「dr」の検索結果

Applies SOLID and DRY principles. C# anti-patterns, fixes, SRP compliance checks.

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Complete guide for ASP.NET Core Web API integration testing. Use when performing integration testing on Web API endpoints or validating ProblemDetails error format. Covers WebApplicationFactory, IExceptionHandler, Testcontainers multi-container orchestration, Flurl URL construction, and AwesomeAssertions HTTP validation. Keywords: webapi integration testing, WebApplicationFactory, asp.net core integration test, webapi integration test, IExceptionHandler, ProblemDetails, ValidationProblemDetails, AwesomeAssertions, Flurl, Respawn, Be201Created, Be400BadRequest, multi-container testing, Collection Fixture, global exception handling

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Complete guide for TUnit new-generation testing framework. Use when creating test projects with TUnit or migrating from xUnit to TUnit. Covers Source Generator driven test discovery, AOT compilation support, fluent async assertions. Includes project creation, [Test] attribute, lifecycle management, parallel control, and xUnit syntax comparison. Keywords: TUnit, tunit testing, source generator testing, AOT testing, new generation testing framework, [Test], [Arguments], TUnit.Assertions, Assert.That, Before(Test), After(Test), NotInParallel, TUnit.Templates, Microsoft.Testing.Platform, TUnit vs xUnit, parallel execution

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Creates Roslyn source generators. IIncrementalGenerator, GeneratedRegex, LoggerMessage, STJ.

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Builds event-driven systems. Pub/sub, competing consumers, DLQ, sagas, delivery guarantees.

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Builds ASP.NET Core Minimal APIs -- route groups, filters, TypedResults, OpenAPI.

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Implements producer/consumer queues. Channel<T>, bounded/unbounded, backpressure, drain.

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Interprets MSBuild output, NuGet errors, analyzer warnings. Error codes, CI drift.

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This skill covers detecting sophisticated cyber-physical attacks that follow the Stuxnet attack pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation from operators. It addresses PLC logic integrity monitoring, physics-based process anomaly detection, engineering workstation compromise indicators, USB-borne attack vectors, and multi-stage attack chain detection spanning IT-to-OT lateral movement through to process manipulation.

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This skill covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.

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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.

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Detect DLL side-loading attacks where adversaries place malicious DLLs alongside legitimate applications to hijack execution flow for defense evasion.

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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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Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.

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Detect Mimikatz execution through command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules.

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This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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Implement a vulnerability aging dashboard and SLA tracking system to measure remediation performance against severity-based timelines and drive accountability.

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Parses and analyzes the Windows Amcache.hve registry hive to extract evidence of program execution, application installation, and driver loading for digital forensics investigations. Uses Eric Zimmerman's AmcacheParser and Timeline Explorer for artifact extraction, SHA-1 hash correlation with threat intel, and timeline reconstruction. Activates for requests involving Amcache forensics, program execution evidence, Windows artifact analysis, or application compatibility cache investigation.

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Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space.

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Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.

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Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.

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Use Sysinternals Autoruns to systematically identify and analyze malware persistence mechanisms across registry keys, scheduled tasks, services, drivers, and startup locations on Windows systems.

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Check docs/ai/ spec files and ADRs for inconsistencies with the actual codebase. Report only, no modifications.

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Check docs/ai/ spec files and ADRs for inconsistencies with the codebase, then fix all discrepancies.

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