[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73EV9NFFRRPEB0E85H8KYYR":3,"skill-same:01M73EV9NFFRRPEB0E85H8KYYR":89},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":19,"publishedAt":20,"linkOnly":21,"markdownContent":22,"markdownBody":23,"frontMatter":24,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M73EV9NFFRRPEB0E85H8KYYR","performing-privacy-impact-assessment",null,"Automates the Privacy Impact Assessment (PIA) workflow including data flow mapping, privacy risk scoring matrices, GDPR Article 35 DPIA and CCPA\u002FCPRA alignment checks, data inventory cataloging, and remediation tracking. Implements the NIST Privacy Framework PRAM methodology and ICO DPIA guidance for systematic identification and mitigation of privacy risks across processing activities. Use when conducting privacy assessments for new systems, evaluating regulatory compliance posture, or building automated privacy governance programs.","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fperforming-privacy-impact-assessment\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fperforming-privacy-impact-assessment\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: performing-privacy-impact-assessment\ndescription: 'Automates the Privacy Impact Assessment (PIA) workflow including data\n  flow mapping, privacy risk scoring matrices, GDPR Article 35 DPIA and CCPA\u002FCPRA\n  alignment checks, data inventory cataloging, and remediation tracking. Implements\n  the NIST Privacy Framework PRAM methodology and ICO DPIA guidance for systematic\n  identification and mitigation of privacy risks across processing activities. Use\n  when conducting privacy assessments for new systems, evaluating regulatory compliance\n  posture, or building automated privacy governance programs.\n\n  '\ndomain: cybersecurity\nsubdomain: privacy-compliance\ntags:\n- privacy\n- impact-assessment\n- GDPR\n- CCPA\n- NIST\n- DPIA\n- data-flow-mapping\n- risk-scoring\nversion: '1.0'\nauthor: mukul975\nlicense: Apache-2.0\nnist_csf:\n- GV.PO-01\n- PR.DS-01\n- GV.OC-05\nmitre_attack:\n- T1078\n- T1190\n- T1059\nsource: https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\nsource_commit: 04450304b12645cb2b974ab96d28c0664758a88d\n---\n\n# Performing Privacy Impact Assessment\n\n## When to Use\n\n- When launching a new system, product, or processing activity that handles personal data\n- When conducting GDPR Article 35 Data Protection Impact Assessments (DPIAs)\n- When evaluating CCPA\u002FCPRA compliance for data processing operations\n- When performing privacy risk assessments aligned to the NIST Privacy Framework\n- When mapping data flows across organizational boundaries and third-party processors\n- When building automated privacy governance and assessment pipelines\n- When preparing for regulatory audits or demonstrating accountability obligations\n\n## Prerequisites\n\n- Familiarity with GDPR, CCPA\u002FCPRA, and NIST Privacy Framework concepts\n- Access to data processing inventories and system architecture documentation\n- Python 3.8+ with required dependencies installed\n- Appropriate authorization from the Data Protection Officer (DPO) or privacy team\n- Knowledge of organizational data flows and third-party processor relationships\n\n## Instructions\n\n### Phase 1: Data Inventory and Processing Activity Catalog\n\nBuild a complete inventory of personal data processing activities. Each record of\nprocessing activity (ROPA) entry must capture the data categories, legal basis,\nretention periods, and data subjects involved.\n\n```python\nfrom agent import PrivacyImpactAssessmentEngine\n\nengine = PrivacyImpactAssessmentEngine()\n\n# Register a processing activity for assessment\nactivity = engine.register_processing_activity(\n    name=\"Customer Analytics Platform\",\n    description=\"Collects browsing behavior and purchase history for personalization\",\n    data_controller=\"Acme Corp\",\n    data_processor=\"CloudAnalytics Inc\",\n    data_categories=[\"browsing_history\", \"purchase_records\", \"ip_address\", \"device_id\"],\n    data_subjects=[\"customers\", \"website_visitors\"],\n    legal_basis=\"consent\",\n    retention_period_days=730,\n    cross_border_transfer=True,\n    transfer_destinations=[\"US\", \"IN\"],\n    automated_decision_making=True,\n)\nprint(f\"Registered activity: {activity['activity_id']}\")\n```\n\n### Phase 2: Data Flow Mapping\n\nMap all data flows from collection to deletion, identifying every touchpoint,\ntransformation, and storage location. This reveals hidden privacy risks in data\nmovement across systems.