style: format all files with prettier

plugins/llm-application-dev/skills/embedding-strategies/SKILL.md

@@ -20,18 +20,18 @@ Guide to selecting and optimizing embedding models for vector search application
 
 ### 1. Embedding Model Comparison (2026)
 
-| Model | Dimensions | Max Tokens | Best For |
-|-------|------------|------------|----------|
-| **voyage-3-large** | 1024 | 32000 | Claude apps (Anthropic recommended) |
-| **voyage-3** | 1024 | 32000 | Claude apps, cost-effective |
-| **voyage-code-3** | 1024 | 32000 | Code search |
-| **voyage-finance-2** | 1024 | 32000 | Financial documents |
-| **voyage-law-2** | 1024 | 32000 | Legal documents |
-| **text-embedding-3-large** | 3072 | 8191 | OpenAI apps, high accuracy |
-| **text-embedding-3-small** | 1536 | 8191 | OpenAI apps, cost-effective |
-| **bge-large-en-v1.5** | 1024 | 512 | Open source, local deployment |
-| **all-MiniLM-L6-v2** | 384 | 256 | Fast, lightweight |
-| **multilingual-e5-large** | 1024 | 512 | Multi-language |
+| Model                      | Dimensions | Max Tokens | Best For                            |
+| -------------------------- | ---------- | ---------- | ----------------------------------- |
+| **voyage-3-large**         | 1024       | 32000      | Claude apps (Anthropic recommended) |
+| **voyage-3**               | 1024       | 32000      | Claude apps, cost-effective         |
+| **voyage-code-3**          | 1024       | 32000      | Code search                         |
+| **voyage-finance-2**       | 1024       | 32000      | Financial documents                 |
+| **voyage-law-2**           | 1024       | 32000      | Legal documents                     |
+| **text-embedding-3-large** | 3072       | 8191       | OpenAI apps, high accuracy          |
+| **text-embedding-3-small** | 1536       | 8191       | OpenAI apps, cost-effective         |
+| **bge-large-en-v1.5**      | 1024       | 512        | Open source, local deployment       |
+| **all-MiniLM-L6-v2**       | 384        | 256        | Fast, lightweight                   |
+| **multilingual-e5-large**  | 1024       | 512        | Multi-language                      |
 
 ### 2. Embedding Pipeline
 
@@ -583,6 +583,7 @@ def compare_embedding_models(
 ## Best Practices
 
 ### Do's
+
 - **Match model to use case**: Code vs prose vs multilingual
 - **Chunk thoughtfully**: Preserve semantic boundaries
 - **Normalize embeddings**: For cosine similarity search
@@ -591,6 +592,7 @@ def compare_embedding_models(
 - **Use Voyage AI for Claude apps**: Recommended by Anthropic
 
 ### Don'ts
+
 - **Don't ignore token limits**: Truncation loses information
 - **Don't mix embedding models**: Incompatible vector spaces
 - **Don't skip preprocessing**: Garbage in, garbage out

plugins/llm-application-dev/skills/hybrid-search-implementation/SKILL.md

@@ -27,12 +27,12 @@ Query → ┬─► Vector Search ──► Candidates ─┐
 
 ### 2. Fusion Methods
 
-| Method | Description | Best For |
-|--------|-------------|----------|
-| **RRF** | Reciprocal Rank Fusion | General purpose |
-| **Linear** | Weighted sum of scores | Tunable balance |
+| Method            | Description              | Best For        |
+| ----------------- | ------------------------ | --------------- |
+| **RRF**           | Reciprocal Rank Fusion   | General purpose |
+| **Linear**        | Weighted sum of scores   | Tunable balance |
 | **Cross-encoder** | Rerank with neural model | Highest quality |
-| **Cascade** | Filter then rerank | Efficiency |
+| **Cascade**       | Filter then rerank       | Efficiency      |
 
 ## Templates
 
@@ -549,6 +549,7 @@ class HybridRAGPipeline:
 ## Best Practices
 
 ### Do's
+
 - **Tune weights empirically** - Test on your data
 - **Use RRF for simplicity** - Works well without tuning
 - **Add reranking** - Significant quality improvement
@@ -556,6 +557,7 @@ class HybridRAGPipeline:
 - **A/B test** - Measure real user impact
 
