[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADSX0KN81AMAS0J8RPK50M":3,"skill-same:01M6ADSX0KN81AMAS0J8RPK50M":127},{"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":68,"commitSha":69,"skillDirectory":70,"forksCount":71,"repositoryHtmlUrl":72,"repositoryHomepageUrl":73,"ownerHtmlUrl":74,"ownerBlogUrl":75,"ownerDescription":76,"updatedAt":77,"install":78,"resources":91},"01M6ADSX0KN81AMAS0J8RPK50M","analyzing-threat-actor-ttps-with-mitre-attack",null,"Systematically map threat actor behavior and observed IOCs to the MITRE ATT&CK framework, build technique coverage heatmaps with the ATT&CK Navigator, identify detection gaps, and produce actionable threat intelligence reports across the Enterprise, Mobile, and ICS matrices. Use when analyzing threat actor TTPs, correlating IOCs to specific ATT&CK techniques, or assessing defensive detection coverage against adversary behavior.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",36078,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-threat-actor-ttps-with-mitre-attack\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],"analyzing threat actor ttps with mitre attack","analyzing","mitre attack","ttp-analysis","threat-actors","T1059.001","Enterprise","stix","ioc","cti","threat-intelligence","cybersecurity","navigator","mitre-attack","file","metadata","content","produce","validation","threat intelligence","2026-11-10 10:12:30",false,"---\nname: analyzing-threat-actor-ttps-with-mitre-attack\ndescription: Systematically map threat actor behavior and observed IOCs to the MITRE ATT&CK framework, build technique coverage heatmaps with the ATT&CK Navigator, identify detection gaps, and produce actionable threat intelligence reports across the Enterprise, Mobile, and ICS matrices. Use when analyzing threat actor TTPs, correlating IOCs to specific ATT&CK techniques, or assessing defensive detection coverage against adversary behavior.\ndomain: cybersecurity\nsubdomain: threat-intelligence\ntags:\n- threat-intelligence\n- cti\n- ioc\n- mitre-attack\n- stix\n- ttp-analysis\n- threat-actors\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nd3fend_techniques:\n- Executable Denylisting\n- Execution Isolation\n- File Metadata Consistency Validation\n- Content Format Conversion\n- File Content Analysis\nnist_csf:\n- ID.RA-01\n- ID.RA-05\n- DE.CM-01\n- DE.AE-02\nmitre_attack:\n- T1566.001\n- T1059.001\n- T1071.001\n- T1547.001\n- T1053.005\n---\n# Analyzing Threat Actor TTPs with MITRE ATT&CK\n\n## Overview\n\nMITRE ATT&CK is a globally-accessible knowledge base of adversary tactics, techniques, and procedures (TTPs) based on real-world observations. This skill covers systematically mapping threat actor behavior to the ATT&CK framework, building technique coverage heatmaps using the ATT&CK Navigator, identifying detection gaps, and producing actionable intelligence reports that link observed IOCs to specific adversary techniques across the Enterprise, Mobile, and ICS matrices.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing threat actor ttps with mitre attack\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Python 3.9+ with `mitreattack-python`, `attackcti`, `stix2` libraries\n- MITRE ATT&CK Navigator (web-based or local deployment)\n- Understanding of ATT&CK matrix structure: Tactics, Techniques, Sub-techniques\n- Access to threat intelligence reports or MISP\u002FOpenCTI for threat actor data\n- Familiarity with STIX 2.1 Attack Pattern objects\n\n## Key Concepts\n\n### ATT&CK Matrix Structure\n\nThe ATT&CK Enterprise matrix organizes adversary behavior into 14 Tactics (the \"why\") containing Techniques (the \"how\") and Sub-techniques (specific implementations). Each technique has associated data sources, detections, mitigations, and real-world procedure examples from observed threat groups.\n\n### Threat Group Profiles\n\nATT&CK catalogs over 140 threat groups (e.g., APT28, APT29, Lazarus Group, FIN7) with documented technique usage. Each group profile includes aliases, targeted sectors, associated campaigns, software used, and technique mappings with procedure-level detail.\n\n### ATT&CK Navigator\n\nThe ATT&CK Navigator is a web-based tool for creating custom ATT&CK matrix visualizations. Analysts create layers (JSON files) that annotate techniques with scores, colors, comments, and metadata to visualize threat actor coverage, detection capabilities, or risk assessments.