[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AG4026A4MZ8XGQNFW0SDF5":3,"skill-same:01M6AG4026A4MZ8XGQNFW0SDF5":115},{"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":70,"commitSha":71,"skillDirectory":72,"forksCount":73,"repositoryHtmlUrl":74,"repositoryHomepageUrl":75,"ownerHtmlUrl":76,"ownerBlogUrl":77,"ownerDescription":78,"updatedAt":79,"install":80,"resources":93},"01M6AG4026A4MZ8XGQNFW0SDF5","performing-threat-landscape-assessment-for-sector",null,"Conducts a sector-specific threat landscape assessment (financial, healthcare, energy, government, etc.) by profiling targeting threat actors, mapping attack vectors and MITRE ATT&CK TTPs with the attackcti\u002Fpandas Python stack, and analyzing exploited CVEs and incident trends from ISAC and vendor reports. Use when producing CTI for risk management or board-level reporting on an industry's threat exposure.","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-threat-landscape-assessment-for-sector\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 threat landscape assessment for sector","assessment","attackcti","industry-targeting","sector-analysis","strategic-intelligence","analysis","t1566","risk","ATT&CK","threat-landscape","python","analyzing","cti","threat-intelligence","reporting","file","pandas","validation","cybersecurity","2026-11-10 10:12:30",false,"---\nname: performing-threat-landscape-assessment-for-sector\ndescription: >-\n  Conducts a sector-specific threat landscape assessment (financial,\n  healthcare, energy, government, etc.) by profiling targeting threat actors,\n  mapping attack vectors and MITRE ATT&CK TTPs with the attackcti\u002Fpandas\n  Python stack, and analyzing exploited CVEs and incident trends from ISAC\n  and vendor reports. Use when producing CTI for risk management or\n  board-level reporting on an industry's threat exposure.\ndomain: cybersecurity\nsubdomain: threat-intelligence\ntags:\n- threat-landscape\n- sector-analysis\n- risk-assessment\n- threat-intelligence\n- industry-targeting\n- cti\n- strategic-intelligence\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nd3fend_techniques:\n- File Metadata Consistency Validation\n- Application Protocol Command Analysis\n- Identifier Analysis\n- Content Format Conversion\n- Message Analysis\nnist_csf:\n- ID.RA-01\n- ID.RA-05\n- DE.CM-01\n- DE.AE-02\nmitre_attack:\n- T1591\n- T1592\n- T1593\n- T1589\n- T1566\n---\n# Performing Threat Landscape Assessment for Sector\n\n## Overview\n\nA sector-specific threat landscape assessment analyzes the cyber threat environment facing a particular industry vertical (healthcare, financial services, energy, government, manufacturing) by examining which threat actors target the sector, their preferred attack vectors and TTPs, common vulnerabilities exploited, historical incident data, and emerging threats. This produces actionable intelligence for risk management, security investment prioritization, and board-level reporting.\n\n\n## When to Use\n\n- When conducting security assessments that involve performing threat landscape assessment for sector\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- Python 3.9+ with `attackcti`, `requests`, `pandas`, `matplotlib` libraries\n- Access to threat intelligence feeds (AlienVault OTX, MISP, vendor reports)\n- MITRE ATT&CK knowledge base for TTP mapping\n- Industry-specific ISAC membership (FS-ISAC, H-ISAC, E-ISAC, etc.)\n- Understanding of sector-specific regulatory requirements\n\n## Key Concepts\n\n### Sector Targeting Analysis\n\nDifferent sectors face different threat profiles. Financial services face sophisticated nation-state actors (Lazarus Group) and cybercriminal groups focused on financial fraud. Healthcare faces ransomware groups exploiting urgency and legacy systems. Energy and critical infrastructure face nation-state groups (TEMP.Veles, Sandworm) with destructive capabilities. Government faces espionage-focused APTs (APT29, APT28, Turla).