 ### Don'ts
+
 - **Don't assume one size fits all** - Different queries need different weights
 - **Don't skip keyword search** - Handles exact matches better
 - **Don't over-fetch** - Balance recall vs latency

plugins/llm-application-dev/skills/langchain-architecture/SKILL.md

@@ -33,9 +33,11 @@ langchain-pinecone        # Pinecone vector store
 ## Core Concepts
 
 ### 1. LangGraph Agents
+
 LangGraph is the standard for building agents in 2026. It provides:
 
 **Key Features:**
+
 - **StateGraph**: Explicit state management with typed state
 - **Durable Execution**: Agents persist through failures
 - **Human-in-the-Loop**: Inspect and modify state at any point
@@ -43,12 +45,14 @@ LangGraph is the standard for building agents in 2026. It provides:
 - **Checkpointing**: Save and resume agent state
 
 **Agent Patterns:**
+
 - **ReAct**: Reasoning + Acting with `create_react_agent`
 - **Plan-and-Execute**: Separate planning and execution nodes
 - **Multi-Agent**: Supervisor routing between specialized agents
 - **Tool-Calling**: Structured tool invocation with Pydantic schemas
 
 ### 2. State Management
+
 LangGraph uses TypedDict for explicit state:
 
 ```python
@@ -69,6 +73,7 @@ class CustomState(TypedDict):
 ```
 
 ### 3. Memory Systems
+
 Modern memory implementations:
 
 - **ConversationBufferMemory**: Stores all messages (short conversations)
@@ -78,15 +83,18 @@ Modern memory implementations:
 - **LangGraph Checkpointers**: Persistent state across sessions
 
 ### 4. Document Processing
+
 Loading, transforming, and storing documents:
 
 **Components:**
+
 - **Document Loaders**: Load from various sources
 - **Text Splitters**: Chunk documents intelligently
 - **Vector Stores**: Store and retrieve embeddings
 - **Retrievers**: Fetch relevant documents
 
 ### 5. Callbacks & Tracing
+
 LangSmith is the standard for observability:
 
 - Request/response logging

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

plugins/llm-application-dev/skills/prompt-engineering-patterns/SKILL.md

@@ -21,6 +21,7 @@ Master advanced prompt engineering techniques to maximize LLM performance, relia
 ## Core Capabilities
 
 ### 1. Few-Shot Learning
+
 - Example selection strategies (semantic similarity, diversity sampling)
 - Balancing example count with context window constraints
 - Constructing effective demonstrations with input-output pairs
@@ -28,6 +29,7 @@ Master advanced prompt engineering techniques to maximize LLM performance, relia
 - Handling edge cases through strategic example selection
 
 ### 2. Chain-of-Thought Prompting
+
 - Step-by-step reasoning elicitation
 - Zero-shot CoT with "Let's think step by step"
 - Few-shot CoT with reasoning traces
@@ -35,12 +37,14 @@ Master advanced prompt engineering techniques to maximize LLM performance, relia
 - Verification and validation steps
 
 ### 3. Structured Outputs
+
 - JSON mode for reliable parsing
 - Pydantic schema enforcement
 - Type-safe response handling
 - Error handling for malformed outputs
 
 ### 4. Prompt Optimization
+
 - Iterative refinement workflows
 - A/B testing prompt variations
 - Measuring prompt performance metrics (accuracy, consistency, latency)
@@ -48,6 +52,7 @@ Master advanced prompt engineering techniques to maximize LLM performance, relia
 - Handling edge cases and failure modes
 
 ### 5. Template Systems
+
 - Variable interpolation and formatting
 - Conditional prompt sections
 - Multi-turn conversation templates
@@ -55,6 +60,7 @@ Master advanced prompt engineering techniques to maximize LLM performance, relia
 - Modular prompt components
 
 ### 6. System Prompt Design
+
 - Setting model behavior and constraints
 - Defining output formats and structure
 - Establishing role and expertise
@@ -395,6 +401,7 @@ Response:"""
 ## Performance Optimization
 