\n\n## Workflow\n\n### Step 1: Query ATT&CK Data Programmatically\n\n```python\nfrom attackcti import attack_client\nimport json\n\n# Initialize ATT&CK client (queries MITRE TAXII server)\nlift = attack_client()\n\n# Get all Enterprise techniques\nenterprise_techniques = lift.get_enterprise_techniques()\nprint(f\"Total Enterprise techniques: {len(enterprise_techniques)}\")\n\n# Get all threat groups\ngroups = lift.get_groups()\nprint(f\"Total threat groups: {len(groups)}\")\n\n# Get specific group by name\napt29 = [g for g in groups if 'APT29' in g.get('name', '')]\nif apt29:\n    group = apt29[0]\n    print(f\"Group: {group['name']}\")\n    print(f\"Aliases: {group.get('aliases', [])}\")\n    print(f\"Description: {group.get('description', '')[:200]}\")\n```\n\n### Step 2: Map Threat Actor to ATT&CK Techniques\n\n```python\nfrom attackcti import attack_client\n\nlift = attack_client()\n\n# Get techniques used by APT29\napt29_techniques = lift.get_techniques_used_by_group(\"G0016\")  # APT29 group ID\n\ntechnique_map = {}\nfor entry in apt29_techniques:\n    tech_id = entry.get(\"external_references\", [{}])[0].get(\"external_id\", \"\")\n    tech_name = entry.get(\"name\", \"\")\n    description = entry.get(\"description\", \"\")\n    tactic_refs = [\n        phase.get(\"phase_name\", \"\")\n        for phase in entry.get(\"kill_chain_phases\", [])\n    ]\n\n    technique_map[tech_id] = {\n        \"name\": tech_name,\n        \"tactics\": tactic_refs,\n        \"description\": description[:300],\n    }\n\nprint(f\"\\nAPT29 uses {len(technique_map)} techniques:\")\nfor tid, info in sorted(technique_map.items()):\n    print(f\"  {tid}: {info['name']} [{', '.join(info['tactics'])}]\")\n```\n\n### Step 3: Generate ATT&CK Navigator Layer\n\n```python\nimport json\n\ndef create_navigator_layer(group_name, technique_map, description=\"\"):\n    \"\"\"Generate ATT&CK Navigator layer JSON for a threat group.\"\"\"\n    techniques_list = []\n    for tech_id, info in technique_map.items():\n        techniques_list.append({\n            \"techniqueID\": tech_id,\n            \"tactic\": info[\"tactics\"][0] if info[\"tactics\"] else \"\",\n            \"color\": \"#ff6666\",  # Red for observed techniques\n            \"comment\": info[\"description\"][:200],\n            \"enabled\": True,\n            \"score\": 100,\n            \"metadata\": [\n                {\"name\": \"group\", \"value\": group_name},\n            ],\n        })\n\n    layer = {\n        \"name\": f\"{group_name} TTP Coverage\",\n        \"versions\": {\n            \"attack\": \"16.1\",\n            \"navigator\": \"5.1.0\",\n            \"layer\": \"4.5\",\n        },\n        \"domain\": \"enterprise-attack\",\n        \"description\": description or f\"Techniques attributed to {group_name}\",\n        \"filters\": {\"platforms\": [\"Windows\", \"Linux\", \"macOS\", \"Cloud\"]},\n        \"sorting\": 0,\n        \"layout\": {\n            \"layout\": \"side\",\n            \"aggregateFunction\": \"average\",\n            \"showID\": True,\n            \"showName\": True,\n            \"showAggregateScores\": False,\n            \"countUnscored\": False,\n        },\n        \"hideDisabled\": False,\n        \"techniques\": techniques_list,\n        \"gradient\": {\n            \"colors\": [\"#ffffff\", \"#ff6666\"],\n            \"minValue\": 0,\n            \"maxValue\": 100,\n        },\n        \"legendItems\": [\n            {\"label\": \"Observed technique\", \"color\": \"#ff6666\"},\n            {\"label\": \"Not observed\", \"color\": \"#ffffff\"},\n        ],\n        \"showTacticRowBackground\": True,\n        \"tacticRowBackground\": \"#dddddd\",\n        \"selectTechniquesAcrossTactics\": True,\n        \"selectSubtechniquesWithParent\": False,\n        \"selectVisibleTechniques\": False,\n    }\n\n    return layer\n\n\n# Generate and save layer\nlayer = create_navigator_layer(\"APT29\", technique_map, \"APT29 (Cozy Bear) TTP analysis\")\nwith open(\"apt29_navigator_layer.json\", \"w\") as f:\n    json.dump(layer, f, indent=2)\nprint(\"[+] Navigator layer saved to apt29_navigator_layer.json\")\n```\n\n### Step 4: Identify Detection Gaps\n\n```python\nfrom attackcti import attack_client\n\nlift = attack_client()\n\n# Get all techniques with data sources\nall_techniques = lift.get_enterprise_techniques()\n\n# Build