\n\n### Threat Landscape Components\n\nA comprehensive assessment includes: threat actor profiling (groups targeting the sector), attack vector analysis (initial access methods observed), TTP mapping (techniques commonly used against sector), vulnerability landscape (CVEs commonly exploited), incident trend analysis (breach frequency, impact, recovery time), and emerging threats (new groups, evolving techniques, supply chain risks).\n\n### Intelligence Sources\n\nSector-specific intelligence comes from ISACs (Information Sharing and Analysis Centers), government advisories (CISA, FBI, NSA), vendor threat reports (CrowdStrike Annual Threat Report, Mandiant M-Trends, Verizon DBIR), and academic research on sector-specific attacks.\n\n## Workflow\n\n### Step 1: Identify Threat Actors Targeting the Sector\n\n```python\nfrom attackcti import attack_client\nimport json\n\nclass SectorThreatAssessment:\n    SECTOR_GROUPS = {\n        \"financial\": [\"FIN7\", \"FIN8\", \"FIN11\", \"Carbanak\", \"Lazarus Group\",\n                       \"Cobalt Group\", \"TA505\", \"GOLD SOUTHFIELD\"],\n        \"healthcare\": [\"FIN12\", \"Ryuk\", \"Conti\", \"Wizard Spider\",\n                        \"GOLD ULRICK\", \"Vice Society\"],\n        \"energy\": [\"TEMP.Veles\", \"Sandworm Team\", \"Dragonfly\",\n                    \"XENOTIME\", \"ERYTHRITE\", \"Berserk Bear\"],\n        \"government\": [\"APT29\", \"APT28\", \"Turla\", \"Gamaredon Group\",\n                        \"Mustang Panda\", \"APT41\", \"Lazarus Group\"],\n        \"manufacturing\": [\"APT41\", \"TEMP.Veles\", \"Dragonfly\",\n                           \"HEXANE\", \"MAGNALLIUM\"],\n        \"technology\": [\"APT41\", \"Lazarus Group\", \"APT10\",\n                        \"HAFNIUM\", \"Winnti Group\"],\n    }\n\n    def __init__(self, sector):\n        self.sector = sector.lower()\n        self.lift = attack_client()\n        self.groups = self.lift.get_groups()\n        self.assessment = {\n            \"sector\": sector,\n            \"threat_actors\": [],\n            \"common_techniques\": {},\n            \"attack_vectors\": {},\n            \"risk_summary\": {},\n        }\n\n    def analyze_sector_actors(self):\n        \"\"\"Analyze threat actors known to target this sector.\"\"\"\n        target_groups = self.SECTOR_GROUPS.get(self.sector, [])\n        actor_profiles = []\n\n        for group_name in target_groups:\n            group = next(\n                (g for g in self.groups\n                 if g.get(\"name\", \"\").lower() == group_name.lower()\n                 or group_name.lower() in [a.lower() for a in g.get(\"aliases\", [])]),\n                None\n            )\n            if group:\n                group_id = \"\"\n                for ref in group.get(\"external_references\", []):\n                    if ref.get(\"source_name\") == \"mitre-attack\":\n                        group_id = ref.get(\"external_id\", \"\")\n                        break\n\n                techniques = []\n                if group_id:\n                    techs = self.lift.get_techniques_used_by_group(group_id)\n                    for t in techs:\n                        for ref in t.get(\"external_references\", []):\n                            if ref.get(\"source_name\") == \"mitre-attack\":\n                                techniques.append({\n                                    \"id\": ref.get(\"external_id\", \"\"),\n                                    \"name\": t.get(\"name\", \"\"),\n                                })\n                                break\n\n                profile = {\n                    \"name\": group.get(\"name\", \"\"),\n                    \"aliases\": group.get(\"aliases\", []),\n                    \"description\": group.get(\"description\", \"\")[:300],\n                    \"attack_id\": group_id,\n                    \"technique_count\": len(techniques),\n                    \"techniques\": techniques[:20],\n                }\n                actor_profiles.append(profile)\n                print(f\"  [+] {group.get('name')}: {len(techniques)} techniques\")\n\n        self.assessment[\"threat_actors\"] = actor_profiles\n        print(f\"[+] Profiled {len(actor_profiles)} threat actors for {self.sector}\")\n        return actor_profiles\n\n    def identify_common_techniques(self):\n        \"\"\"Find the most commonly used techniques across sector actors.