\n\n```python\n# Build the data flow map\nflow_map = engine.map_data_flows(\n    activity_id=activity[\"activity_id\"],\n    flows=[\n        {\n            \"stage\": \"collection\",\n            \"source\": \"Web browser cookie + form submission\",\n            \"destination\": \"CDN edge server\",\n            \"data_elements\": [\"ip_address\", \"device_id\", \"browsing_history\"],\n            \"encryption_in_transit\": True,\n            \"protocol\": \"TLS 1.3\",\n        },\n        {\n            \"stage\": \"processing\",\n            \"source\": \"CDN edge server\",\n            \"destination\": \"Analytics data warehouse (US-East)\",\n            \"data_elements\": [\"browsing_history\", \"purchase_records\", \"device_id\"],\n            \"encryption_in_transit\": True,\n            \"encryption_at_rest\": True,\n            \"protocol\": \"mTLS\",\n        },\n        {\n            \"stage\": \"storage\",\n            \"source\": \"Analytics data warehouse\",\n            \"destination\": \"S3 encrypted bucket\",\n            \"data_elements\": [\"browsing_history\", \"purchase_records\"],\n            \"encryption_at_rest\": True,\n            \"retention_days\": 730,\n            \"access_controls\": \"IAM role-based, MFA required\",\n        },\n        {\n            \"stage\": \"sharing\",\n            \"source\": \"Analytics data warehouse\",\n            \"destination\": \"Third-party ML provider (IN)\",\n            \"data_elements\": [\"browsing_history\", \"purchase_records\"],\n            \"encryption_in_transit\": True,\n            \"data_processing_agreement\": True,\n            \"cross_border\": True,\n        },\n        {\n            \"stage\": \"deletion\",\n            \"source\": \"S3 bucket + data warehouse\",\n            \"destination\": \"Secure erasure\",\n            \"method\": \"Cryptographic erasure + lifecycle policy\",\n            \"verification\": \"Automated deletion audit log\",\n        },\n    ],\n)\nengine.render_data_flow_diagram(flow_map)\n```\n\n### Phase 3: Privacy Risk Assessment with Scoring Matrix\n\nApply a structured risk scoring methodology evaluating likelihood and impact\nacross multiple privacy risk dimensions. The matrix aligns with both the\nNIST PRAM and ICO DPIA risk assessment approaches.\n\n```python\n# Run the risk assessment\nrisk_report = engine.assess_privacy_risks(\n    activity_id=activity[\"activity_id\"],\n    assessment_type=\"full_dpia\",\n)\n\n# Display risk matrix results\nfor risk in risk_report[\"risks\"]:\n    print(f\"[{risk['severity']}] {risk['category']}: {risk['description']}\")\n    print(f\"  Likelihood: {risk['likelihood']}\u002F5 | Impact: {risk['impact']}\u002F5 | Score: {risk['risk_score']}\u002F25\")\n    print(f\"  Mitigation: {risk['recommended_mitigation']}\")\n```\n\nRisk categories evaluated include:\n1. **Data Minimization** -- Excessive collection beyond stated purpose\n2. **Purpose Limitation** -- Secondary use without legal basis\n3. **Cross-Border Transfer** -- Transfers without adequate safeguards (SCCs, BCRs)\n4. **Automated Decision Making** -- Profiling without human oversight or appeal\n5. **Data Subject Rights** -- Inability to fulfill access\u002Ferasure\u002Fportability requests\n6. **Third-Party Risk** -- Processor compliance gaps, subprocessor chains\n7. **Security Controls** -- Encryption, access control, breach response gaps\n8. **Retention** -- Storing data beyond necessity or legal requirement\n9. **Consent Management** -- Invalid or ambiguous consent mechanisms\n10. **Breach Notification** -- Inability to detect and notify within 72 hours (GDPR)\n\n### Phase 4: GDPR and CCPA\u002FCPRA Alignment Checks\n\nRun automated compliance checks against specific regulatory requirements.\nThe engine maps each processing activity against article-level GDPR obligations\nand CCPA\u002FCPRA consumer rights requirements.\n\n```python\n# GDPR compliance check\ngdpr_report = engine.check_gdpr_compliance(activity_id=activity[\"activity_id\"])\nprint(f\"GDPR Score: {gdpr_report['compliance_score']}\u002F100\")\nfor finding in gdpr_report[\"findings\"]:\n    print(f\"  [{finding['status']}] Art.