Patterns and techniques for evaluating and improving AI agent outputs.
中文简介正在准备中。当前显示原文说明。
Comprehensive testing framework for skills
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Validate skill functionality with automated tests.
skill/
├── SKILL.md
└── test-cases/
├── basic.yml
├── edge-cases.yml
└── integration.yml
name: Basic Functionality
description: Test core skill behavior
setup:
- mkdir -p /tmp/test-project
- cd /tmp/test-project
tests:
- name: Should load successfully
input: invoke skill
expected:
status: success
output_contains: 'Skill loaded'
- name: Should reject invalid input
input: invoke skill with "invalid"
expected:
status: error
error_contains: 'Invalid input'
teardown:
- rm -rf /tmp/test-project
dotnet-harness:test <skill> - Run all testsdotnet-harness:test <skill> --filter basic - Filter testsdotnet-harness:test --all - Test all skillsdotnet-harness:test --watch - Watch modestatus: success|erroroutput_contains: "string"output_matches: /regex/file_exists: "path"no_errors - Check stderr empty✓ skill-name
✓ basic functionality (12ms)
✓ edge cases (8ms)
✗ integration test (failed)
Expected: "success"
Got: "error: missing dependency"
Results: 2 passed, 1 failed
- name: Test Skills
run: |
dotnet-harness:test --all --format junit > results.xml
- name: Upload Results
uses: actions/upload-artifact@v4
with:
name: test-results
path: results.xml
Test fails: Check skill dependencies Timeout: Increase timeout in test config Flaky tests: Use deterministic inputs
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"
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Patterns and techniques for evaluating and improving AI agent outputs.
中文简介正在准备中。当前显示原文说明。
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations.
中文简介正在准备中。当前显示原文说明。
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
中文简介正在准备中。当前显示原文说明。
AI-powered wiki generation for code repositories with commands, agents, and skills
中文简介正在准备中。当前显示原文说明。
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
中文简介正在准备中。当前显示原文说明。
Implements accessible .NET UI. SemanticProperties, ARIA, AutomationPeer, testing per platform.
中文简介正在准备中。当前显示原文说明。