\"\"\"\n        from collections import Counter\n        technique_counter = Counter()\n\n        for actor in self.assessment[\"threat_actors\"]:\n            for tech in actor.get(\"techniques\", []):\n                technique_counter[f\"{tech['id']}:{tech['name']}\"] += 1\n\n        common = technique_counter.most_common(20)\n        self.assessment[\"common_techniques\"] = [\n            {\n                \"technique\": tech.split(\":\")[0],\n                \"name\": tech.split(\":\")[1] if \":\" in tech else \"\",\n                \"actor_count\": count,\n                \"actors_using\": [\n                    a[\"name\"] for a in self.assessment[\"threat_actors\"]\n                    if any(t[\"id\"] == tech.split(\":\")[0] for t in a.get(\"techniques\", []))\n                ],\n            }\n            for tech, count in common\n        ]\n\n        print(f\"\\n=== Top Techniques for {self.sector.upper()} ===\")\n        for entry in self.assessment[\"common_techniques\"][:10]:\n            print(f\"  {entry['technique']} {entry['name']}: \"\n                  f\"used by {entry['actor_count']} groups\")\n\n        return self.assessment[\"common_techniques\"]\n\nassessment = SectorThreatAssessment(\"financial\")\nassessment.analyze_sector_actors()\nassessment.identify_common_techniques()\n```\n\n### Step 2: Analyze Attack Vectors and Initial Access\n\n```python\ndef analyze_attack_vectors(assessment):\n    \"\"\"Analyze initial access vectors common for the sector.\"\"\"\n    initial_access_techniques = [\n        t for t in assessment.assessment[\"common_techniques\"]\n        if t[\"technique\"].startswith(\"T1566\") or t[\"technique\"].startswith(\"T1190\")\n        or t[\"technique\"].startswith(\"T1133\") or t[\"technique\"].startswith(\"T1078\")\n        or t[\"technique\"].startswith(\"T1195\")\n    ]\n\n    # Supplement with known sector-specific vectors\n    sector_vectors = {\n        \"financial\": {\n            \"primary\": [\"Spearphishing (T1566)\", \"Exploit Public-Facing App (T1190)\",\n                        \"Valid Accounts (T1078)\", \"Supply Chain Compromise (T1195)\"],\n            \"emerging\": [\"MFA Fatigue\u002FPush Bombing\", \"QR Code Phishing (Quishing)\",\n                         \"Business Email Compromise\", \"API Key Theft\"],\n        },\n        \"healthcare\": {\n            \"primary\": [\"Spearphishing (T1566)\", \"Exploit Public-Facing App (T1190)\",\n                        \"External Remote Services (T1133)\", \"Valid Accounts (T1078)\"],\n            \"emerging\": [\"IoMT Device Exploitation\", \"Telehealth Platform Attacks\",\n                         \"Medical Device Firmware Attacks\", \"Supply Chain via EHR Vendors\"],\n        },\n        \"energy\": {\n            \"primary\": [\"Spearphishing (T1566)\", \"Exploit Public-Facing App (T1190)\",\n                        \"External Remote Services (T1133)\", \"Supply Chain Compromise (T1195)\"],\n            \"emerging\": [\"OT\u002FICS Protocol Exploitation\", \"Remote Access to SCADA\",\n                         \"Engineering Workstation Compromise\", \"Vendor VPN Exploitation\"],\n        },\n    }\n\n    vectors = sector_vectors.get(assessment.sector, {})\n    assessment.assessment[\"attack_vectors\"] = vectors\n    return vectors\n```\n\n### Step 3: Generate Sector Threat Report\n\n```python\ndef generate_sector_report(assessment):\n    data = assessment.assessment\n    report = f\"\"\"# {data['sector'].title()} Sector Threat Landscape Assessment\nGenerated: {datetime.datetime.now().isoformat()}\n\n## Executive Summary\nThis assessment analyzes the cyber threat landscape for the {data['sector']} sector,\nidentifying {len(data['threat_actors'])} active threat groups, their preferred techniques,\nand recommended defensive priorities.