{finding['article']}: {finding['description']}\")\n\n# CCPA\u002FCPRA compliance check\nccpa_report = engine.check_ccpa_compliance(activity_id=activity[\"activity_id\"])\nprint(f\"CCPA Score: {ccpa_report['compliance_score']}\u002F100\")\nfor finding in ccpa_report[\"findings\"]:\n    print(f\"  [{finding['status']}] Sec.{finding['section']}: {finding['description']}\")\n```\n\n### Phase 5: Remediation Plan and Report Generation\n\nGenerate a prioritized remediation plan with specific action items, responsible\nparties, deadlines, and generate the formal PIA\u002FDPIA report document.\n\n```python\n# Generate remediation plan\nremediation = engine.generate_remediation_plan(\n    activity_id=activity[\"activity_id\"],\n    risk_report=risk_report,\n    gdpr_report=gdpr_report,\n    ccpa_report=ccpa_report,\n)\n\nfor item in remediation[\"action_items\"]:\n    print(f\"[{item['priority']}] {item['action']}\")\n    print(f\"  Owner: {item['owner']} | Deadline: {item['deadline']}\")\n    print(f\"  Addresses: {', '.join(item['addresses_risks'])}\")\n\n# Generate formal DPIA report\nengine.generate_dpia_report(\n    activity_id=activity[\"activity_id\"],\n    output_path=\"dpia_report_customer_analytics.json\",\n    format=\"json\",\n)\nprint(\"[+] DPIA report generated\")\n```\n\n## Examples\n\n### Quick Screening Assessment\n\nDetermine whether a full DPIA is required using the ICO screening checklist:\n\n```python\nengine = PrivacyImpactAssessmentEngine()\n\nscreening = engine.run_screening_checklist(\n    uses_special_category_data=False,\n    large_scale_processing=True,\n    systematic_monitoring=True,\n    automated_decision_making=True,\n    cross_border_transfer=True,\n    vulnerable_data_subjects=False,\n    innovative_technology=True,\n    denial_of_service_or_rights=False,\n)\nprint(f\"DPIA Required: {screening['dpia_required']}\")\nprint(f\"Triggers: {screening['triggers']}\")\n# Output: DPIA Required: True\n# Triggers: ['large_scale_processing', 'systematic_monitoring',\n#            'automated_decision_making', 'cross_border_transfer',\n#            'innovative_technology']\n```\n\n### Batch Assessment of Multiple Processing Activities\n\n```python\nengine = PrivacyImpactAssessmentEngine()\n\nactivities = [\n    {\"name\": \"Email Marketing\", \"data_categories\": [\"email\", \"name\"],\n     \"legal_basis\": \"consent\", \"cross_border_transfer\": False},\n    {\"name\": \"HR Analytics\", \"data_categories\": [\"employee_id\", \"performance_scores\",\n     \"health_data\"], \"legal_basis\": \"legitimate_interest\", \"cross_border_transfer\": True},\n    {\"name\": \"Fraud Detection\", \"data_categories\": [\"transaction_data\", \"ip_address\",\n     \"device_fingerprint\"], \"legal_basis\": \"legitimate_interest\",\n     \"automated_decision_making\": True, \"cross_border_transfer\": False},\n]\n\nfor act_def in activities:\n    activity = engine.register_processing_activity(**act_def)\n    risk = engine.assess_privacy_risks(activity_id=activity[\"activity_id\"])\n    print(f\"{act_def['name']}: Overall Risk={risk['overall_risk_level']} \"\n          f\"({risk['risk_count_by_severity']})\")\n```\n\n### NIST Privacy Framework Profile Mapping\n\n```python\nengine = PrivacyImpactAssessmentEngine()\n\nprofile = engine.generate_nist_privacy_profile(\n    activity_id=activity[\"activity_id\"],\n    target_tier=\"tier_3\",  # Repeatable\n)\n\nfor function_id, outcomes in profile[\"functions\"].items():\n    print(f\"\\n{function_id}:\")\n    for outcome in outcomes:\n        status = \"PASS\" if outcome[\"implemented\"] else \"GAP\"\n        print(f\"  [{status}] {outcome['subcategory']}: {outcome['description']}\")\n```\n","# Performing Privacy Impact Assessment\n\n## When to Use\n\n- When launching a new system, product, or processing activity that handles personal data\n- When conducting GDPR Article 35 Data Protection Impact Assessments (DPIAs)\n- When evaluating CCPA\u002FCPRA compliance for data processing operations\n- When performing privacy risk assessments aligned to the NIST Privacy Framework\n- When mapping data flows across organizational boundaries and third-party processors\n- When building automated privacy governance and assessment pipelines\n- When preparing for regulatory audits or demonstrating accountability obligations\n\n## Prerequisites\n\n- Familiarity with GDPR, CCPA\u002FCPRA, and NIST Privacy Framework concepts\n- Access to data processing inventories and system architecture documentation\n- Python 3.8+ with required dependencies installed\n- Appropriate authorization from the Data Protection Officer (DPO) or privacy team\n- Knowledge of organizational data flows and third-party processor relationships\n\n## Instructions\n\n### Phase 1: Data Inventory and Processing Activity Catalog\n\nBuild a complete inventory of personal data processing activities. Each record of\nprocessing activity (ROPA) entry must capture the data categories, legal basis,\nretention periods, and data subjects involved.