style: format all files with prettier

plugins/llm-application-dev/skills/similarity-search-patterns/SKILL.md

@@ -20,12 +20,12 @@ Patterns for implementing efficient similarity search in production systems.
 
 ### 1. Distance Metrics
 
-| Metric | Formula | Best For |
-|--------|---------|----------|
-| **Cosine** | 1 - (A·B)/(‖A‖‖B‖) | Normalized embeddings |
-| **Euclidean (L2)** | √Σ(a-b)² | Raw embeddings |
-| **Dot Product** | A·B | Magnitude matters |
-| **Manhattan (L1)** | Σ|a-b| | Sparse vectors |
+| Metric             | Formula            | Best For              |
+| ------------------ | ------------------ | --------------------- | --- | -------------- |
+| **Cosine**         | 1 - (A·B)/(‖A‖‖B‖) | Normalized embeddings |
+| **Euclidean (L2)** | √Σ(a-b)²           | Raw embeddings        |
+| **Dot Product**    | A·B                | Magnitude matters     |
+| **Manhattan (L1)** | Σ                  | a-b                   |     | Sparse vectors |
 
 ### 2. Index Types
 
@@ -538,6 +538,7 @@ class WeaviateVectorStore:
 ## Best Practices
 
 ### Do's
+
 - **Use appropriate index** - HNSW for most cases
 - **Tune parameters** - ef_search, nprobe for recall/speed
 - **Implement hybrid search** - Combine with keyword search
@@ -545,6 +546,7 @@ class WeaviateVectorStore:
 - **Pre-filter when possible** - Reduce search space
 
 ### Don'ts
+
 - **Don't skip evaluation** - Measure before optimizing
 - **Don't over-index** - Start with flat, scale up
 - **Don't ignore latency** - P99 matters for UX