[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADM2Z32J4Q79JHWHB4WKPK":3,"skill-same:01M6ADM2Z32J4Q79JHWHB4WKPK":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":31,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":64,"commitSha":65,"skillDirectory":66,"forksCount":67,"repositoryHtmlUrl":68,"repositoryHomepageUrl":69,"ownerHtmlUrl":70,"ownerBlogUrl":71,"ownerDescription":72,"updatedAt":73,"install":74,"resources":87},"01M6ADM2Z32J4Q79JHWHB4WKPK","analyzing-campaign-attribution-evidence",null,"Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing\u002Flanguage artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.","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\u002Fanalyzing-campaign-attribution-evidence\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45],"campaign attribution","analyzing campaign attribution evidence","cybersecurity","infrastructure","code","artifacts","mitre-attack","threat-intelligence","malware","cti","ioc","stix","attribution","campaign-analysis","2026-11-10 10:12:30",false,"---\nname: analyzing-campaign-attribution-evidence\ndescription: Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing\u002Flanguage artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.\ndomain: cybersecurity\nsubdomain: threat-intelligence\ntags:\n- threat-intelligence\n- cti\n- ioc\n- mitre-attack\n- stix\n- attribution\n- campaign-analysis\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- ID.RA-01\n- ID.RA-05\n- DE.CM-01\n- DE.AE-02\nmitre_attack:\n- T1587.001\n- T1583.001\n- T1588.002\n- T1071.001\n---\n# Analyzing Campaign Attribution Evidence\n\n## Overview\n\nCampaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attribution indicators using the Diamond Model and ACH (Analysis of Competing Hypotheses), analyzing infrastructure overlaps, TTP consistency, malware code similarities, operational timing patterns, and language artifacts to build confidence-weighted attribution assessments.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing campaign attribution evidence\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 `attackcti`, `stix2`, `networkx` libraries\n- Access to threat intelligence platforms (MISP, OpenCTI)\n- Understanding of Diamond Model of Intrusion Analysis\n- Familiarity with MITRE ATT&CK threat group profiles\n- Knowledge of malware analysis and infrastructure tracking techniques\n\n## Key Concepts\n\n### Attribution Evidence Categories\n1. **Infrastructure Overlap**: Shared C2 servers, domains, IP ranges, hosting providers\n2. **TTP Consistency**: Matching ATT&CK techniques and sub-techniques across campaigns\n3. **Malware Code Similarity**: Shared code bases, compilers, PDB paths, encryption routines\n4. **Operational Patterns**: Timing (working hours, time zones), targeting patterns, operational tempo\n5. **Language Artifacts**: Embedded strings, variable names, error messages in specific languages\n6. **Victimology**: Target sector, geography, and organizational profile consistency\n\n### Confidence Levels\n- **High Confidence**: Multiple independent evidence categories converge on same actor\n- **Moderate Confidence**: Several evidence categories match, some ambiguity remains\n- **Low Confidence**: Limited evidence, possible false flags or shared tooling\n\n### Analysis of Competing Hypotheses (ACH)\nStructured analytical method that evaluates evidence against multiple competing hypotheses. Each piece of evidence is scored as consistent, inconsistent, or neutral with respect to each hypothesis. The hypothesis with the least inconsistent evidence is favored.\n\n## Workflow\n\n### Step 1: Collect Attribution Evidence\n\n```python\nfrom stix2 import MemoryStore, Filter\nfrom collections import defaultdict\n\nclass AttributionAnalyzer:\n    def __init__(self):\n        self.evidence = []\n        self.hypotheses = {}\n\n    def add_evidence(self, category, description, value, confidence):\n        self.evidence.append({\n            \"category\": category,\n            \"description\": description,\n            \"value\": value,\n            \"confidence\": confidence,\n            \"timestamp\": None,\n        })\n\n    def add_hypothesis(self, actor_name, actor_id=\"\"):\n        self.hypotheses[actor_name] = {\n            \"actor_id\": actor_id,\n            \"consistent_evidence\": [],\n            \"inconsistent_evidence\": [],\n            \"neutral_evidence\": [],\n            \"score\": 0,\n        }\n\n    def evaluate_evidence(self, evidence_idx, actor_name, assessment):\n        \"\"\"Assess evidence against a hypothesis: consistent\u002Finconsistent\u002Fneutral.