data source coverage map\ndata_source_coverage = {}\nfor tech in all_techniques:\n    tech_id = tech.get(\"external_references\", [{}])[0].get(\"external_id\", \"\")\n    data_sources = tech.get(\"x_mitre_data_sources\", [])\n\n    for ds in data_sources:\n        if ds not in data_source_coverage:\n            data_source_coverage[ds] = []\n        data_source_coverage[ds].append(tech_id)\n\n# Compare threat actor techniques against available detections\ndetected_techniques = {\"T1059\", \"T1071\", \"T1566\"}  # Example: techniques you can detect\nactor_techniques = set(technique_map.keys())\n\ncovered = actor_techniques.intersection(detected_techniques)\ngaps = actor_techniques - detected_techniques\n\nprint(f\"\\n=== Detection Gap Analysis for APT29 ===\")\nprint(f\"Actor techniques: {len(actor_techniques)}\")\nprint(f\"Detected: {len(covered)} ({len(covered)\u002Flen(actor_techniques)*100:.0f}%)\")\nprint(f\"Gaps: {len(gaps)} ({len(gaps)\u002Flen(actor_techniques)*100:.0f}%)\")\nprint(f\"\\nUndetected techniques:\")\nfor tech_id in sorted(gaps):\n    if tech_id in technique_map:\n        print(f\"  {tech_id}: {technique_map[tech_id]['name']}\")\n```\n\n### Step 5: Cross-Group Technique Comparison\n\n```python\nfrom attackcti import attack_client\n\nlift = attack_client()\n\n# Compare techniques across multiple groups\ngroups_to_compare = {\n    \"G0016\": \"APT29\",\n    \"G0007\": \"APT28\",\n    \"G0032\": \"Lazarus Group\",\n}\n\ngroup_techniques = {}\nfor gid, gname in groups_to_compare.items():\n    techs = lift.get_techniques_used_by_group(gid)\n    tech_ids = set()\n    for t in techs:\n        tid = t.get(\"external_references\", [{}])[0].get(\"external_id\", \"\")\n        if tid:\n            tech_ids.add(tid)\n    group_techniques[gname] = tech_ids\n\n# Find common and unique techniques\nall_groups = list(group_techniques.keys())\ncommon_to_all = set.intersection(*group_techniques.values())\nprint(f\"\\nTechniques common to all {len(all_groups)} groups: {len(common_to_all)}\")\nfor tid in sorted(common_to_all):\n    print(f\"  {tid}\")\n\nfor gname, techs in group_techniques.items():\n    unique = techs - set.union(*[t for n, t in group_techniques.items() if n != gname])\n    print(f\"\\nUnique to {gname}: {len(unique)} techniques\")\n```\n\n## Validation Criteria\n\n- ATT&CK data successfully queried via TAXII server or local copy\n- Threat actor mapped to specific techniques with procedure examples\n- ATT&CK Navigator layer JSON is valid and renders correctly\n- Detection gap analysis identifies unmonitored techniques\n- Cross-group comparison reveals shared and unique TTPs\n- Output is actionable for detection engineering prioritization\n\n## References\n\n- [MITRE ATT&CK](https:\u002F\u002Fattack.mitre.org\u002F)\n- [ATT&CK Navigator](https:\u002F\u002Fmitre-attack.github.io\u002Fattack-navigator\u002F)\n- [attackcti Python Library](https:\u002F\u002Fgithub.com\u002FOTRF\u002FATTACK-Python-Client)\n- [ATT&CK STIX Data](https:\u002F\u002Fgithub.com\u002Fmitre\u002Fcti)\n- [ATT&CK Groups](https:\u002F\u002Fattack.mitre.org\u002Fgroups\u002F)\n","# Analyzing Threat Actor TTPs with MITRE ATT&CK\n\n## Overview\n\nMITRE ATT&CK is a globally-accessible knowledge base of adversary tactics, techniques, and procedures (TTPs) based on real-world observations. This skill covers systematically mapping threat actor behavior to the ATT&CK framework, building technique coverage heatmaps using the ATT&CK Navigator, identifying detection gaps, and producing actionable intelligence reports that link observed IOCs to specific adversary techniques across the Enterprise, Mobile, and ICS matrices.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing threat actor ttps with mitre attack\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Python 3.9+ with `mitreattack-python`, `attackcti`, `stix2` libraries\n- MITRE ATT&CK Navigator (web-based or local deployment)\n- Understanding of ATT&CK matrix structure: Tactics, Techniques, Sub-techniques\n- Access to threat intelligence reports or MISP\u002FOpenCTI for threat actor data\n- Familiarity with STIX 2.1 Attack Pattern objects\n\n## Key Concepts\n\n### ATT&CK Matrix Structure\n\nThe ATT&CK Enterprise matrix organizes adversary behavior into 14 Tactics (the \"why\") containing Techniques (the \"how\") and Sub-techniques (specific implementations). Each technique has associated data sources, detections, mitigations, and real-world procedure examples from observed threat groups.