“patterns”的搜索结果

Analyzes .NET code for modernization. Outdated TFMs, deprecated packages, superseded patterns.

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Synchronizes threads and protects shared state. lock, SemaphoreSlim, Interlocked, concurrent collections.

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Using records, pattern matching, primary constructors, collection expressions. C# 12-15 by TFM.

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

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AI-powered skill recommendation engine for dotnet-harness. Analyzes project context and recommends relevant skills based on detected frameworks, packages, and patterns. Triggers on: recommend skill, suggest skills, what skill should I use, skill suggestion, project analysis recommendation.

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Tunes GC and memory. GC modes, LOH/POH, Gen0/1/2, Span/Memory deep patterns, ArrayPool.

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Composes GitHub Actions workflows. Reusable workflows, composite actions, matrix, caching.

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Authors MSBuild targets, props, conditions, incremental builds, and Directory.Build patterns.

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EF Core, DbContext, AsNoTracking, query splitting.

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Builds ASP.NET Core middleware. Pipeline ordering, short-circuit, exception handling.

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Orchestrates .NET Aspire apps. AppHost, service discovery, components, dashboard, health checks.

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Detects C# code smells during review. Anti-patterns, async misuse, DI mistakes, fixes.

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Designs public .NET APIs. Naming, parameter ordering, return types, error patterns, extensions.

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AI-powered skill recommendation engine for dotnet-agent-harness. Analyzes project context and recommends relevant skills based on detected frameworks, packages, and patterns. Triggers on: recommend skill, suggest skills, what skill should I use, skill suggestion, project analysis recommendation.

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Architects Blazor apps. Hosting models, render modes, routing, streaming, prerender.

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Composes Azure DevOps YAML pipelines. Templates, variable groups, multi-stage, triggers.

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Patterns and techniques for evaluating and improving AI agent outputs.

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Designs ASP.NET Core architecture -- vertical slices, pipelines, caching, errors.

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Build and harden high-performance APIs with strong security controls, endpoint budgets, rate limiting, and resilience patterns. Use when tasks involve API latency/throughput tuning, abuse resistance, authz hardening, or API release gates.

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Extract decisions, lessons, patterns, and surprises from completed phase artifacts

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Detect suspicious PowerShell execution patterns including encoded commands, download cradles, AMSI bypass attempts, and constrained language mode evasion.

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Detect process injection techniques (T1055) including classic DLL injection, process hollowing, and APC injection by analyzing Sysmon events for cross-process memory operations, remote thread creation, and anomalous DLL loading patterns.

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Analyze WAF (ModSecurity/AWS WAF/Cloudflare) logs to detect SQL injection attack campaigns. Parses ModSecurity audit logs and JSON WAF event logs to identify SQLi patterns (UNION SELECT, OR 1=1, SLEEP(), BENCHMARK()), tracks attack sources, correlates multi-stage injection attempts, and generates incident reports with OWASP classification.

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Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance scanning, and staging behavior. Uses network detection tools (Zeek, Suricata, Arkime), SIEM correlation rules, and threat intelligence feeds to identify ransomware precursor patterns such as Cobalt Strike beacons, Mimikatz network signatures, and RDP brute-force attempts. Activates for requests involving pre-ransomware detection, network-based ransomware indicators, or early warning ransomware monitoring.

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