\"\"\"\n        if assessment == \"consistent\":\n            self.hypotheses[actor_name][\"consistent_evidence\"].append(evidence_idx)\n            self.hypotheses[actor_name][\"score\"] += self.evidence[evidence_idx][\"confidence\"]\n        elif assessment == \"inconsistent\":\n            self.hypotheses[actor_name][\"inconsistent_evidence\"].append(evidence_idx)\n            self.hypotheses[actor_name][\"score\"] -= self.evidence[evidence_idx][\"confidence\"] * 2\n        else:\n            self.hypotheses[actor_name][\"neutral_evidence\"].append(evidence_idx)\n\n    def rank_hypotheses(self):\n        \"\"\"Rank hypotheses by attribution score.\"\"\"\n        ranked = sorted(\n            self.hypotheses.items(),\n            key=lambda x: x[1][\"score\"],\n            reverse=True,\n        )\n        return [\n            {\n                \"actor\": name,\n                \"score\": data[\"score\"],\n                \"consistent\": len(data[\"consistent_evidence\"]),\n                \"inconsistent\": len(data[\"inconsistent_evidence\"]),\n                \"confidence\": self._score_to_confidence(data[\"score\"]),\n            }\n            for name, data in ranked\n        ]\n\n    def _score_to_confidence(self, score):\n        if score >= 80:\n            return \"HIGH\"\n        elif score >= 40:\n            return \"MODERATE\"\n        else:\n            return \"LOW\"\n```\n\n### Step 2: Infrastructure Overlap Analysis\n\n```python\ndef analyze_infrastructure_overlap(campaign_a_infra, campaign_b_infra):\n    \"\"\"Compare infrastructure between two campaigns for attribution.\"\"\"\n    overlap = {\n        \"shared_ips\": set(campaign_a_infra.get(\"ips\", [])).intersection(\n            campaign_b_infra.get(\"ips\", [])\n        ),\n        \"shared_domains\": set(campaign_a_infra.get(\"domains\", [])).intersection(\n            campaign_b_infra.get(\"domains\", [])\n        ),\n        \"shared_asns\": set(campaign_a_infra.get(\"asns\", [])).intersection(\n            campaign_b_infra.get(\"asns\", [])\n        ),\n        \"shared_registrars\": set(campaign_a_infra.get(\"registrars\", [])).intersection(\n            campaign_b_infra.get(\"registrars\", [])\n        ),\n    }\n\n    overlap_score = 0\n    if overlap[\"shared_ips\"]:\n        overlap_score += 30\n    if overlap[\"shared_domains\"]:\n        overlap_score += 25\n    if overlap[\"shared_asns\"]:\n        overlap_score += 15\n    if overlap[\"shared_registrars\"]:\n        overlap_score += 10\n\n    return {\n        \"overlap\": {k: list(v) for k, v in overlap.items()},\n        \"overlap_score\": overlap_score,\n        \"assessment\": \"STRONG\" if overlap_score >= 40 else \"MODERATE\" if overlap_score >= 20 else \"WEAK\",\n    }\n```\n\n### Step 3: TTP Comparison Across Campaigns\n\n```python\nfrom attackcti import attack_client\n\ndef compare_campaign_ttps(campaign_techniques, known_actor_techniques):\n    \"\"\"Compare campaign TTPs against known threat actor profiles.\"\"\"\n    campaign_set = set(campaign_techniques)\n    actor_set = set(known_actor_techniques)\n\n    common = campaign_set.intersection(actor_set)\n    unique_campaign = campaign_set - actor_set\n    unique_actor = actor_set - campaign_set\n\n    jaccard = len(common) \u002F len(campaign_set.union(actor_set)) if campaign_set.union(actor_set) else 0\n\n    return {\n        \"common_techniques\": sorted(common),\n        \"common_count\": len(common),\n        \"unique_to_campaign\": sorted(unique_campaign),\n        \"unique_to_actor\": sorted(unique_actor),\n        \"jaccard_similarity\": round(jaccard, 3),\n        \"overlap_percentage\": round(len(common) \u002F len(campaign_set) * 100, 1) if campaign_set else 0,\n    }\n```\n\n### Step 4: Generate Attribution Report\n\n```python\ndef generate_attribution_report(analyzer):\n    \"\"\"Generate structured attribution assessment report.