\n\n```python\nfrom agent import PrivacyImpactAssessmentEngine\n\nengine = PrivacyImpactAssessmentEngine()\n\n# Register a processing activity for assessment\nactivity = engine.register_processing_activity(\n    name=\"Customer Analytics Platform\",\n    description=\"Collects browsing behavior and purchase history for personalization\",\n    data_controller=\"Acme Corp\",\n    data_processor=\"CloudAnalytics Inc\",\n    data_categories=[\"browsing_history\", \"purchase_records\", \"ip_address\", \"device_id\"],\n    data_subjects=[\"customers\", \"website_visitors\"],\n    legal_basis=\"consent\",\n    retention_period_days=730,\n    cross_border_transfer=True,\n    transfer_destinations=[\"US\", \"IN\"],\n    automated_decision_making=True,\n)\nprint(f\"Registered activity: {activity['activity_id']}\")\n```\n\n### Phase 2: Data Flow Mapping\n\nMap all data flows from collection to deletion, identifying every touchpoint,\ntransformation, and storage location. This reveals hidden privacy risks in data\nmovement across systems.\n\n```python\n# Build the data flow map\nflow_map = engine.map_data_flows(\n    activity_id=activity[\"activity_id\"],\n    flows=[\n        {\n            \"stage\": \"collection\",\n            \"source\": \"Web browser cookie + form submission\",\n            \"destination\": \"CDN edge server\",\n            \"data_elements\": [\"ip_address\", \"device_id\", \"browsing_history\"],\n            \"encryption_in_transit\": True,\n            \"protocol\": \"TLS 1.3\",\n        },\n        {\n            \"stage\": \"processing\",\n            \"source\": \"CDN edge server\",\n            \"destination\": \"Analytics data warehouse (US-East)\",\n            \"data_elements\": [\"browsing_history\", \"purchase_records\", \"device_id\"],\n            \"encryption_in_transit\": True,\n            \"encryption_at_rest\": True,\n            \"protocol\": \"mTLS\",\n        },\n        {\n            \"stage\": \"storage\",\n            \"source\": \"Analytics data warehouse\",\n            \"destination\": \"S3 encrypted bucket\",\n            \"data_elements\": [\"browsing_history\", \"purchase_records\"],\n            \"encryption_at_rest\": True,\n            \"retention_days\": 730,\n            \"access_controls\": \"IAM role-based, MFA required\",\n        },\n        {\n            \"stage\": \"sharing\",\n            \"source\": \"Analytics data warehouse\",\n            \"destination\": \"Third-party ML provider (IN)\",\n            \"data_elements\": [\"browsing_history\", \"purchase_records\"],\n            \"encryption_in_transit\": True,\n            \"data_processing_agreement\": True,\n            \"cross_border\": True,\n        },\n        {\n            \"stage\": \"deletion\",\n            \"source\": \"S3 bucket + data warehouse\",\n            \"destination\": \"Secure erasure\",\n            \"method\": \"Cryptographic erasure + lifecycle policy\",\n            \"verification\": \"Automated deletion audit log\",\n        },\n    ],\n)\nengine.render_data_flow_diagram(flow_map)\n```\n\n### Phase 3: Privacy Risk Assessment with Scoring Matrix\n\nApply a structured risk scoring methodology evaluating likelihood and impact\nacross multiple privacy risk dimensions. The matrix aligns with both the\nNIST PRAM and ICO DPIA risk assessment approaches.