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Builds multi-touch pipeline attribution models and generates stakeholder-ready narratives from campaign performance data. Use when analyzing channel contribution to pipeline, preparing monthly/quarterly revenue reports for leadership, diagnosing pipeline misses, modeling budget reallocation scenarios, or translating raw attribution data into board-deck language. Designed for B2B SaaS demand gen teams that report on pipeline and revenue, not leads or MQLs.

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Generates and maintains competitive battlecards from win/loss data, competitor intel, G2 reviews, and sales feedback. Use when building battlecards for a specific competitor, updating existing battlecards with new intel, preparing BDRs or AEs for competitive deals, analyzing win/loss patterns against a competitor, or responding to a competitor's product launch or pricing change. Also use when asked to "build a battlecard," "update competitive intel," "how do we beat [competitor]," "what do we say when a prospect mentions [competitor]," or "analyze our win/loss against [competitor]." Designed for B2B SaaS teams selling against known competitors in deals with multi-persona buying committees.

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End-to-end ABM program design and orchestration for B2B SaaS mid-market and enterprise motions. Use when building a target account list, designing account tiering criteria, planning ABM channel orchestration, coordinating BDR outreach with marketing campaigns, defining ABM measurement frameworks, or running an ABM program review. Also use when asked to "build an ABM program," "set up account-based marketing," "design a target account strategy," "plan ABM campaigns," or "measure ABM results." Designed for demand gen teams selling to Mid-Market companies with $10K-$50K+ ACV, multi-persona buying committees, and 3-6 month sales cycles.

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Generates sourced, BDR-ready account research briefs for named target accounts in B2B SaaS ABM and outbound motions. Use after a target account list is built and before outreach sequences are written. Best for Tier 1, Tier 2, and intent-qualified accounts where BDRs need a clear "why this account, why now, why this persona" hypothesis. Designed for B2B SaaS teams selling compliance, security, GRC, risk, or other technical platforms into mid-market and enterprise buyers. Not intended for broad list building or mass enrichment.

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Configures Fail2ban with custom filters and actions to detect port scanning activity, SSH brute force attempts, and network reconnaissance, automatically banning offending IP addresses and alerting security teams to suspicious network probing.

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Detect RDP brute force attacks by analyzing Windows Security Event Logs for failed authentication patterns (Event ID 4625), successful logons after failures (Event ID 4624), NLA failures, and source IP frequency analysis.

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This skill teaches security teams how to detect and respond to unauthorized cryptocurrency mining operations in cloud environments. It covers identifying cryptomining indicators through compute usage anomalies, network traffic patterns to mining pools, GuardDuty CryptoCurrency findings, and runtime process monitoring on EC2, ECS, EKS, and Azure Automation workloads.

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This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.

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Collects and synthesizes open-source intelligence (OSINT) about threat actors, malicious infrastructure, and attack campaigns using publicly available data sources, passive reconnaissance tools, and dark web monitoring. Use when investigating external threat actor infrastructure, performing pre-engagement reconnaissance for authorized red team assessments, or enriching CTI reports with publicly available adversary context. Activates for requests involving Maltego, Shodan, OSINT framework, SpiderFoot, or infrastructure reconnaissance.

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Builds automated threat intelligence feed integration pipelines connecting STIX/TAXII feeds, open-source threat intel, and commercial TI platforms into SIEM and security tools for real-time IOC matching and alerting. Use when SOC teams need to operationalize threat intelligence by automating feed ingestion, normalization, scoring, and distribution to detection systems.

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Conduct internal Active Directory reconnaissance using BloodHound Community Edition to map attack paths, identify privilege escalation chains, and discover misconfigurations in domain environments.

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Builds a structured vulnerability scanning workflow using tools like Nessus, Qualys, and OpenVAS to discover, prioritize, and track remediation of security vulnerabilities across infrastructure. Use when SOC teams need to establish recurring vulnerability assessment processes, integrate scan results with SIEM alerting, and build remediation tracking dashboards.

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Design and execute a social engineering penetration test including phishing, vishing, smishing, and physical pretexting campaigns to measure human security resilience and identify training gaps.

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Plan and execute authorized vishing (voice phishing) pretext calls to assess employee susceptibility to social engineering and evaluate security awareness controls.

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Builds a structured SOC incident response playbook for ransomware attacks covering detection, containment, eradication, and recovery phases with specific SIEM queries, isolation procedures, and decision trees. Use when SOC teams need formalized response procedures for ransomware incidents aligned to NIST SP 800-61 and MITRE ATT&CK ransomware techniques.

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Perform DCSync attacks to replicate Active Directory credentials and establish domain persistence by extracting KRBTGT, Domain Admin, and service account hashes for Golden Ticket creation.

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Pass-the-Ticket (PtT) is a lateral movement technique that uses stolen Kerberos tickets (TGT or TGS) to authenticate to services without knowing the user's password. By extracting Kerberos tickets fro

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Builds real-time incident response dashboards in Splunk, Elastic, or Grafana to provide SOC analysts and leadership with situational awareness during active incidents, tracking affected systems, containment status, IOC spread, and response timeline. Use when IR teams need unified visibility during incident coordination and post-incident reporting.

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Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration. Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert triage.

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Analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network security controls.

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Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.

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Extract and analyze Cobalt Strike beacon configuration from PE files and memory dumps to identify C2 infrastructure, malleable profiles, and operator tradecraft.

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Focused Signals scout for PostHog projects running surveys. Watches active surveys for score regressions (NPS / CSAT / rating drops), response-volume drops, abandonment spikes, and targeting drift, AND aggregates open-text responses into recurring themes the team should know about (clusters of complaints, praise, feature requests). Emits findings only when a theme or anomaly clears the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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Signals scout that watches a PostHog project's most-viewed dashboards and insights for recent anomalies — sudden bursts, drops, flat-lines, and trend breaks at the daily or hourly level. It discovers what the team actually looks at (view counts, dashboard access), curates a durable watchlist in the scratchpad, and balances re-checking known high-value insights (exploit) against discovering new ones (explore) across runs, since no single run can cover a busy project. Anomalies are scored by robust deviation from each insight's own seasonality-matched baseline; it emits a finding only when a move clears the confidence bar, otherwise it updates the baseline memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

日本語の概要は準備中です。原文の説明を表示しています。