\"\"\"\n    rankings = analyzer.rank_hypotheses()\n\n    report = {\n        \"assessment_date\": \"2026-02-23\",\n        \"total_evidence_items\": len(analyzer.evidence),\n        \"hypotheses_evaluated\": len(analyzer.hypotheses),\n        \"rankings\": rankings,\n        \"primary_attribution\": rankings[0] if rankings else None,\n        \"evidence_summary\": [\n            {\n                \"index\": i,\n                \"category\": e[\"category\"],\n                \"description\": e[\"description\"],\n                \"confidence\": e[\"confidence\"],\n            }\n            for i, e in enumerate(analyzer.evidence)\n        ],\n    }\n\n    return report\n```\n\n## Validation Criteria\n\n- Evidence collection covers all six attribution categories\n- ACH matrix properly evaluates evidence against competing hypotheses\n- Infrastructure overlap analysis identifies shared indicators\n- TTP comparison uses ATT&CK technique IDs for precision\n- Attribution confidence levels are properly justified\n- Report includes alternative hypotheses and false flag considerations\n\n## References\n\n- [Diamond Model of Intrusion Analysis](https:\u002F\u002Fwww.activeresponse.org\u002Fwp-content\u002Fuploads\u002F2013\u002F07\u002Fdiamond.pdf)\n- [MITRE ATT&CK Groups](https:\u002F\u002Fattack.mitre.org\u002Fgroups\u002F)\n- [Analysis of Competing Hypotheses](https:\u002F\u002Fwww.cia.gov\u002Fstatic\u002F9a5f1162fd0932c29e985f0159f56c07\u002FTradecraft-Primer-apr09.pdf)\n- [Threat Attribution Framework](https:\u002F\u002Fwww.mandiant.com\u002Fresources\u002Freports)\n","# Analyzing Campaign Attribution Evidence\n\n## Overview\n\nCampaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attribution indicators using the Diamond Model and ACH (Analysis of Competing Hypotheses), analyzing infrastructure overlaps, TTP consistency, malware code similarities, operational timing patterns, and language artifacts to build confidence-weighted attribution assessments.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing campaign attribution evidence\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 `attackcti`, `stix2`, `networkx` libraries\n- Access to threat intelligence platforms (MISP, OpenCTI)\n- Understanding of Diamond Model of Intrusion Analysis\n- Familiarity with MITRE ATT&CK threat group profiles\n- Knowledge of malware analysis and infrastructure tracking techniques\n\n## Key Concepts\n\n### Attribution Evidence Categories\n1. **Infrastructure Overlap**: Shared C2 servers, domains, IP ranges, hosting providers\n2. **TTP Consistency**: Matching ATT&CK techniques and sub-techniques across campaigns\n3. **Malware Code Similarity**: Shared code bases, compilers, PDB paths, encryption routines\n4. **Operational Patterns**: Timing (working hours, time zones), targeting patterns, operational tempo\n5. **Language Artifacts**: Embedded strings, variable names, error messages in specific languages\n6. **Victimology**: Target sector, geography, and organizational profile consistency\n\n### Confidence Levels\n- **High Confidence**: Multiple independent evidence categories converge on same actor\n- **Moderate Confidence**: Several evidence categories match, some ambiguity remains\n- **Low Confidence**: Limited evidence, possible false flags or shared tooling\n\n### Analysis of Competing Hypotheses (ACH)\nStructured analytical method that evaluates evidence against multiple competing hypotheses. Each piece of evidence is scored as consistent, inconsistent, or neutral with respect to each hypothesis. The hypothesis with the least inconsistent evidence is favored.