\n\n## Threat Actor Summary\n| Actor | ATT&CK ID | Techniques | Key Focus |\n|-------|-----------|------------|-----------|\n\"\"\"\n    for actor in data[\"threat_actors\"]:\n        report += (f\"| {actor['name']} | {actor['attack_id']} \"\n                   f\"| {actor['technique_count']} | {actor['description'][:60]}... |\\n\")\n\n    report += f\"\"\"\n## Most Common Techniques\n| Rank | Technique | Name | Groups Using |\n|------|-----------|------|-------------|\n\"\"\"\n    for i, tech in enumerate(data.get(\"common_techniques\", [])[:15], 1):\n        actors = \", \".join(tech[\"actors_using\"][:3])\n        report += f\"| {i} | {tech['technique']} | {tech['name']} | {actors} |\\n\"\n\n    vectors = data.get(\"attack_vectors\", {})\n    report += f\"\"\"\n## Attack Vectors\n### Primary Vectors\n\"\"\"\n    for v in vectors.get(\"primary\", []):\n        report += f\"- {v}\\n\"\n    report += \"\\n### Emerging Vectors\\n\"\n    for v in vectors.get(\"emerging\", []):\n        report += f\"- {v}\\n\"\n\n    report += \"\"\"\n## Recommendations\n1. Prioritize detections for the top 10 techniques used by sector-targeting groups\n2. Conduct threat-informed red team exercises mimicking identified actors\n3. Join sector ISAC for real-time threat sharing\n4. Implement controls for identified initial access vectors\n5. Review supply chain security posture for sector-specific risks\n\"\"\"\n    with open(f\"threat_landscape_{data['sector']}.md\", \"w\") as f:\n        f.write(report)\n    print(f\"[+] Sector report saved: threat_landscape_{data['sector']}.md\")\n\ngenerate_sector_report(assessment)\n```\n\n## Validation Criteria\n\n- Sector-specific threat actors identified and profiled\n- Common techniques across actors analyzed and ranked\n- Attack vectors mapped for the target sector\n- Emerging threats identified based on recent intelligence\n- Comprehensive sector threat report generated\n- Recommendations actionable for security investment decisions\n\n## References\n\n- [MITRE ATT&CK Groups](https:\u002F\u002Fattack.mitre.org\u002Fgroups\u002F)\n- [Verizon DBIR](https:\u002F\u002Fwww.verizon.com\u002Fbusiness\u002Fresources\u002Freports\u002Fdbir\u002F)\n- [CrowdStrike Global Threat Report](https:\u002F\u002Fwww.crowdstrike.com\u002Fglobal-threat-report\u002F)\n- [FS-ISAC Financial Sector](https:\u002F\u002Fwww.fsisac.com\u002F)\n- [H-ISAC Healthcare Sector](https:\u002F\u002Fh-isac.org\u002F)\n- [CyCognito: Threat Intelligence Lifecycle](https:\u002F\u002Fwww.cycognito.com\u002Flearn\u002Fthreat-intelligence\u002F)\n","# Performing Threat Landscape Assessment for Sector\n\n## Overview\n\nA sector-specific threat landscape assessment analyzes the cyber threat environment facing a particular industry vertical (healthcare, financial services, energy, government, manufacturing) by examining which threat actors target the sector, their preferred attack vectors and TTPs, common vulnerabilities exploited, historical incident data, and emerging threats. This produces actionable intelligence for risk management, security investment prioritization, and board-level reporting.\n\n\n## When to Use\n\n- When conducting security assessments that involve performing threat landscape assessment for sector\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- Python 3.9+ with `attackcti`, `requests`, `pandas`, `matplotlib` libraries\n- Access to threat intelligence feeds (AlienVault OTX, MISP, vendor reports)\n- MITRE ATT&CK knowledge base for TTP mapping\n- Industry-specific ISAC membership (FS-ISAC, H-ISAC, E-ISAC, etc.)