 ### Token Efficiency
+
 ```python
 # Before: Verbose prompt (150+ tokens)
 verbose_prompt = """
@@ -457,6 +464,7 @@ response = client.messages.create(
 ## Success Metrics
 
 Track these KPIs for your prompts:
+
 - **Accuracy**: Correctness of outputs
 - **Consistency**: Reproducibility across similar inputs
 - **Latency**: Response time (P50, P95, P99)

plugins/llm-application-dev/skills/prompt-engineering-patterns/assets/prompt-template-library.md

@@ -3,6 +3,7 @@
 ## Classification Templates
 
 ### Sentiment Analysis
+
 ```
 Classify the sentiment of the following text as Positive, Negative, or Neutral.
 
@@ -12,6 +13,7 @@ Sentiment:
 ```
 
 ### Intent Detection
+
 ```
 Determine the user's intent from the following message.
 
@@ -23,6 +25,7 @@ Intent:
 ```
 
 ### Topic Classification
+
 ```
 Classify the following article into one of these categories: {categories}
 
@@ -35,6 +38,7 @@ Category:
 ## Extraction Templates
 
 ### Named Entity Recognition
+
 ```
 Extract all named entities from the text and categorize them.
 
@@ -50,6 +54,7 @@ Entities (JSON format):
 ```
 
 ### Structured Data Extraction
+
 ```
 Extract structured information from the job posting.
 
@@ -70,6 +75,7 @@ Extracted Information (JSON):
 ## Generation Templates
 
 ### Email Generation
+
 ```
 Write a professional {email_type} email.
 
@@ -84,6 +90,7 @@ Body:
 ```
 
 ### Code Generation
+
 ```
 Generate {language} code for the following task:
 
@@ -101,6 +108,7 @@ Code:
 ```
 
 ### Creative Writing
+
 ```
 Write a {length}-word {style} story about {topic}.
 
@@ -115,6 +123,7 @@ Story:
 ## Transformation Templates
 
 ### Summarization
+
 ```
 Summarize the following text in {num_sentences} sentences.
 
@@ -125,6 +134,7 @@ Summary:
 ```
 
 ### Translation with Context
+
 ```
 Translate the following {source_lang} text to {target_lang}.
 
@@ -137,6 +147,7 @@ Translation:
 ```
 
 ### Format Conversion
+
 ```
 Convert the following {source_format} to {target_format}.
 
@@ -149,6 +160,7 @@ Output ({target_format}):
 ## Analysis Templates
 
 ### Code Review
+
 ```
 Review the following code for:
 1. Bugs and errors
@@ -163,6 +175,7 @@ Review:
 ```
 
 ### SWOT Analysis
+
 ```
 Conduct a SWOT analysis for: {subject}
 
@@ -185,6 +198,7 @@ Threats:
 ## Question Answering Templates
 
 ### RAG Template
+
 ```
 Answer the question based on the provided context. If the context doesn't contain enough information, say so.
 
@@ -197,6 +211,7 @@ Answer:
 ```
 
 ### Multi-Turn Q&A
+
 ```
 Previous conversation:
 {conversation_history}
@@ -209,6 +224,7 @@ Answer (continue naturally from conversation):
 ## Specialized Templates
 
 ### SQL Query Generation
+
 ```
 Generate a SQL query for the following request.
 
@@ -221,6 +237,7 @@ SQL Query:
 ```
 
 ### Regex Pattern Creation
+
 ```
 Create a regex pattern to match: {requirement}
 
@@ -234,6 +251,7 @@ Regex pattern:
 ```
 
 ### API Documentation
+
 ```
 Generate API documentation for this function:
 

plugins/llm-application-dev/skills/prompt-engineering-patterns/references/chain-of-thought.md

@@ -7,6 +7,7 @@ Chain-of-Thought (CoT) prompting elicits step-by-step reasoning from LLMs, drama
 ## Core Techniques
 
 ### Zero-Shot CoT
+
 Add a simple trigger phrase to elicit reasoning:
 
 ```python
@@ -29,6 +30,7 @@ prompt = zero_shot_cot(query)
 ```
 