\n\n```python\n# Run the risk assessment\nrisk_report = engine.assess_privacy_risks(\n    activity_id=activity[\"activity_id\"],\n    assessment_type=\"full_dpia\",\n)\n\n# Display risk matrix results\nfor risk in risk_report[\"risks\"]:\n    print(f\"[{risk['severity']}] {risk['category']}: {risk['description']}\")\n    print(f\"  Likelihood: {risk['likelihood']}\u002F5 | Impact: {risk['impact']}\u002F5 | Score: {risk['risk_score']}\u002F25\")\n    print(f\"  Mitigation: {risk['recommended_mitigation']}\")\n```\n\nRisk categories evaluated include:\n1. **Data Minimization** -- Excessive collection beyond stated purpose\n2. **Purpose Limitation** -- Secondary use without legal basis\n3. **Cross-Border Transfer** -- Transfers without adequate safeguards (SCCs, BCRs)\n4. **Automated Decision Making** -- Profiling without human oversight or appeal\n5. **Data Subject Rights** -- Inability to fulfill access\u002Ferasure\u002Fportability requests\n6. **Third-Party Risk** -- Processor compliance gaps, subprocessor chains\n7. **Security Controls** -- Encryption, access control, breach response gaps\n8. **Retention** -- Storing data beyond necessity or legal requirement\n9. **Consent Management** -- Invalid or ambiguous consent mechanisms\n10. **Breach Notification** -- Inability to detect and notify within 72 hours (GDPR)\n\n### Phase 4: GDPR and CCPA\u002FCPRA Alignment Checks\n\nRun automated compliance checks against specific regulatory requirements.\nThe engine maps each processing activity against article-level GDPR obligations\nand CCPA\u002FCPRA consumer rights requirements.\n\n```python\n# GDPR compliance check\ngdpr_report = engine.check_gdpr_compliance(activity_id=activity[\"activity_id\"])\nprint(f\"GDPR Score: {gdpr_report['compliance_score']}\u002F100\")\nfor finding in gdpr_report[\"findings\"]:\n    print(f\"  [{finding['status']}] Art.{finding['article']}: {finding['description']}\")\n\n# CCPA\u002FCPRA compliance check\nccpa_report = engine.check_ccpa_compliance(activity_id=activity[\"activity_id\"])\nprint(f\"CCPA Score: {ccpa_report['compliance_score']}\u002F100\")\nfor finding in ccpa_report[\"findings\"]:\n    print(f\"  [{finding['status']}] Sec.{finding['section']}: {finding['description']}\")\n```\n\n### Phase 5: Remediation Plan and Report Generation\n\nGenerate a prioritized remediation plan with specific action items, responsible\nparties, deadlines, and generate the formal PIA\u002FDPIA report document.\n\n```python\n# Generate remediation plan\nremediation = engine.generate_remediation_plan(\n    activity_id=activity[\"activity_id\"],\n    risk_report=risk_report,\n    gdpr_report=gdpr_report,\n    ccpa_report=ccpa_report,\n)\n\nfor item in remediation[\"action_items\"]:\n    print(f\"[{item['priority']}] {item['action']}\")\n    print(f\"  Owner: {item['owner']} | Deadline: {item['deadline']}\")\n    print(f\"  Addresses: {', '.join(item['addresses_risks'])}\")\n\n# Generate formal DPIA report\nengine.generate_dpia_report(\n    activity_id=activity[\"activity_id\"],\n    output_path=\"dpia_report_customer_analytics.json\",\n    format=\"json\",\n)\nprint(\"[+] DPIA report generated\")\n```\n\n## Examples\n\n### Quick Screening Assessment\n\nDetermine whether a full DPIA is required using the ICO screening checklist:\n\n```python\nengine = PrivacyImpactAssessmentEngine()\n\nscreening = engine.run_screening_checklist(\n    uses_special_category_data=False,\n    large_scale_processing=True,\n    systematic_monitoring=True,\n    automated_decision_making=True,\n    cross_border_transfer=True,\n    vulnerable_data_subjects=False,\n    innovative_technology=True,\n    denial_of_service_or_rights=False,\n)\nprint(f\"DPIA Required: {screening['dpia_required']}\")\nprint(f\"Triggers: {screening['triggers']}\")\n# Output: DPIA Required: True\n# Triggers: ['large_scale_processing', 'systematic_monitoring',\n#            'automated_decision_making', 'cross_border_transfer',\n#            'innovative_technology']\n```\n\n### Batch Assessment of Multiple Processing Activities\n\n```python\nengine = PrivacyImpactAssessmentEngine()\n\nactivities = [\n    {\"name\": \"Email Marketing\", \"data_categories\": [\"email\", \"name\"],\n     \"legal_basis\": \"consent\", \"cross_border_transfer\": False},\n    {\"name\": \"HR Analytics\", \"data_categories\": [\"employee_id\", \"performance_scores\",\n     \"health_data\"], \"legal_basis\": \"legitimate_interest\", \"cross_border_transfer\": True},\n    {\"name\": \"Fraud Detection\", \"data_categories\": [\"transaction_data\", \"ip_address\",\n     \"device_fingerprint\"], \"legal_basis\": \"legitimate_interest\",\n     \"automated_decision_making\": True, \"cross_border_transfer\": False},\n]\n\nfor act_def in activities:\n    activity = engine.register_processing_activity(**act_def)\n    risk = engine.assess_privacy_risks(activity_id=activity[\"activity_id\"])\n    print(f\"{act_def['name']}: Overall Risk={risk['overall_risk_level']} \"\n          