\n\n### Threat Group Profiles\n\nATT&CK catalogs over 140 threat groups (e.g., APT28, APT29, Lazarus Group, FIN7) with documented technique usage. Each group profile includes aliases, targeted sectors, associated campaigns, software used, and technique mappings with procedure-level detail.\n\n### ATT&CK Navigator\n\nThe ATT&CK Navigator is a web-based tool for creating custom ATT&CK matrix visualizations. Analysts create layers (JSON files) that annotate techniques with scores, colors, comments, and metadata to visualize threat actor coverage, detection capabilities, or risk assessments.\n\n## Workflow\n\n### Step 1: Query ATT&CK Data Programmatically\n\n```python\nfrom attackcti import attack_client\nimport json\n\n# Initialize ATT&CK client (queries MITRE TAXII server)\nlift = attack_client()\n\n# Get all Enterprise techniques\nenterprise_techniques = lift.get_enterprise_techniques()\nprint(f\"Total Enterprise techniques: {len(enterprise_techniques)}\")\n\n# Get all threat groups\ngroups = lift.get_groups()\nprint(f\"Total threat groups: {len(groups)}\")\n\n# Get specific group by name\napt29 = [g for g in groups if 'APT29' in g.get('name', '')]\nif apt29:\n    group = apt29[0]\n    print(f\"Group: {group['name']}\")\n    print(f\"Aliases: {group.get('aliases', [])}\")\n    print(f\"Description: {group.get('description', '')[:200]}\")\n```\n\n### Step 2: Map Threat Actor to ATT&CK Techniques\n\n```python\nfrom attackcti import attack_client\n\nlift = attack_client()\n\n# Get techniques used by APT29\napt29_techniques = lift.get_techniques_used_by_group(\"G0016\")  # APT29 group ID\n\ntechnique_map = {}\nfor entry in apt29_techniques:\n    tech_id = entry.get(\"external_references\", [{}])[0].get(\"external_id\", \"\")\n    tech_name = entry.get(\"name\", \"\")\n    description = entry.get(\"description\", \"\")\n    tactic_refs = [\n        phase.get(\"phase_name\", \"\")\n        for phase in entry.get(\"kill_chain_phases\", [])\n    ]\n\n    technique_map[tech_id] = {\n        \"name\": tech_name,\n        \"tactics\": tactic_refs,\n        \"description\": description[:300],\n    }\n\nprint(f\"\\nAPT29 uses {len(technique_map)} techniques:\")\nfor tid, info in sorted(technique_map.items()):\n    print(f\"  {tid}: {info['name']} [{', '.join(info['tactics'])}]\")\n```\n\n### Step 3: Generate ATT&CK Navigator Layer\n\n```python\nimport json\n\ndef create_navigator_layer(group_name, technique_map, description=\"\"):\n    \"\"\"Generate ATT&CK Navigator layer JSON for a threat group.\"\"\"\n    techniques_list = []\n    for tech_id, info in technique_map.items():\n        techniques_list.append({\n            \"techniqueID\": tech_id,\n            \"tactic\": info[\"tactics\"][0] if info[\"tactics\"] else \"\",\n            \"color\": \"#ff6666\",  # Red for observed techniques\n            \"comment\": info[\"description\"][:200],\n            \"enabled\": True,\n            \"score\": 100,\n            \"metadata\": [\n                {\"name\": \"group\", \"value\": group_name},\n            ],\n        })\n\n    layer = {\n        \"name\": f\"{group_name} TTP Coverage\",\n        \"versions\": {\n            \"attack\": \"16.1\",\n            \"navigator\": \"5.1.0\",\n            \"layer\": \"4.5\",\n        },\n        \"domain\": \"enterprise-attack\",\n        \"description\": description or f\"Techniques attributed to {group_name}\",\n        \"filters\": {\"platforms\": [\"Windows\", \"Linux\", \"macOS\", \"Cloud\"]},\n        \"sorting\": 0,\n        \"layout\": {\n            \"layout\": \"side\",\n            \"aggregateFunction\": \"average\",\n            \"showID\": True,\n            \"showName\": True,\n            \"showAggregateScores\": False,\n            \"countUnscored\": False,\n        },\n        \"hideDisabled\": False,\n        \"techniques\": techniques_list,\n        \"gradient\": {\n            \"colors\": [\"#ffffff\", \"#ff6666\"],\n            \"minValue\": 0,\n            \"maxValue\": 100,\n        },\n        \"legendItems\": [\n            {\"label\": \"Observed technique\", \"color\": \"#ff6666\"},\n            {\"label\": \"Not observed\", \"color\": \"#ffffff\"},\n        ],\n        \"showTacticRowBackground\": True,\n        \"tacticRowBackground\": \"#dddddd\",\n        \"selectTechniquesAcrossTactics\": True,\n        \"selectSubtechniquesWithParent\": False,\n        \"selectVisibleTechniques\": False,\n    }\n\n    return layer\n\n\n# Generate and save layer\nlayer = create_navigator_layer(\"APT29\", technique_map, \"APT29 (Cozy Bear) TTP analysis\")\nwith open(\"apt29_navigator_layer.json\", \"w\") as f:\n    json.dump(layer, f, indent=2)\nprint(\"[+] Navigator layer saved to apt29_navigator_layer.json\")\n```\n\n### Step 4: Identify Detection Gaps\n\n```python\nfrom attackcti import attack_client\n\nlift = attack_client()\n\n# Get all techniques with data sources\nall_techniques = lift.get_enterprise_techniques()\n\n# Build data source coverage map\ndata_source_coverage = {}\nfor tech in all_techniques:\n    tech_id = tech.get(\"external_references\", [{}])[0].get(\"external_id\", \"\")\n    data_sources = tech.get(\"x_mitre_data_sources\", [])\n\n    for ds in data_sources:\n        if ds not in data_source_coverage:\n            data_source_coverage[ds] = []\n        data_source_coverage[ds].append(tech_id)\n\n# Compare threat actor techniques against available detections\ndetected_techniques = {\"T1059\", \"T1071\", \"T1566\"}  # Example: techniques you can detect\nactor_techniques = set(technique_map.keys())\n\ncovered = actor_techniques.intersection(detected_techniques)\ngaps = actor_techniques - detected_techniques\n\nprint(f\"\\n=== Detection Gap Analysis for APT29 ===\")\nprint(f\"Actor techniques: {len(actor_techniques)}\")\nprint(f\"Detected: {len(covered)} ({len(covered)\u002Flen(actor_techniques)*100:.0f}%)\")\nprint(f\"Gaps: {len(gaps)} ({len(gaps)\u002Flen(actor_techniques)*100:.0f}%)\")\nprint(f\"\\nUndetected techniques:\")\nfor tech_id in sorted(gaps):\n    if tech_id in technique_map:\n        print(f\"  {tech_id}: {technique_map[tech_id]['name']}\")\n```\n\n### Step 5: Cross-Group Technique Comparison\n\n```python\nfrom attackcti import attack_client\n\nlift = attack_client()\n\n# Compare techniques across multiple groups\ngroups_to_compare = {\n    \"G0016\": \"APT29\",\n    \"G0007\": \"APT28\",\n    \"G0032\": \"Lazarus Group\",\n}\n\ngroup_techniques = {}\nfor gid, gname in groups_to_compare.items():\n    techs = lift.get_techniques_used_by_group(gid)\n    tech_ids = set()\n    for t in techs:\n        tid = t.get(\"external_references\", [{}])[0].get(\"external_id\", \"\")\n        if tid:\n            tech_ids.add(tid)\n    group_techniques[gname] = tech_ids\n\n# Find common and unique techniques\nall_groups = list(group_techniques.keys())\ncommon_to_all = set.intersection(*group_techniques.values())\nprint(f\"\\nTechniques common to all {len(all_groups)} groups: {len(common_to_all)}\")\nfor tid in sorted(common_to_all):\n    print(f\"  {tid}\")\n\nfor gname, techs in group_techniques.items():\n    unique = techs - set.union(*[t for n, t in group_techniques.items() if n != gname])\n    print(f\"\\nUnique to {gname}: {len(unique)} techniques\")\n```\n\n## Validation Criteria\n\n- ATT&CK data successfully queried via TAXII server or local copy\n- Threat actor mapped to specific techniques with procedure examples\n- ATT&CK Navigator layer JSON is valid and renders correctly\n- Detection gap analysis identifies unmonitored techniques\n- Cross-group comparison reveals shared and unique TTPs\n- Output is actionable for detection engineering prioritization\n\n## References\n\n- [MITRE ATT&CK](https:\u002F\u002Fattack.mitre.org\u002F)\n- [ATT&CK Navigator](https:\u002F\u002Fmitre-attack.github.io\u002Fattack-navigator\u002F)\n- [attackcti Python Library](https:\u002F\u002Fgithub.com\u002FOTRF\u002FATTACK-Python-Client)\n- [ATT&CK STIX Data](https:\u002F\u002Fgithub.com\u002Fmitre\u002Fcti)\n- [ATT&CK Groups](https:\u002F\u002Fattack.mitre.org\u002Fgroups\u002F)\n",{"name":5,"tags":49,"author":50,"domain":35,"license":15,"version":51,"nist_csf":52,"subdomain":34,"description":7,"mitre_attack":57,"d3fend_techniques":62},[34,33,32,37,31,27,28],"mahipal","1.0",[53,54,55,56],"ID.RA-01","ID.RA-05","DE.CM-01","DE.AE-02",[58,29,59,60,61],"T1566.001","T1071.001","T1547.001","T1053.005",[63,64,65,66,67],"Executable Denylisting","Execution Isolation","File Metadata Consistency Validation","Content Format Conversion","File Content