0\n        while elapsed \u003C max_wait:\n            status = self.get_status(task_id)\n            if status == \"reported\":\n                print(f\"[+] Task {task_id} completed\")\n                return True\n            time.sleep(poll_interval)\n            elapsed += poll_interval\n            print(f\"  Waiting... ({elapsed}s, status: {status})\")\n        return False\n\n    def get_report(self, task_id):\n        \"\"\"Retrieve full analysis report.\"\"\"\n        url = f\"{self.base_url}\u002Fapiv2\u002Ftasks\u002Fget\u002Freport\u002F{task_id}\u002F\"\n        resp = requests.get(url, headers=self.headers)\n        return resp.json()\n\n    def get_config(self, task_id):\n        \"\"\"Get extracted malware configuration.\"\"\"\n        report = self.get_report(task_id)\n        configs = report.get(\"CAPE\", {}).get(\"configs\", [])\n        return configs\n\n    def get_dropped_files(self, task_id):\n        \"\"\"List files dropped during analysis.\"\"\"\n        report = self.get_report(task_id)\n        return report.get(\"dropped\", [])\n\n    def get_network_iocs(self, task_id):\n        \"\"\"Extract network IOCs from analysis.\"\"\"\n        report = self.get_report(task_id)\n        network = report.get(\"network\", {})\n        iocs = {\n            \"dns\": [d.get(\"request\") for d in network.get(\"dns\", [])],\n            \"http\": [h.get(\"uri\") for h in network.get(\"http\", [])],\n            \"tcp\": [f\"{h.get('dst')}:{h.get('dport')}\"\n                    for h in network.get(\"tcp\", [])],\n        }\n        return iocs\n\n    def analyze_sample(self, filepath):\n        \"\"\"Full automated analysis pipeline.\"\"\"\n        task_id = self.submit_file(filepath)\n        if not task_id:\n            return None\n\n        if self.wait_for_completion(task_id):\n            report = {\n                \"task_id\": task_id,\n                \"config\": self.get_config(task_id),\n                \"network_iocs\": self.get_network_iocs(task_id),\n                \"dropped_files\": len(self.get_dropped_files(task_id)),\n            }\n            return report\n        return None\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) \u003C 2:\n        print(f\"Usage: {sys.argv[0]} \u003Cmalware_sample> [cape_url]\")\n        sys.exit(1)\n\n    url = sys.argv[2] if len(sys.argv) > 2 else \"http:\u002F\u002Flocalhost:8000\"\n    client = CAPEClient(url)\n    result = client.analyze_sample(sys.argv[1])\n    if result:\n        print(json.dumps(result, indent=2))\n```\n\n## Validation Criteria\n\n- Samples submitted and analyzed within configured timeout\n- Behavioral signatures triggered for known malware families\n- Malware configurations extracted by cape-parsers\n- Network traffic captured and IOCs extracted\n- Dropped files and payloads collected for further analysis\n- Anti-evasion bypasses effective against sandbox-aware malware\n\n## References\n\n- [CAPEv2 GitHub](https:\u002F\u002Fgithub.com\u002Fkevoreilly\u002FCAPEv2)\n- [CAPE Sandbox Documentation](https:\u002F\u002Fcapev2.readthedocs.io\u002F)\n- [Automating Malware Analysis with CAPE](https:\u002F\u002Fendsec.au\u002Fblog\u002Fbuilding-an-automated-malware-sandbox-using-cape\u002F)\n- [Installing CAPEv2 on Ubuntu](https:\u002F\u002Fmedium.com\u002F@rizqisetyokus\u002Fbuilding-capev2-automated-malware-analysis-sandbox-part-1-da2a6ff69cdb)\n",{"name":5,"tags":49,"author":51,"domain":42,"license":15,"version":52,"nist_csf":53,"subdomain":28,"description":7,"mitre_attack":58},[31,43,30,28,34,29,50],"cuckoo","mahipal","1.0",[54,55,56,57],"DE.AE-02","RS.AN-03","ID.RA-01","DE.CM-01",[59,60,61,62,63],"T1027","T1055","T1140","T1497","T1070","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fperforming-automated-malware-analysis-with-cape",4395,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","https:\u002F\u002Fmahipal.engineer\u002FAnthropic-Cybersecurity-Skills\u002F","https:\u002F\u002Fgithub.com\u002Fmukul975","Mahipal.engineer","🔐 Cybersecurity | 💻 Dev | 📷 Street Photographer\r\n🎓 MSc | 📚 Research & AI Security","2026-08-31 12:32:44",{"source":8,"skillName":5,"commands":75,"prompt":85,"zipAvailable":86},[76,79,82],{"tool":77,"command":78},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-automated-malware-analysis-with-cape",{"tool":80,"command":81},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-automated-malware-analysis-with-cape",{"tool":83,"command":84},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-automated-malware-analysis-with-cape","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-automated-malware-analysis-with-cape\u002FSKILL.md",true,[88,94,99,105,109,113],{"path":89,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":92,"sourceHtmlUrl":93},"assets\u002Ftemplate.md","ASSET","资产文件",428,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-automated-malware-analysis-with-cape\u002Fassets\u002Ftemplate.md",{"path":95,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":97,"sourceHtmlUrl":98},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-automated-malware-analysis-with-cape\u002FLICENSE",{"path":100,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fapi-reference.md","REFERENCE","参考文档",5798,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-automated-malware-analysis-with-cape\u002Freferences\u002Fapi-reference.md",{"path":106,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fstandards.md",316,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-automated-malware-analysis-with-cape\u002Freferences\u002Fstandards.md",{"path":110,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002Fworkflows.md",475,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-automated-malware-analysis-with-cape\u002Freferences\u002Fworkflows.md",{"path":114,"resourceType":115,"resourceTypeDesc":116,"sizeBytes":117,"sourceHtmlUrl":118},"scripts\u002Fagent.py","SCRIPT","脚本",10790,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-automated-malware-analysis-with-cape\u002Fscripts\u002Fagent.py",[120,147,171,199,224,251],{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":127,"publishedAt":44},"01M6AGDM3HJXTKXX15ZZ0ZVCJ3","verifying-build-provenance-with-slsa-sigstore","Verifies artifact signatures and SLSA provenance using Sigstore's cosign (verify, verify-attestation, verify-blob-attestation) and slsa-verifier (verify-artifact), enforcing keyless OIDC builder identity and source repo against SLSA Build levels. Use in CI\u002FCD before deploying artifacts, when consuming third-party attestations, establishing a SLSA Build L3 pipeline, or confirming provenance during incident response or admission control.