\n- Understanding of sector-specific regulatory requirements\n\n## Key Concepts\n\n### Sector Targeting Analysis\n\nDifferent sectors face different threat profiles. Financial services face sophisticated nation-state actors (Lazarus Group) and cybercriminal groups focused on financial fraud. Healthcare faces ransomware groups exploiting urgency and legacy systems. Energy and critical infrastructure face nation-state groups (TEMP.Veles, Sandworm) with destructive capabilities. Government faces espionage-focused APTs (APT29, APT28, Turla).\n\n### Threat Landscape Components\n\nA comprehensive assessment includes: threat actor profiling (groups targeting the sector), attack vector analysis (initial access methods observed), TTP mapping (techniques commonly used against sector), vulnerability landscape (CVEs commonly exploited), incident trend analysis (breach frequency, impact, recovery time), and emerging threats (new groups, evolving techniques, supply chain risks).\n\n### Intelligence Sources\n\nSector-specific intelligence comes from ISACs (Information Sharing and Analysis Centers), government advisories (CISA, FBI, NSA), vendor threat reports (CrowdStrike Annual Threat Report, Mandiant M-Trends, Verizon DBIR), and academic research on sector-specific attacks.\n\n## Workflow\n\n### Step 1: Identify Threat Actors Targeting the Sector\n\n```python\nfrom attackcti import attack_client\nimport json\n\nclass SectorThreatAssessment:\n    SECTOR_GROUPS = {\n        \"financial\": [\"FIN7\", \"FIN8\", \"FIN11\", \"Carbanak\", \"Lazarus Group\",\n                       \"Cobalt Group\", \"TA505\", \"GOLD SOUTHFIELD\"],\n        \"healthcare\": [\"FIN12\", \"Ryuk\", \"Conti\", \"Wizard Spider\",\n                        \"GOLD ULRICK\", \"Vice Society\"],\n        \"energy\": [\"TEMP.Veles\", \"Sandworm Team\", \"Dragonfly\",\n                    \"XENOTIME\", \"ERYTHRITE\", \"Berserk Bear\"],\n        \"government\": [\"APT29\", \"APT28\", \"Turla\", \"Gamaredon Group\",\n                        \"Mustang Panda\", \"APT41\", \"Lazarus Group\"],\n        \"manufacturing\": [\"APT41\", \"TEMP.Veles\", \"Dragonfly\",\n                           \"HEXANE\", \"MAGNALLIUM\"],\n        \"technology\": [\"APT41\", \"Lazarus Group\", \"APT10\",\n                        \"HAFNIUM\", \"Winnti Group\"],\n    }\n\n    def __init__(self, sector):\n        self.sector = sector.lower()\n        self.lift = attack_client()\n        self.groups = self.lift.get_groups()\n        self.assessment = {\n            \"sector\": sector,\n            \"threat_actors\": [],\n            \"common_techniques\": {},\n            \"attack_vectors\": {},\n            \"risk_summary\": {},\n        }\n\n    def analyze_sector_actors(self):\n        \"\"\"Analyze threat actors known to target this sector.\"\"\"\n        target_groups = self.SECTOR_GROUPS.get(self.sector, [])\n        actor_profiles = []\n\n        for group_name in target_groups:\n            group = next(\n                (g for g in self.groups\n                 if g.get(\"name\", \"\").lower() == group_name.lower()\n                 or group_name.lower() in [a.lower() for a in g.get(\"aliases\", [])]),\n                None\n            )\n            if group:\n                group_id = \"\"\n                for ref in group.get(\"external_references\", []):\n                    if ref.get(\"source_name\") == \"mitre-attack\":\n                        group_id = ref.get(\"external_id\", \"\")\n                        break\n\n                techniques = []\n                if group_id:\n                    techs = self.lift.get_techniques_used_by_group(group_id)\n                    for t in techs:\n                        for ref in t.get(\"external_references\", []):\n                            if ref.get(\"source_name\") == \"mitre-attack\":\n                                techniques.append({\n                                    \"id\": ref.get(\"external_id\", \"\"),\n                                    \"name\": t.get(\"name\", \"\"),\n                                })\n                                break\n\n                profile = {\n                    \"name\": group.get(\"name\", \"\"),\n                    \"aliases\": group.get(\"aliases\", []),\n                    \"description\": group.get(\"description\", \"\")[:300],\n                    \"attack_id\": group_id,\n                    \"technique_count\": len(techniques),\n                    \"techniques\": techniques[:20],\n                }\n                actor_profiles.append(profile)\n                print(f\"  [+] {group.get('name')}: {len(techniques)} techniques\")\n\n        self.assessment[\"threat_actors\"] = actor_profiles\n        print(f\"[+] Profiled {len(actor_profiles)} threat actors for {self.sector}\")\n        return actor_profiles\n\n    def identify_common_techniques(self):\n        \"\"\"Find the most commonly used techniques across sector actors.\"\"\"\n        from collections import Counter\n        technique_counter = Counter()\n\n        for actor in self.assessment[\"threat_actors\"]:\n            for tech in actor.get(\"techniques\", []):\n                technique_counter[f\"{tech['id']}:{tech['name']}\"] += 1\n\n        common = technique_counter.most_common(20)\n        self.assessment[\"common_techniques\"] = [\n            {\n                \"technique\": tech.split(\":\")[0],\n                \"name\": tech.split(\":\")[1] if \":\" in tech else \"\",\n                \"actor_count\": count,\n                \"actors_using\": [\n                    a[\"name\"] for a in self.assessment[\"threat_actors\"]\n                    if any(t[\"id\"] == tech.split(\":\")[0] for t in a.get(\"techniques\", []))\n                ],\n            }\n            for tech, count in common\n        ]\n\n        print(f\"\\n=== Top Techniques for {self.sector.upper()} ===\")\n        for entry in self.assessment[\"common_techniques\"][:10]:\n            print(f\"  {entry['technique']} {entry['name']}: \"\n                  f\"used by {entry['actor_count']} groups\")\n\n        return self.assessment[\"common_techniques\"]\n\nassessment = SectorThreatAssessment(\"financial\")\nassessment.analyze_sector_actors()\nassessment.identify_common_techniques()\n```\n\n### Step 2: Analyze Attack Vectors and Initial Access\n\n```python\ndef analyze_attack_vectors(assessment):\n    \"\"\"Analyze initial access vectors common for the sector.\"\"\"\n    initial_access_techniques = [\n        t for t in assessment.assessment[\"common_techniques\"]\n        if t[\"technique\"].startswith(\"T1566\") or t[\"technique\"].startswith(\"T1190\")\n        or t[\"technique\"].startswith(\"T1133\") or t[\"technique\"].startswith(\"T1078\")\n        or t[\"technique\"].startswith(\"T1195\")\n    ]\n\n    # Supplement with known sector-specific vectors\n    sector_vectors = {\n        \"financial\": {\n            \"primary\": [\"Spearphishing (T1566)\", \"Exploit Public-Facing App (T1190)\",\n                        \"Valid Accounts (T1078)\", \"Supply Chain Compromise (T1195)\"],\n            \"emerging\": [\"MFA Fatigue\u002FPush Bombing\", \"QR Code Phishing (Quishing)\",\n                         \"Business Email Compromise\", \"API Key Theft\"],\n        },\n        \"healthcare\": {\n            \"primary\": [\"Spearphishing (T1566)\", \"Exploit Public-Facing App (T1190)\",\n                        \"External Remote Services (T1133)\", \"Valid Accounts (T1078)\"],\n            \"emerging\": [\"IoMT Device Exploitation\", \"Telehealth Platform Attacks\",\n                         \"Medical Device Firmware Attacks\", \"Supply Chain via EHR Vendors\"],\n        },\n        \"energy\": {\n            \"primary\": [\"Spearphishing (T1566)\", \"Exploit Public-Facing App (T1190)\",\n                        \"External Remote Services (T1133)\", \"Supply Chain