style: format all files with prettier

plugins/llm-application-dev/skills/llm-evaluation/SKILL.md

@@ -20,9 +20,11 @@ Master comprehensive evaluation strategies for LLM applications, from automated
 ## Core Evaluation Types
 
 ### 1. Automated Metrics
+
 Fast, repeatable, scalable evaluation using computed scores.
 
 **Text Generation:**
+
 - **BLEU**: N-gram overlap (translation)
 - **ROUGE**: Recall-oriented (summarization)
 - **METEOR**: Semantic similarity
@@ -30,21 +32,25 @@ Fast, repeatable, scalable evaluation using computed scores.
 - **Perplexity**: Language model confidence
 
 **Classification:**
+
 - **Accuracy**: Percentage correct
 - **Precision/Recall/F1**: Class-specific performance
 - **Confusion Matrix**: Error patterns
 - **AUC-ROC**: Ranking quality
 
 **Retrieval (RAG):**
+
 - **MRR**: Mean Reciprocal Rank
 - **NDCG**: Normalized Discounted Cumulative Gain
 - **Precision@K**: Relevant in top K
 - **Recall@K**: Coverage in top K
 
 ### 2. Human Evaluation
+
 Manual assessment for quality aspects difficult to automate.
 
 **Dimensions:**
+
 - **Accuracy**: Factual correctness
 - **Coherence**: Logical flow
 - **Relevance**: Answers the question
@@ -53,9 +59,11 @@ Manual assessment for quality aspects difficult to automate.
 - **Helpfulness**: Useful to the user
 
 ### 3. LLM-as-Judge
+
 Use stronger LLMs to evaluate weaker model outputs.
 
 **Approaches:**
+
 - **Pointwise**: Score individual responses
 - **Pairwise**: Compare two responses
 - **Reference-based**: Compare to gold standard
@@ -134,6 +142,7 @@ results = await suite.evaluate(model=your_model, test_cases=test_cases)
 ## Automated Metrics Implementation
 
 ### BLEU Score
+
 ```python
 from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
 
@@ -149,6 +158,7 @@ def calculate_bleu(reference: str, hypothesis: str, **kwargs) -> float:
 ```
 
 ### ROUGE Score
+
 ```python
 from rouge_score import rouge_scorer
 
@@ -168,6 +178,7 @@ def calculate_rouge(reference: str, hypothesis: str, **kwargs) -> dict:
 ```
 
 ### BERTScore
+
 ```python
 from bert_score import score
 
@@ -192,6 +203,7 @@ def calculate_bertscore(
 ```
 
 ### Custom Metrics
+
 ```python
 def calculate_groundedness(response: str, context: str, **kwargs) -> float:
     """Check if response is grounded in provided context."""
@@ -232,6 +244,7 @@ def calculate_factuality(claim: str, sources: list[str], **kwargs) -> float:
 ## LLM-as-Judge Patterns
 
 ### Single Output Evaluation
+
 ```python
 from anthropic import Anthropic
 from pydantic import BaseModel, Field
@@ -280,6 +293,7 @@ Provide ratings in JSON format:
 ```
 
 ### Pairwise Comparison
+
 ```python
 from pydantic import BaseModel, Field
 from typing import Literal
@@ -324,6 +338,7 @@ Answer with JSON:
 ```
 
 ### Reference-Based Evaluation
+
 ```python
 class ReferenceEvaluation(BaseModel):
     semantic_similarity: float = Field(ge=0, le=1)
@@ -371,6 +386,7 @@ Respond in JSON:
 ## Human Evaluation Frameworks
 
 ### Annotation Guidelines
+
 ```python
 from dataclasses import dataclass, field
 from typing import Optional
@@ -412,6 +428,7 @@ class AnnotationTask:
 ```
 
 ### Inter-Rater Agreement
+
 ```python
 from sklearn.metrics import cohen_kappa_score
 
@@ -444,6 +461,7 @@ def calculate_agreement(
 ## A/B Testing
 
 ### Statistical Testing Framework
+
 ```python
 from scipy import stats
 import numpy as np
@@ -504,6 +522,7 @@ class ABTest:
 ## Regression Testing
 
 ### Regression Detection
+
 ```python
 from dataclasses import dataclass
 
@@ -595,6 +614,7 @@ print(f"Mean score: {experiment_results.aggregate_metrics['qa']['mean']}")
 ## Benchmarking
 
 ### Running Benchmarks
+
 ```python
 from dataclasses import dataclass
 import numpy as np