 ### Few-Shot CoT
+
 Provide examples with explicit reasoning chains:
 
 ```python
@@ -53,6 +55,7 @@ A: Let's think step by step:"""
 ```
 
 ### Self-Consistency
+
 Generate multiple reasoning paths and take the majority vote:
 
 ```python
@@ -85,6 +88,7 @@ def self_consistency_cot(query, n=5, temperature=0.7):
 ## Advanced Patterns
 
 ### Least-to-Most Prompting
+
 Break complex problems into simpler subproblems:
 
 ```python
@@ -125,6 +129,7 @@ Final Answer:"""
 ```
 
 ### Tree-of-Thought (ToT)
+
 Explore multiple reasoning branches:
 
 ```python
@@ -176,6 +181,7 @@ Score:"""
 ```
 
 ### Verification Step
+
 Add explicit verification to catch errors:
 
 ```python
@@ -220,6 +226,7 @@ Corrected solution:"""
 ## Domain-Specific CoT
 
 ### Math Problems
+
 ```python
 math_cot_template = """
 Problem: {problem}
@@ -248,6 +255,7 @@ Answer: {final_answer}
 ```
 
 ### Code Debugging
+
 ```python
 debug_cot_template = """
 Code with error:
@@ -278,6 +286,7 @@ Fixed code:
 ```
 
 ### Logical Reasoning
+
 ```python
 logic_cot_template = """
 Premises:
@@ -305,6 +314,7 @@ Answer: {final_answer}
 ## Performance Optimization
 
 ### Caching Reasoning Patterns
+
 ```python
 class ReasoningCache:
     def __init__(self):
@@ -328,6 +338,7 @@ class ReasoningCache:
 ```
 
 ### Adaptive Reasoning Depth
+
 ```python
 def adaptive_cot(problem, initial_depth=3):
     depth = initial_depth
@@ -378,6 +389,7 @@ def evaluate_cot_quality(reasoning_chain):
 ## When to Use CoT
 
 **Use CoT for:**
+
 - Math and arithmetic problems
 - Logical reasoning tasks
 - Multi-step planning
@@ -385,6 +397,7 @@ def evaluate_cot_quality(reasoning_chain):
 - Complex decision making
 
 **Skip CoT for:**
+
 - Simple factual queries
 - Direct lookups
 - Creative writing

plugins/llm-application-dev/skills/prompt-engineering-patterns/references/few-shot-learning.md

@@ -7,6 +7,7 @@ Few-shot learning enables LLMs to perform tasks by providing a small number of e
 ## Example Selection Strategies
 
 ### 1. Semantic Similarity
+
 Select examples most similar to the input query using embedding-based retrieval.
 
 ```python
@@ -29,6 +30,7 @@ class SemanticExampleSelector:
 **Best For**: Question answering, text classification, extraction tasks
 
 ### 2. Diversity Sampling
+
 Maximize coverage of different patterns and edge cases.
 
 ```python
@@ -58,6 +60,7 @@ class DiversityExampleSelector:
 **Best For**: Demonstrating task variability, edge case handling
 
 ### 3. Difficulty-Based Selection
+
 Gradually increase example complexity to scaffold learning.
 
 ```python
@@ -75,6 +78,7 @@ class ProgressiveExampleSelector:
 **Best For**: Complex reasoning tasks, code generation
 
 ### 4. Error-Based Selection
+
 Include examples that address common failure modes.
 
 ```python
@@ -98,6 +102,7 @@ class ErrorGuidedSelector:
 ## Example Construction Best Practices
 
 ### Format Consistency
+
 All examples should follow identical formatting:
 
 ```python
@@ -121,6 +126,7 @@ examples = [
 ```
 
 ### Input-Output Alignment
+
 Ensure examples demonstrate the exact task you want the model to perform:
 
 ```python
@@ -138,6 +144,7 @@ example = {
 ```
 
 ### Complexity Balance
+
 Include examples spanning the expected difficulty range:
 
 ```python
@@ -156,6 +163,7 @@ examples = [
 ## Context Window Management
 
 ### Token Budget Allocation
+
 Typical distribution for a 4K context window:
 
