Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
"
Evaluate this output against criteria:
{string.Join("
", criteria)}
Output:
{output}
Rate each criteria. Return ONLY a JSON object where keys are the criteria and values are objects with a 'status' ("
" or "
") and 'feedback' string.
"
""
var
await
new
var
"{}"
string
null
try
string
catch
// Fallback to empty if json parsing fails
new
string
if
null
0
"PASS"
return
var
"FAIL"
new
string
string
if
0
// If nothing explicitly failed but parsing succeeded, break out to avoid unguided infinite loops
break
var
var
$"Improve the original output to address these failures: {failedJson}\nOriginal Output: {output}"
var
await
string
return
private
class
CritiqueResult
JsonPropertyName("status")
public
string
get
set
string
JsonPropertyName("feedback")
public
string
get
set
string
Key insight: Use structured JSON output for reliable parsing of critique results. In .NET, you can also use
IChatClient directly with structured output models to avoid manual deserialization checking.
Pattern 2: Evaluator-Optimizer
Separate generation and evaluation into distinct components for clearer responsibilities.
using System;
using System.Collections.Generic;
using System.Text.Json;
using System.Text.Json.Serialization;
using System.Threading;
using System.Threading.Tasks;
using Microsoft.Extensions.AI;
namespaceDotNetAgentHarness.Evals.Engine;
publicclassEvaluatorOptimizer(IChatClient chatClient, double scoreThreshold = 0.8)
{
publicasync Task<string> GenerateAsync(string task, CancellationToken cancellationToken = default)
{
var response = await chatClient.GetResponseAsync($"Complete: {task}", cancellationToken: cancellationToken);
return response.Text ?? string.Empty;
}
publicasync Task<EvaluationResult> EvaluateAsync(string output, string task, CancellationToken cancellationToken = default)
{
var prompt = $$"""
Evaluate output for task: {{task}}
Output:
{{output}}
Return JSON in this format: { "overall_score": 0.0, "dimensions": { "accuracy": 0.0, "clarity": 0.0 } }
""";
var response = await chatClient.GetResponseAsync(
prompt,
new ChatOptions { ResponseFormat = ChatResponseFormat.Json },
cancellationToken);
var jsonText = response.Text ?? "{}";
try
{
return JsonSerializer.Deserialize<EvaluationResult>(jsonText) ?? new EvaluationResult();
}
catch (JsonException)
{
returnnew EvaluationResult();
}
}
publicasync Task<string> OptimizeAsync(string output, EvaluationResult feedback, CancellationToken cancellationToken = default)
{
var feedbackJson = JsonSerializer.Serialize(feedback);
var prompt = $"Improve based on feedback: {feedbackJson}\nOutput: {output}";
var response = await chatClient.GetResponseAsync(prompt, cancellationToken: cancellationToken);
return response.Text ?? string.Empty;
}
publicasync Task<string> RunAsync(string task, int maxIterations = 3, CancellationToken cancellationToken = default)
{
var output = await GenerateAsync(task, cancellationToken);
for (int i = 0; i < maxIterations; i++)
{
var evaluation = await EvaluateAsync(output, task, cancellationToken);
if (evaluation.OverallScore >= scoreThreshold)
{
break;
}
output = await OptimizeAsync(output, evaluation, cancellationToken);
}
return output;
}
}
publicclassEvaluationResult
{
[JsonPropertyName("overall_score")]
publicdouble OverallScore { get; set; }
[JsonPropertyName("dimensions")]
public Dictionary<string, double> Dimensions { get; set; } = new();
}
Pattern 3: Code-Specific Reflection
Test-driven refinement loop for code generation.
using System;
using System.Threading;
using System.Threading.Tasks;
using Microsoft.Extensions.AI;
namespaceDotNetAgentHarness.Evals.Engine;
publicclassCodeReflector(IChatClient chatClient)
{
publicasync Task<string> ReflectAndFixAsync(string spec, int maxIterations = 3, CancellationToken cancellationToken = default)
{
var codeResponse = await chatClient.GetResponseAsync($"Write C# code for: {spec}", cancellationToken: cancellationToken);
var code = codeResponse.Text ?? string.Empty;
var testsResponse = await chatClient.GetResponseAsync($"Generate xUnit tests for: {spec}\nCode: {code}", cancellationToken: cancellationToken);
var tests = testsResponse.Text ?? string.Empty;
for (int i = 0; i < maxIterations; i++)
{
var result = await RunTestsAsync(code, tests, cancellationToken);
if (result.Success)
{
return code;
}
var fixResponse = await chatClient.GetResponseAsync($"Fix error: {result.Error}\nCode: {code}", cancellationToken: cancellationToken);
code = fixResponse.Text ?? string.Empty;
}
return code;
}
// Stub for actual test executionprivate Task<TestResult> RunTestsAsync(string code, string tests, CancellationToken cancellationToken = default)
{
// In a real implementation, you would compile and run the tests dynamicallyreturn Task.FromResult(new TestResult { Success = false, Error = "Mock test failure" });
}
privateclassTestResult
{
publicbool Success { get; set; }
publicstring Error { get; set; } = string.Empty;
}
}
Evaluation Strategies
Outcome-Based
Evaluate whether output achieves the expected result.
publicasync Task<string> LlmJudgeAsync(string outputA, string outputB, string criteria, CancellationToken cancellationToken = default)
{
var response = await chatClient.GetResponseAsync(
$"Compare outputs A and B for {criteria}. Which is better and why?\n\nOutput A:\n{outputA}\n\nOutput B:\n{outputB}",
cancellationToken: cancellationToken
);
return response.Text ?? string.Empty;
}
Rubric-Based
Score outputs against weighted dimensions.
publicclassRubricDimension
{
publicdouble Weight { get; set; }
}
publicasync Task<double> EvaluateWithRubricAsync(string output, Dictionary<string, RubricDimension> rubric, CancellationToken cancellationToken = default)
{
var dimensions = string.Join(", ", rubric.Keys);
var prompt = $"Rate 1-5 for each dimension: {dimensions}\nOutput: {output}\n\nReturn ONLY a JSON dictionary where keys are dimensions and values are numbers.";
var response = await chatClient.GetResponseAsync(
prompt,
new ChatOptions { ResponseFormat = ChatResponseFormat.Json },
cancellationToken);
var jsonText = response.Text ?? "{}";
Dictionary<string, double> scores;
try
{
scores = JsonSerializer.Deserialize<Dictionary<string, double>>(jsonText) ?? new Dictionary<string, double>();
}
catch (JsonException)
{
scores = new Dictionary<string, double>();
}
double totalScore = 0;
foreach (var dimension in rubric.Keys)
{
if (scores.TryGetValue(dimension, outvar score))
{
totalScore += score * rubric[dimension].Weight;
}
}
return totalScore / 5.0; // Normalize
}
