Patterns and techniques for evaluating and improving AI agent outputs.
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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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AI-powered skill recommendation engine that analyzes .NET project context and suggests relevant skills.
Load this skill when:
Analyzes:
.csproj, .sln, global.jsonnet8.0, netstandard2.0)Maps detected technologies to skill tags:
| Detected | Suggested Skills |
|---|---|
| Microsoft.AspNetCore.* | dotnet-minimal-apis, dotnet-api-security, dotnet-middleware-patterns |
| Microsoft.EntityFrameworkCore.* | dotnet-efcore-patterns, dotnet-efcore-architecture |
| Microsoft.Maui.* | dotnet-maui-development, dotnet-maui-aot |
| Microsoft.NET.Sdk.BlazorWebAssembly | dotnet-blazor-components, dotnet-blazor-testing |
| Azure.* | dotnet-cloud-specialist, dotnet-resilience |
| xunit, nunit, mstest | dotnet-xunit, dotnet-testing-strategy |
Scoring factors:
/dotnet-agent-harness:recommend
dotnet agent-harness recommend --format json
Analyzes current project and outputs:
Recommended Skills (based on project analysis):
1. dotnet-efcore-patterns (95/100)
Detected: Microsoft.EntityFrameworkCore 8.0.0
Why: Core EF patterns for data access layer
2. dotnet-api-security (80/100)
Detected: Microsoft.AspNetCore.Authentication.JwtBearer
Why: JWT auth and API security patterns
3. dotnet-resilience (75/100)
Detected: Polly package
Why: Retry, circuit breaker patterns
/dotnet-agent-harness:recommend --platform codexcli
/dotnet-agent-harness:recommend --category data
/dotnet-agent-harness:recommend --category testing
/dotnet-agent-harness:recommend --category security
dotnet agent-harness recommend --platform codexcli --category data --format json
Use --platform when you need only artifacts that map cleanly to a specific runtime surface. For example,
--platform codexcli excludes command-only content, while --platform geminicli excludes subagent-only content.
Session start hook automatically runs recommendation when:
.csproj detected in workspaceSee .rulesync/hooks.json for implementation.
<Project Sdk="Microsoft.NET.Sdk.Web">
<PackageReference Include="Microsoft.AspNetCore.OpenApi" />
</Project>
Triggers: dotnet-minimal-apis, dotnet-api-versioning, dotnet-openapi
<Project Sdk="Microsoft.NET.Sdk">
<TargetFramework>netstandard2.0</TargetFramework>
</Project>
Triggers: dotnet-library-api-compat, dotnet-nuget-authoring
<Project Sdk="Microsoft.NET.Sdk">
<UseMaui>true</UseMaui>
</Project>
Triggers: dotnet-maui-development, dotnet-maui-aot, dotnet-uno-platform
<Project Sdk="Microsoft.NET.Sdk.BlazorWebAssembly">
<PackageReference Include="Microsoft.AspNetCore.Components.WebAssembly" />
</Project>
Triggers: dotnet-blazor-components, dotnet-blazor-testing, dotnet-aot-wasm
<Project Sdk="Microsoft.NET.Sdk">
<OutputType>Exe</OutputType>
</Project>
Triggers: dotnet-system-commandline, dotnet-cli-architecture
{
"project_type": "Web API",
"detected_frameworks": ["ASP.NET Core 8.0", "EF Core 8.0"],
"recommendations": [
{
"skill": "dotnet-efcore-patterns",
"confidence": 95,
"reason": "Microsoft.EntityFrameworkCore detected",
"priority": "high"
}
],
"alternatives": [
{
"skill": "dotnet-dapper-patterns",
"reason": "Alternative to EF Core for performance"
}
]
}
All analysis happens client-side:
.dotnet-agent-harness/recommendations.jsonPrimary approach: Use Serena symbol operations for efficient code navigation:
serena_find_symbol instead of text searchserena_get_symbols_overview for file organizationserena_find_referencing_symbols for impact analysisserena_replace_symbol_body for clean modificationsWhen to use Serena vs traditional tools:
Example workflow:
# Instead of:
Read: src/Services/OrderService.cs
Grep: "public void ProcessOrder"
# Use:
serena_find_symbol: "OrderService/ProcessOrder"
serena_get_symbols_overview: "src/Services/OrderService.cs"
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Patterns and techniques for evaluating and improving AI agent outputs.
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Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations.
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Use when user requests research requiring multiple sources, comprehensive analysis, or synthesis across topics - technical research, domain knowledge gathering, market analysis, or learning about complex subjects
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AI-powered wiki generation for code repositories with commands, agents, and skills
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Use when building .NET 10 or C# 14 applications; when using minimal APIs, modular monolith patterns, or feature folders; when implementing HTTP resilience, Options pattern, Channels, or validation; when seeing outdated patterns like old extension method syntax
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Implements accessible .NET UI. SemanticProperties, ARIA, AutomationPeer, testing per platform.
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