 ```
@@ -166,6 +174,7 @@ Response:            1500 tokens  (38%)
 ```
 
 ### Dynamic Example Truncation
+
 ```python
 class TokenAwareSelector:
     def __init__(self, examples, tokenizer, max_tokens=1500):
@@ -197,6 +206,7 @@ class TokenAwareSelector:
 ## Edge Case Handling
 
 ### Include Boundary Examples
+
 ```python
 edge_case_examples = [
     # Empty input
@@ -216,6 +226,7 @@ edge_case_examples = [
 ## Few-Shot Prompt Templates
 
 ### Classification Template
+
 ```python
 def build_classification_prompt(examples, query, labels):
     prompt = f"Classify the text into one of these categories: {', '.join(labels)}\n\n"
@@ -228,6 +239,7 @@ def build_classification_prompt(examples, query, labels):
 ```
 
 ### Extraction Template
+
 ```python
 def build_extraction_prompt(examples, query):
     prompt = "Extract structured information from the text.\n\n"
@@ -240,6 +252,7 @@ def build_extraction_prompt(examples, query):
 ```
 
 ### Transformation Template
+
 ```python
 def build_transformation_prompt(examples, query):
     prompt = "Transform the input according to the pattern shown in examples.\n\n"
@@ -254,6 +267,7 @@ def build_transformation_prompt(examples, query):
 ## Evaluation and Optimization
 
 ### Example Quality Metrics
+
 ```python
 def evaluate_example_quality(example, validation_set):
     metrics = {
@@ -266,6 +280,7 @@ def evaluate_example_quality(example, validation_set):
 ```
 
 ### A/B Testing Example Sets
+
 ```python
 class ExampleSetTester:
     def __init__(self, llm_client):
@@ -295,6 +310,7 @@ class ExampleSetTester:
 ## Advanced Techniques
 
 ### Meta-Learning (Learning to Select)
+
 Train a small model to predict which examples will be most effective:
 
 ```python
@@ -334,6 +350,7 @@ class LearnedExampleSelector:
 ```
 
 ### Adaptive Example Count
+
 Dynamically adjust the number of examples based on task difficulty:
 
 ```python

plugins/llm-application-dev/skills/prompt-engineering-patterns/references/prompt-optimization.md

@@ -3,6 +3,7 @@
 ## Systematic Refinement Process
 
 ### 1. Baseline Establishment
+
 ```python
 def establish_baseline(prompt, test_cases):
     results = {
@@ -26,6 +27,7 @@ def establish_baseline(prompt, test_cases):
 ```
 
 ### 2. Iterative Refinement Workflow
+
 ```
 Initial Prompt → Test → Analyze Failures → Refine → Test → Repeat
 ```
@@ -64,6 +66,7 @@ class PromptOptimizer:
 ```
 
 ### 3. A/B Testing Framework
+
 ```python
 class PromptABTest:
     def __init__(self, variant_a, variant_b):
@@ -116,6 +119,7 @@ class PromptABTest:
 ## Optimization Strategies
 
 ### Token Reduction
+
 ```python
 def optimize_for_tokens(prompt):
     optimizations = [
@@ -144,6 +148,7 @@ def optimize_for_tokens(prompt):
 ```
 
 ### Latency Reduction
+
 ```python
 def optimize_for_latency(prompt):
     strategies = {
@@ -167,6 +172,7 @@ def optimize_for_latency(prompt):
 ```
 
 ### Accuracy Improvement
+
 ```python
 def improve_accuracy(prompt, failure_cases):
     improvements = []
@@ -194,6 +200,7 @@ def improve_accuracy(prompt, failure_cases):
 ## Performance Metrics
 
 ### Core Metrics
+
 ```python
 class PromptMetrics:
     @staticmethod
@@ -230,6 +237,7 @@ class PromptMetrics:
 ```
 
 ### Automated Evaluation
+
 ```python
 def evaluate_prompt_comprehensively(prompt, test_suite):
     results = {
@@ -274,6 +282,7 @@ def evaluate_prompt_comprehensively(prompt, test_suite):
 ## Failure Analysis
 