Compromise (T1195)\"],\n            \"emerging\": [\"OT\u002FICS Protocol Exploitation\", \"Remote Access to SCADA\",\n                         \"Engineering Workstation Compromise\", \"Vendor VPN Exploitation\"],\n        },\n    }\n\n    vectors = sector_vectors.get(assessment.sector, {})\n    assessment.assessment[\"attack_vectors\"] = vectors\n    return vectors\n```\n\n### Step 3: Generate Sector Threat Report\n\n```python\ndef generate_sector_report(assessment):\n    data = assessment.assessment\n    report = f\"\"\"# {data['sector'].title()} Sector Threat Landscape Assessment\nGenerated: {datetime.datetime.now().isoformat()}\n\n## Executive Summary\nThis assessment analyzes the cyber threat landscape for the {data['sector']} sector,\nidentifying {len(data['threat_actors'])} active threat groups, their preferred techniques,\nand recommended defensive priorities.\n\n## Threat Actor Summary\n| Actor | ATT&CK ID | Techniques | Key Focus |\n|-------|-----------|------------|-----------|\n\"\"\"\n    for actor in data[\"threat_actors\"]:\n        report += (f\"| {actor['name']} | {actor['attack_id']} \"\n                   f\"| {actor['technique_count']} | {actor['description'][:60]}... |\\n\")\n\n    report += f\"\"\"\n## Most Common Techniques\n| Rank | Technique | Name | Groups Using |\n|------|-----------|------|-------------|\n\"\"\"\n    for i, tech in enumerate(data.get(\"common_techniques\", [])[:15], 1):\n        actors = \", \".join(tech[\"actors_using\"][:3])\n        report += f\"| {i} | {tech['technique']} | {tech['name']} | {actors} |\\n\"\n\n    vectors = data.get(\"attack_vectors\", {})\n    report += f\"\"\"\n## Attack Vectors\n### Primary Vectors\n\"\"\"\n    for v in vectors.get(\"primary\", []):\n        report += f\"- {v}\\n\"\n    report += \"\\n### Emerging Vectors\\n\"\n    for v in vectors.get(\"emerging\", []):\n        report += f\"- {v}\\n\"\n\n    report += \"\"\"\n## Recommendations\n1. Prioritize detections for the top 10 techniques used by sector-targeting groups\n2. Conduct threat-informed red team exercises mimicking identified actors\n3. Join sector ISAC for real-time threat sharing\n4. Implement controls for identified initial access vectors\n5. Review supply chain security posture for sector-specific risks\n\"\"\"\n    with open(f\"threat_landscape_{data['sector']}.md\", \"w\") as f:\n        f.write(report)\n    print(f\"[+] Sector report saved: threat_landscape_{data['sector']}.md\")\n\ngenerate_sector_report(assessment)\n```\n\n## Validation Criteria\n\n- Sector-specific threat actors identified and profiled\n- Common techniques across actors analyzed and ranked\n- Attack vectors mapped for the target sector\n- Emerging threats identified based on recent intelligence\n- Comprehensive sector threat report generated\n- Recommendations actionable for security investment decisions\n\n## References\n\n- [MITRE ATT&CK Groups](https:\u002F\u002Fattack.mitre.org\u002Fgroups\u002F)\n- [Verizon DBIR](https:\u002F\u002Fwww.verizon.com\u002Fbusiness\u002Fresources\u002Freports\u002Fdbir\u002F)\n- [CrowdStrike Global Threat Report](https:\u002F\u002Fwww.crowdstrike.com\u002Fglobal-threat-report\u002F)\n- [FS-ISAC Financial Sector](https:\u002F\u002Fwww.fsisac.com\u002F)\n- [H-ISAC Healthcare Sector](https:\u002F\u002Fh-isac.org\u002F)\n- [CyCognito: Threat Intelligence Lifecycle](https:\u002F\u002Fwww.cycognito.com\u002Flearn\u002Fthreat-intelligence\u002F)\n",{"name":5,"tags":49,"author":51,"domain":43,"license":15,"version":52,"nist_csf":53,"subdomain":38,"description":7,"mitre_attack":58,"d3fend_techniques":64},[34,28,50,38,27,37,29],"risk-assessment","mahipal","1.0",[54,55,56,57],"ID.RA-01","ID.RA-05","DE.CM-01","DE.AE-02",[59,60,61,62,63],"T1591","T1592","T1593","T1589","T1566",[65,66,67,68,69],"File Metadata Consistency Validation","Application Protocol Command Analysis","Identifier Analysis","Content Format Conversion","Message