f\"({risk['risk_count_by_severity']})\")\n```\n\n### NIST Privacy Framework Profile Mapping\n\n```python\nengine = PrivacyImpactAssessmentEngine()\n\nprofile = engine.generate_nist_privacy_profile(\n    activity_id=activity[\"activity_id\"],\n    target_tier=\"tier_3\",  # Repeatable\n)\n\nfor function_id, outcomes in profile[\"functions\"].items():\n    print(f\"\\n{function_id}:\")\n    for outcome in outcomes:\n        status = \"PASS\" if outcome[\"implemented\"] else \"GAP\"\n        print(f\"  [{status}] {outcome['subcategory']}: {outcome['description']}\")\n```\n",{"name":5,"tags":25,"author":34,"domain":35,"source":36,"license":15,"version":37,"nist_csf":38,"subdomain":42,"description":43,"mitre_attack":44,"source_commit":48},[26,27,28,29,30,31,32,33],"privacy","impact-assessment","GDPR","CCPA","NIST","DPIA","data-flow-mapping","risk-scoring","mukul975","cybersecurity","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","1.0",[39,40,41],"GV.PO-01","PR.DS-01","GV.OC-05","privacy-compliance","Automates the Privacy Impact Assessment (PIA) workflow including data flow mapping, privacy risk scoring matrices, GDPR Article 35 DPIA and CCPA\u002FCPRA alignment checks, data inventory cataloging, and remediation tracking. Implements the NIST Privacy Framework PRAM methodology and ICO DPIA guidance for systematic identification and mitigation of privacy risks across processing activities. Use when conducting privacy assessments for new systems, evaluating regulatory compliance posture, or building automated privacy governance programs.\n",[45,46,47],"T1078","T1190","T1059","04450304b12645cb2b974ab96d28c0664758a88d","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Fperforming-privacy-impact-assessment",1,"https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI","https:\u002F\u002Fyana.vutam.link\u002F","https:\u002F\u002Fgithub.com\u002FHoronomos-laps","https:\u002F\u002Fyana.vutam.link","An open-source governed runtime & execution environment for multi-agent AI systems.","2026-11-11 23:56:26",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill performing-privacy-impact-assessment",{"tool":65,"command":66},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill performing-privacy-impact-assessment",{"tool":68,"command":69},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill performing-privacy-impact-assessment","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fperforming-privacy-impact-assessment\u002FSKILL.md",true,[73,77,83],{"path":74,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":16},"LICENSE","许可证",11255,{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fapi-reference.md","REFERENCE","参考文档",7597,"https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fperforming-privacy-impact-assessment\u002Freferences\u002Fapi-reference.md",{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"scripts\u002Fagent.py","SCRIPT","脚本",64215,"https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fperforming-privacy-impact-assessment\u002Fscripts\u002Fagent.py",[90,98,105,113,121,129],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":94,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":20},"01M735GKET5YVYKAY62W2P5YXB","openai--sentry--sentry","Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.","MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":20},"01M73DCAP83T17HY7SMGK7MGFE","ito-basket-compare","Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":109,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":20},"01M734CYEMZFDR3KH6SW0JP8TD","implementing-network-traffic-baselining","Build network traffic baselines from NetFlow\u002FIPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly\u002Fdaily traffic pattern profiling","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FSKILL.md",[],[],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":117,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":20},"01M73ASZ6NJRJM44Z2P84NGQ3Q","detecting-evasion-techniques-in-endpoint-logs","Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":125,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":20},"01M734AFNXD06Z78QRK0VKGT7Q","implementing-microsegmentation-with-guardicore","Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":130,"name":131,"nameZh":6,"description":6,"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":134,"publishedAt":20},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]