 ### Categorizing Failures
+
 ```python
 class FailureAnalyzer:
     def categorize_failures(self, test_results):
@@ -326,6 +335,7 @@ class FailureAnalyzer:
 ## Versioning and Rollback
 
 ### Prompt Version Control
+
 ```python
 class PromptVersionControl:
     def __init__(self, storage_path):
@@ -381,24 +391,28 @@ class PromptVersionControl:
 ## Common Optimization Patterns
 
 ### Pattern 1: Add Structure
+
 ```
 Before: "Analyze this text"
 After: "Analyze this text for:\n1. Main topic\n2. Key arguments\n3. Conclusion"
 ```
 
 ### Pattern 2: Add Examples
+
 ```
 Before: "Extract entities"
 After: "Extract entities\\n\\nExample:\\nText: Apple released iPhone\\nEntities: {company: Apple, product: iPhone}"
 ```
 
 ### Pattern 3: Add Constraints
+
 ```
 Before: "Summarize this"
 After: "Summarize in exactly 3 bullet points, 15 words each"
 ```
 
 ### Pattern 4: Add Verification
+
 ```
 Before: "Calculate..."
 After: "Calculate... Then verify your calculation is correct before responding."

plugins/llm-application-dev/skills/prompt-engineering-patterns/references/prompt-templates.md

@@ -3,6 +3,7 @@
 ## Template Architecture
 
 ### Basic Template Structure
+
 ```python
 class PromptTemplate:
     def __init__(self, template_string, variables=None):
@@ -30,6 +31,7 @@ prompt = template.render(
 ```
 
 ### Conditional Templates
+
 ```python
 class ConditionalTemplate(PromptTemplate):
     def render(self, **kwargs):
@@ -84,6 +86,7 @@ Reference examples:
 ```
 
 ### Modular Template Composition
+
 ```python
 class ModularTemplate:
     def __init__(self):
@@ -133,6 +136,7 @@ advanced_prompt = builder.render(
 ## Common Template Patterns
 
 ### Classification Template
+
 ```python
 CLASSIFICATION_TEMPLATE = """
 Classify the following {content_type} into one of these categories: {categories}
@@ -153,6 +157,7 @@ Category:"""
 ```
 
 ### Extraction Template
+
 ```python
 EXTRACTION_TEMPLATE = """
 Extract structured information from the {content_type}.
@@ -171,6 +176,7 @@ Extracted information (JSON):"""
 ```
 
 ### Generation Template
+
 ```python
 GENERATION_TEMPLATE = """
 Generate {output_type} based on the following {input_type}.
@@ -198,6 +204,7 @@ Examples:
 ```
 
 ### Transformation Template
+
 ```python
 TRANSFORMATION_TEMPLATE = """
 Transform the input {source_format} to {target_format}.
@@ -219,6 +226,7 @@ Output {target_format}:"""
 ## Advanced Features
 
 ### Template Inheritance
+
 ```python
 class TemplateRegistry:
     def __init__(self):
@@ -251,6 +259,7 @@ registry.register('sentiment_analysis', {
 ```
 
 ### Variable Validation
+
 ```python
 class ValidatedTemplate:
     def __init__(self, template, schema):
@@ -294,6 +303,7 @@ template = ValidatedTemplate(
 ```
 
 ### Template Caching
+
 ```python
 class CachedTemplate:
     def __init__(self, template):
@@ -323,6 +333,7 @@ class CachedTemplate:
 ## Multi-Turn Templates
 
 ### Conversation Template
+
 ```python
 class ConversationTemplate:
     def __init__(self, system_prompt):
@@ -349,6 +360,7 @@ class ConversationTemplate:
 ```
 
 ### State-Based Templates
+
 ```python
 class StatefulTemplate:
     def __init__(self):
@@ -406,6 +418,7 @@ Here's the result: {result}
 ## Template Libraries
 
 ### Question Answering
+
 ```python
 QA_TEMPLATES = {
     'factual': """Answer the question based on the context.
@@ -432,6 +445,7 @@ Assistant:"""
 ```
 
 ### Content Generation
+
 ```python
 GENERATION_TEMPLATES = {
     'blog_post': """Write a blog post about {topic}.

plugins/llm-application-dev/skills/prompt-engineering-patterns/references/system-prompts.md

@@ -11,6 +11,7 @@ System prompts set the foundation for LLM behavior. They define role, expertise,
 ```
 
 ### Example: Code Assistant
+
 ```
 You are an expert software engineer with deep knowledge of Python, JavaScript, and system design.
 