Analysis","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fperforming-threat-landscape-assessment-for-sector",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":81,"prompt":91,"zipAvailable":92},[82,85,88],{"tool":83,"command":84},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-threat-landscape-assessment-for-sector",{"tool":86,"command":87},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-threat-landscape-assessment-for-sector",{"tool":89,"command":90},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill performing-threat-landscape-assessment-for-sector","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-threat-landscape-assessment-for-sector\u002FSKILL.md",true,[94,99,105,111],{"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-threat-landscape-assessment-for-sector\u002FLICENSE",{"path":100,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fapi-reference.md","REFERENCE","参考文档",1995,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-threat-landscape-assessment-for-sector\u002Freferences\u002Fapi-reference.md",{"path":106,"resourceType":107,"resourceTypeDesc":108,"sizeBytes":109,"sourceHtmlUrl":110},"scripts\u002Fagent.py","SCRIPT","脚本",6357,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fperforming-threat-landscape-assessment-for-sector\u002Fscripts\u002Fagent.py",{"path":112,"resourceType":107,"resourceTypeDesc":108,"sizeBytes":113,"sourceHtmlUrl":114},"scripts\u002Fvoice_analyzer.py",18507,"https:\u002F\u002Fgithub.com\u002Fjmagly\u002Faiwg\u002Fblob\u002Fmain\u002Fagentic\u002Fcode\u002Faddons\u002Fvoice-framework\u002Fskills\u002Fvoice-analyze\u002Fscripts\u002Fvoice_analyzer.py",[116,143,167,194,220,246],{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":123,"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",[122],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[124,125,126,127,128,43,129,130,131,132,133,134,135,136,137,138,139,140,141,142],"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":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":150,"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",[149],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[151,139,152,153,128,154,43,131,155,156,157,158,159,160,161,162,163,164,165,166],"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":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":177,"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",[173,176],{"categoryNo":174,"categoryName":175,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[161,178,179,180,181,43,182,42,155,183,184,185,186,187,188,189,190,191,192,193],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":201,"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",[200],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[202,203,204,43,205,206,207,208,209,210,211,212,213,214,215,216,158,217,218,219],"windows","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":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":224,"categories":225,"keywords":227,"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",[226],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[228,229,230,43,231,232,233,234,235,236,237,238,239,240,32,241,242,243,244,245],"vulnerabilities","framework","triaging vulnerabilities with ssvc framework","status","planning","CISA","vulnerability-management","risk-management","nessus","qualys","openvas","cvss","remediation","vulnerability","api","prioritization","ssvc","decision-tree",{"skillNo":247,"name":248,"nameZh":6,"description":249,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":250,"categories":251,"keywords":253,"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",[252],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[128,254,43,255,40,256,257,258,259,260,261,189,262,190,30,263,264,265,266,267],"triaging security incident","siem","ioc","soc","NIST-800-61","NIST","severity","edr","execution","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]