\n\n## Workflow\n\n### Step 1: Collect Attribution Evidence\n\n```python\nfrom stix2 import MemoryStore, Filter\nfrom collections import defaultdict\n\nclass AttributionAnalyzer:\n    def __init__(self):\n        self.evidence = []\n        self.hypotheses = {}\n\n    def add_evidence(self, category, description, value, confidence):\n        self.evidence.append({\n            \"category\": category,\n            \"description\": description,\n            \"value\": value,\n            \"confidence\": confidence,\n            \"timestamp\": None,\n        })\n\n    def add_hypothesis(self, actor_name, actor_id=\"\"):\n        self.hypotheses[actor_name] = {\n            \"actor_id\": actor_id,\n            \"consistent_evidence\": [],\n            \"inconsistent_evidence\": [],\n            \"neutral_evidence\": [],\n            \"score\": 0,\n        }\n\n    def evaluate_evidence(self, evidence_idx, actor_name, assessment):\n        \"\"\"Assess evidence against a hypothesis: consistent\u002Finconsistent\u002Fneutral.\"\"\"\n        if assessment == \"consistent\":\n            self.hypotheses[actor_name][\"consistent_evidence\"].append(evidence_idx)\n            self.hypotheses[actor_name][\"score\"] += self.evidence[evidence_idx][\"confidence\"]\n        elif assessment == \"inconsistent\":\n            self.hypotheses[actor_name][\"inconsistent_evidence\"].append(evidence_idx)\n            self.hypotheses[actor_name][\"score\"] -= self.evidence[evidence_idx][\"confidence\"] * 2\n        else:\n            self.hypotheses[actor_name][\"neutral_evidence\"].append(evidence_idx)\n\n    def rank_hypotheses(self):\n        \"\"\"Rank hypotheses by attribution score.\"\"\"\n        ranked = sorted(\n            self.hypotheses.items(),\n            key=lambda x: x[1][\"score\"],\n            reverse=True,\n        )\n        return [\n            {\n                \"actor\": name,\n                \"score\": data[\"score\"],\n                \"consistent\": len(data[\"consistent_evidence\"]),\n                \"inconsistent\": len(data[\"inconsistent_evidence\"]),\n                \"confidence\": self._score_to_confidence(data[\"score\"]),\n            }\n            for name, data in ranked\n        ]\n\n    def _score_to_confidence(self, score):\n        if score >= 80:\n            return \"HIGH\"\n        elif score >= 40:\n            return \"MODERATE\"\n        else:\n            return \"LOW\"\n```\n\n### Step 2: Infrastructure Overlap Analysis\n\n```python\ndef analyze_infrastructure_overlap(campaign_a_infra, campaign_b_infra):\n    \"\"\"Compare infrastructure between two campaigns for attribution.\"\"\"\n    overlap = {\n        \"shared_ips\": set(campaign_a_infra.get(\"ips\", [])).intersection(\n            campaign_b_infra.get(\"ips\", [])\n        ),\n        \"shared_domains\": set(campaign_a_infra.get(\"domains\", [])).intersection(\n            campaign_b_infra.get(\"domains\", [])\n        ),\n        \"shared_asns\": set(campaign_a_infra.get(\"asns\", [])).intersection(\n            campaign_b_infra.get(\"asns\", [])\n        ),\n        \"shared_registrars\": set(campaign_a_infra.get(\"registrars\", [])).intersection(\n            campaign_b_infra.get(\"registrars\", [])\n        ),\n    }\n\n    overlap_score = 0\n    if overlap[\"shared_ips\"]:\n        overlap_score += 30\n    if overlap[\"shared_domains\"]:\n        overlap_score += 25\n    if overlap[\"shared_asns\"]:\n        overlap_score += 15\n    if overlap[\"shared_registrars\"]:\n        overlap_score += 10\n\n    return {\n        \"overlap\": {k: list(v) for k, v in overlap.items()},\n        \"overlap_score\": overlap_score,\n        \"assessment\": \"STRONG\" if overlap_score >= 40 else \"MODERATE\" if overlap_score >= 20 else \"WEAK\",\n    }\n```\n\n### Step 3: TTP Comparison Across Campaigns\n\n```python\nfrom attackcti import attack_client\n\ndef compare_campaign_ttps(campaign_techniques, known_actor_techniques):\n    \"\"\"Compare campaign TTPs against known threat actor profiles.\"\"\"\n    campaign_set = set(campaign_techniques)\n    actor_set = set(known_actor_techniques)\n\n    common = campaign_set.intersection(actor_set)\n    unique_campaign = campaign_set - actor_set\n    unique_actor = actor_set - campaign_set\n\n    jaccard = len(common) \u002F len(campaign_set.union(actor_set)) if campaign_set.union(actor_set) else 0\n\n    return {\n        \"common_techniques\": sorted(common),\n        \"common_count\": len(common),\n        \"unique_to_campaign\": sorted(unique_campaign),\n        \"unique_to_actor\": sorted(unique_actor),\n        \"jaccard_similarity\": round(jaccard, 3),\n        \"overlap_percentage\": round(len(common) \u002F len(campaign_set) * 100, 1) if campaign_set else 0,\n    }\n```\n\n### Step 4: Generate Attribution Report\n\n```python\ndef generate_attribution_report(analyzer):\n    \"\"\"Generate structured attribution assessment report.