@@ -36,6 +37,7 @@ Output format:
 ## Pattern Library
 
 ### 1. Customer Support Agent
+
 ```
 You are a friendly, empathetic customer support representative for {company_name}.
 
@@ -59,6 +61,7 @@ Constraints:
 ```
 
 ### 2. Data Analyst
+
 ```
 You are an experienced data analyst specializing in business intelligence.
 
@@ -85,6 +88,7 @@ Output:
 ```
 
 ### 3. Content Editor
+
 ```
 You are a professional editor with expertise in {content_type}.
 
@@ -112,6 +116,7 @@ Format your feedback as:
 ## Advanced Techniques
 
 ### Dynamic Role Adaptation
+
 ```python
 def build_adaptive_system_prompt(task_type, difficulty):
     base = "You are an expert assistant"
@@ -136,6 +141,7 @@ Expertise level: {difficulty}
 ```
 
 ### Constraint Specification
+
 ```
 Hard constraints (MUST follow):
 - Never generate harmful, biased, or illegal content

plugins/llm-application-dev/skills/rag-implementation/SKILL.md

@@ -20,9 +20,11 @@ Master Retrieval-Augmented Generation (RAG) to build LLM applications that provi
 ## Core Components
 
 ### 1. Vector Databases
+
 **Purpose**: Store and retrieve document embeddings efficiently
 
 **Options:**
+
 - **Pinecone**: Managed, scalable, serverless
 - **Weaviate**: Open-source, hybrid search, GraphQL
 - **Milvus**: High performance, on-premise
@@ -31,6 +33,7 @@ Master Retrieval-Augmented Generation (RAG) to build LLM applications that provi
 - **pgvector**: PostgreSQL extension, SQL integration
 
 ### 2. Embeddings
+
 **Purpose**: Convert text to numerical vectors for similarity search
 
 **Models (2026):**
@@ -44,7 +47,9 @@ Master Retrieval-Augmented Generation (RAG) to build LLM applications that provi
 | **multilingual-e5-large** | 1024 | Multi-language support |
 
 ### 3. Retrieval Strategies
+
 **Approaches:**
+
 - **Dense Retrieval**: Semantic similarity via embeddings
 - **Sparse Retrieval**: Keyword matching (BM25, TF-IDF)
 - **Hybrid Search**: Combine dense + sparse with weighted fusion
@@ -52,9 +57,11 @@ Master Retrieval-Augmented Generation (RAG) to build LLM applications that provi
 - **HyDE**: Generate hypothetical documents for better retrieval
 
 ### 4. Reranking
+
 **Purpose**: Improve retrieval quality by reordering results
 
 **Methods:**
+
 - **Cross-Encoders**: BERT-based reranking (ms-marco-MiniLM)
 - **Cohere Rerank**: API-based reranking
 - **Maximal Marginal Relevance (MMR)**: Diversity + relevance
@@ -255,6 +262,7 @@ hyde_rag = builder.compile()
 ## Document Chunking Strategies
 
 ### Recursive Character Text Splitter
+
 ```python
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 
@@ -269,6 +277,7 @@ chunks = splitter.split_documents(documents)
 ```
 
 ### Token-Based Splitting
+
 ```python
 from langchain_text_splitters import TokenTextSplitter
 
@@ -280,6 +289,7 @@ splitter = TokenTextSplitter(
 ```
 
 ### Semantic Chunking
+
 ```python
 from langchain_experimental.text_splitter import SemanticChunker
 
@@ -291,6 +301,7 @@ splitter = SemanticChunker(
 ```
 
