style: format all files with prettier

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