\"\"\"\n    rankings = analyzer.rank_hypotheses()\n\n    report = {\n        \"assessment_date\": \"2026-02-23\",\n        \"total_evidence_items\": len(analyzer.evidence),\n        \"hypotheses_evaluated\": len(analyzer.hypotheses),\n        \"rankings\": rankings,\n        \"primary_attribution\": rankings[0] if rankings else None,\n        \"evidence_summary\": [\n            {\n                \"index\": i,\n                \"category\": e[\"category\"],\n                \"description\": e[\"description\"],\n                \"confidence\": e[\"confidence\"],\n            }\n            for i, e in enumerate(analyzer.evidence)\n        ],\n    }\n\n    return report\n```\n\n## Validation Criteria\n\n- Evidence collection covers all six attribution categories\n- ACH matrix properly evaluates evidence against competing hypotheses\n- Infrastructure overlap analysis identifies shared indicators\n- TTP comparison uses ATT&CK technique IDs for precision\n- Attribution confidence levels are properly justified\n- Report includes alternative hypotheses and false flag considerations\n\n## References\n\n- [Diamond Model of Intrusion Analysis](https:\u002F\u002Fwww.activeresponse.org\u002Fwp-content\u002Fuploads\u002F2013\u002F07\u002Fdiamond.pdf)\n- [MITRE ATT&CK Groups](https:\u002F\u002Fattack.mitre.org\u002Fgroups\u002F)\n- [Analysis of Competing Hypotheses](https:\u002F\u002Fwww.cia.gov\u002Fstatic\u002F9a5f1162fd0932c29e985f0159f56c07\u002FTradecraft-Primer-apr09.pdf)\n- [Threat Attribution 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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":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[136,137,138,139,140,34,141,142,143,36,37,144,145,146,147,148,149,150,151,152],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","security","incident response","identity","pr","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":160,"publishedAt":46},"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",[159],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[161,149,162,163,140,164,34,143,165,166,167,168,169,170,171,172,173,174,175,176],"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":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":187,"publishedAt":46},"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",[183,186],{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[171,188,189,190,191,34,35,192,165,193,194,195,196,197,198,199,200,201,202,203],"backup","recovery","validating backup integrity for recovery","testing","validation","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":208,"categories":209,"keywords":211,"publishedAt":46},"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",[210],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[212,213,214,34,215,216,217,218,219,220,221,222,223,224,225,226,168,227,228,229],"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":231,"name":232,"nameZh":6,"description":233,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":234,"categories":235,"keywords":237,"publishedAt":46},"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",[236],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[238,239,240,34,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256],"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":258,"name":259,"nameZh":6,"description":260,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":261,"categories":262,"keywords":264,"publishedAt":46},"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",[263],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[140,265,34,266,267,42,268,269,270,271,272,199,273,200,274,275,276,277,278,279],"triaging security incident","siem","file","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]