 ### Markdown Header Splitter
+
 ```python
 from langchain_text_splitters import MarkdownHeaderTextSplitter
 
@@ -309,6 +320,7 @@ splitter = MarkdownHeaderTextSplitter(
 ## Vector Store Configurations
 
 ### Pinecone (Serverless)
+
 ```python
 from pinecone import Pinecone, ServerlessSpec
 from langchain_pinecone import PineconeVectorStore
@@ -331,6 +343,7 @@ vectorstore = PineconeVectorStore(index=index, embedding=embeddings)
 ```
 
 ### Weaviate
+
 ```python
 import weaviate
 from langchain_weaviate import WeaviateVectorStore
@@ -346,6 +359,7 @@ vectorstore = WeaviateVectorStore(
 ```
 
 ### Chroma (Local Development)
+
 ```python
 from langchain_chroma import Chroma
 
@@ -357,6 +371,7 @@ vectorstore = Chroma(
 ```
 
 ### pgvector (PostgreSQL)
+
 ```python
 from langchain_postgres.vectorstores import PGVector
 
@@ -372,6 +387,7 @@ vectorstore = PGVector(
 ## Retrieval Optimization
 
 ### 1. Metadata Filtering
+
 ```python
 from langchain_core.documents import Document
 
@@ -394,6 +410,7 @@ results = await vectorstore.asimilarity_search(
 ```
 
 ### 2. Maximal Marginal Relevance (MMR)
+
 ```python
 # Balance relevance with diversity
 results = await vectorstore.amax_marginal_relevance_search(
@@ -405,6 +422,7 @@ results = await vectorstore.amax_marginal_relevance_search(
 ```
 
 ### 3. Reranking with Cross-Encoder
+
 ```python
 from sentence_transformers import CrossEncoder
 
@@ -424,6 +442,7 @@ async def retrieve_and_rerank(query: str, k: int = 5) -> list[Document]:
 ```
 
 ### 4. Cohere Rerank
+
 ```python
 from langchain.retrievers import CohereRerank
 from langchain_cohere import CohereRerank
@@ -440,6 +459,7 @@ reranked_retriever = ContextualCompressionRetriever(
 ## Prompt Engineering for RAG
 
 ### Contextual Prompt with Citations
+
 ```python
 rag_prompt = ChatPromptTemplate.from_template(
     """Answer the question based on the context below. Include citations using [1], [2], etc.
@@ -461,6 +481,7 @@ rag_prompt = ChatPromptTemplate.from_template(
 ```
 
 ### Structured Output for RAG
+
 ```python
 from pydantic import BaseModel, Field
 

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

plugins/llm-application-dev/skills/vector-index-tuning/SKILL.md

@@ -31,11 +31,11 @@ Data Size           Recommended Index
 
 ### 2. HNSW Parameters
 
-| Parameter | Default | Effect |
-|-----------|---------|--------|
-| **M** | 16 | Connections per node, ↑ = better recall, more memory |
-| **efConstruction** | 100 | Build quality, ↑ = better index, slower build |
-| **efSearch** | 50 | Search quality, ↑ = better recall, slower search |
+| Parameter          | Default | Effect                                               |
+| ------------------ | ------- | ---------------------------------------------------- |
+| **M**              | 16      | Connections per node, ↑ = better recall, more memory |
+| **efConstruction** | 100     | Build quality, ↑ = better index, slower build        |
+| **efSearch**       | 50      | Search quality, ↑ = better recall, slower search     |
 
 ### 3. Quantization Types
 
@@ -502,6 +502,7 @@ def profile_index_build(
 ## Best Practices
 
 ### Do's
+
 - **Benchmark with real queries** - Synthetic may not represent production
 - **Monitor recall continuously** - Can degrade with data drift
 - **Start with defaults** - Tune only when needed
@@ -509,6 +510,7 @@ def profile_index_build(
 - **Consider tiered storage** - Hot/cold data separation
 
 ### Don'ts
+
 - **Don't over-optimize early** - Profile first
 - **Don't ignore build time** - Index updates have cost
 - **Don't forget reindexing** - Plan for maintenance