Analysis","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fanalyzing-threat-actor-ttps-with-mitre-attack",4414,"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":79,"prompt":89,"zipAvailable":90},[80,83,86],{"tool":81,"command":82},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-threat-actor-ttps-with-mitre-attack",{"tool":84,"command":85},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-threat-actor-ttps-with-mitre-attack",{"tool":87,"command":88},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-threat-actor-ttps-with-mitre-attack","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-threat-actor-ttps-with-mitre-attack\u002FSKILL.md",true,[92,98,103,109,113,117,123],{"path":93,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":96,"sourceHtmlUrl":97},"assets\u002Ftemplate.md","ASSET","资产文件",2234,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-threat-actor-ttps-with-mitre-attack\u002Fassets\u002Ftemplate.md",{"path":99,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":102},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-threat-actor-ttps-with-mitre-attack\u002FLICENSE",{"path":104,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fapi-reference.md","REFERENCE","参考文档",2102,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-threat-actor-ttps-with-mitre-attack\u002Freferences\u002Fapi-reference.md",{"path":110,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002Fstandards.md",3299,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-threat-actor-ttps-with-mitre-attack\u002Freferences\u002Fstandards.md",{"path":114,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":115,"sourceHtmlUrl":116},"references\u002Fworkflows.md",4817,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-threat-actor-ttps-with-mitre-attack\u002Freferences\u002Fworkflows.md",{"path":118,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":121,"sourceHtmlUrl":122},"scripts\u002Fagent.py","SCRIPT","脚本",5176,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-threat-actor-ttps-with-mitre-attack\u002Fscripts\u002Fagent.py",{"path":124,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":125,"sourceHtmlUrl":126},"scripts\u002Fprocess.py",12390,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-threat-actor-ttps-with-mitre-attack\u002Fscripts\u002Fprocess.py",[128,155,179,206,232,259],{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":135,"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",[134],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[136,137,138,139,140,35,141,142,143,144,145,146,147,148,149,150,151,152,153,154],"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":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":162,"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",[161],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[163,151,164,165,140,166,35,143,167,168,169,170,171,172,173,174,175,176,177,178],"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":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":189,"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",[185,188],{"categoryNo":186,"categoryName":187,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[173,190,191,192,193,35,194,42,167,195,196,197,198,199,200,201,202,203,204,205],"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":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":210,"categories":211,"keywords":213,"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",[212],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[214,215,216,35,217,218,219,220,221,222,223,224,225,226,227,228,170,229,230,231],"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":233,"name":234,"nameZh":6,"description":235,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":236,"categories":237,"keywords":239,"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",[238],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[240,241,242,35,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258],"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":260,"name":261,"nameZh":6,"description":262,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":263,"categories":264,"keywords":266,"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",[265],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[140,267,35,268,38,32,269,270,271,272,273,201,274,202,275,276,277,278,279,280],"triaging security incident","siem","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]