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fverifying-build-provenance-with-slsa-sigstore\u002FSKILL.md",[126],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[128,129,130,131,132,42,133,134,135,136,137,138,139,140,141,142,143,144,145,146],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","security","incident response","identity","pr","code","artifacts","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":154,"publishedAt":44},"01M6AGDGBPWYVZ2F11GFKJ4HZ9","validating-tpm-measured-boot-attestation","Verifies TPM 2.0 measured-boot integrity and remote attestation with tpm2-tools -- reading PCRs (tpm2_pcrread), replaying the boot event log, generating and checking signed quotes (tpm2_quote\u002Ftpm2_checkquote), and sealing secrets to a PCR policy. Use to confirm a system booted trusted firmware\u002Fkernel for Zero Trust device posture, detect boot-chain tampering via PCR drift, or build a golden-value baseline for fleet attestation.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-tpm-measured-boot-attestation\u002FSKILL.md",[153],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[155,143,156,157,132,158,42,135,159,160,161,162,163,164,165,166,167,168,169,170],"boot","TPM","validating tpm measured boot attestation","policy","verification","ZeRO","firmware","Tools","measured-boot","PCR","integrity","secrets","remote-attestation","tpm2-tools","integrity-verification","trusted-computing",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":181,"publishedAt":44},"01M6AGDCW0Y4FXZ3362GT1941M","validating-backup-integrity-for-recovery","Validates backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to confirm backups are reliable for disaster recovery and ransomware response. Use before relying on backups for recovery, when building post-backup validation pipelines, auditing backup infrastructure for compliance, or checking immutable\u002Fair-gapped backups for silent corruption or tampering.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-backup-integrity-for-recovery\u002FSKILL.md",[177,180],{"categoryNo":178,"categoryName":179,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[165,182,183,184,185,42,186,187,159,188,189,190,191,192,193,194,195,196,197,198],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":206,"publishedAt":44},"01M6AGD9GBG2KZ336AE763J3WH","triaging-windows-with-kape","Runs KAPE (Kroll Artifact Parser and Extractor) to collect targeted forensic artifacts (registry hives, $MFT, event logs, prefetch, browser data) via Targets and parse them with Modules wrapping Eric Zimmerman's EZ Tools (PECmd, MFTECmd, RECmd). Use during early incident containment\u002Ftriage when full disk imaging is impractical but a defensible, parseable Windows artifact set is needed quickly, including at-scale remote collection.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-windows-with-kape\u002FSKILL.md",[205],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[40,207,208,42,209,210,211,212,213,214,215,216,217,218,219,220,162,221,222,223],"kape","triaging windows with kape","registry","browser","logs","windows-forensics","dfir","mft","mftecmd","data","prefetch","eric-zimmerman","digital-forensics","SET","collection","pecmd","artifact-collection",{"skillNo":225,"name":226,"nameZh":6,"description":227,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":228,"categories":229,"keywords":231,"publishedAt":44},"01M6AGD5PW5J4EVZM958WD74ZQ","triaging-vulnerabilities-with-ssvc-framework","Triages and prioritizes vulnerabilities with CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree, weighing exploitation status (via the CISA KEV catalog and FIRST EPSS API), technical impact, automatability, and mission prevalence to output Track\u002FTrack*\u002FAttend\u002FAct decisions. Use when prioritizing vulnerability scan results (OpenVAS, Nessus, Qualys) for remediation planning beyond raw CVSS scores.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-vulnerabilities-with-ssvc-framework\u002FSKILL.md",[230],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[232,233,234,42,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250],"vulnerabilities","framework","triaging vulnerabilities with ssvc framework","status","planning","CISA","vulnerability-management","risk-management","nessus","qualys","openvas","cvss","remediation","risk","vulnerability","api","prioritization","ssvc","decision-tree",{"skillNo":252,"name":253,"nameZh":6,"description":254,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":255,"categories":256,"keywords":258,"publishedAt":44},"01M6AGD24TTQQM4CXG69E3B9TJ","triaging-security-incident","Performs initial triage of security incidents using the NIST SP 800-61r3 and SANS PICERL frameworks, classifying incident type, assigning priority by business impact, and routing to the appropriate response team. Use when a SIEM\u002FEDR alert needs human classification, concurrent alerts must be prioritized, or a user report or threat-intel IOC match requires initial incident categorization.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-security-incident\u002FSKILL.md",[257],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[132,259,42,260,37,261,262,263,264,265,266,194,267,195,25,268,269,270,271,272],"triaging security incident","siem","ioc","soc","NIST-800-61","NIST","severity","edr","execution","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]