Patterns and techniques for evaluating and improving AI agent outputs.
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。
Offline mode with local caching for air-gapped environments
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Enable offline operation with local skill and MCP caching.
# Set offline mode
export DOTNET_HARNESS_OFFLINE=true
# Or use flag
rulesync generate --offline
```text
## Cache Structure
```text
.dotnet-harness/
├── cache/
│ ├── skills/ # Cached skill content
│ ├── mcp-responses/ # Cached MCP responses
│ └── registry/ # Cached registry data
└── index.json # Search index
Online Sync (when connected):
dotnet-harness:sync-cache
Offline Operation:
{
"offline": {
"enabled": false,
"cachePath": ".dotnet-harness/cache",
"maxCacheAge": "7d",
"fallbackEnabled": true
}
}
dotnet-harness:sync-cache - Sync cache from remotedotnet-harness:clear-cache - Clear local cachedotnet-harness:cache-status - Show cache statisticsWhen offline:
Cache Miss: Run dotnet-harness:sync-cache Stale Data: Clear and re-sync Large Cache: Exclude heavy MCP
servers
Primary 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"
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Patterns and techniques for evaluating and improving AI agent outputs.
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。
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations.
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。
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
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。
AI-powered wiki generation for code repositories with commands, agents, and skills
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。
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patterns, or feature folders; when implementing HTTP resilience, Options pattern, Channels, or validation; when seeing outdated patterns like old extension method syntax
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。
Implements accessible .NET UI. SemanticProperties, ARIA, AutomationPeer, testing per platform.
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。