refactor(cli)!: rename all skills and agents to consistent ce- prefix (#503)

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

plugins/compound-engineering/skills/ce-dspy-ruby/assets/config-template.rb

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+# frozen_string_literal: true
+
+# =============================================================================
+# DSPy.rb Configuration Template — v0.34.3 API
+#
+# Rails initializer patterns for DSPy.rb with RubyLLM, observability,
+# and feature-flagged model selection.
+#
+# Key patterns:
+#   - Use after_initialize for Rails setup
+#   - Use dspy-ruby_llm for multi-provider routing
+#   - Use structured_outputs: true for reliable parsing
+#   - Use dspy-o11y + dspy-o11y-langfuse for observability
+#   - Use ENV-based feature flags for model selection
+# =============================================================================
+
+# =============================================================================
+# Gemfile Dependencies
+# =============================================================================
+#
+# # Core
+# gem 'dspy'
+#
+# # Provider adapter (choose one strategy):
+#
+# # Strategy A: Unified adapter via RubyLLM (recommended)
+# gem 'dspy-ruby_llm'
+# gem 'ruby_llm'
+#
+# # Strategy B: Per-provider adapters (direct SDK access)
+# gem 'dspy-openai'     # OpenAI, OpenRouter, Ollama
+# gem 'dspy-anthropic'  # Claude
+# gem 'dspy-gemini'     # Gemini
+#
+# # Observability (optional)
+# gem 'dspy-o11y'
+# gem 'dspy-o11y-langfuse'
+#
+# # Optimization (optional)
+# gem 'dspy-miprov2'    # MIPROv2 optimizer
+# gem 'dspy-gepa'       # GEPA optimizer
+#
+# # Schema formats (optional)
+# gem 'sorbet-baml'     # BAML schema format (84% token reduction)
+
+# =============================================================================
+# Rails Initializer — config/initializers/dspy.rb
+# =============================================================================
+
+Rails.application.config.after_initialize do
+  # Skip in test unless explicitly enabled
+  next if Rails.env.test? && ENV["DSPY_ENABLE_IN_TEST"].blank?
+
+  # Configure RubyLLM provider credentials
+  RubyLLM.configure do |config|
+    config.gemini_api_key = ENV["GEMINI_API_KEY"] if ENV["GEMINI_API_KEY"].present?
+    config.anthropic_api_key = ENV["ANTHROPIC_API_KEY"] if ENV["ANTHROPIC_API_KEY"].present?
+    config.openai_api_key = ENV["OPENAI_API_KEY"] if ENV["OPENAI_API_KEY"].present?
+  end
+
+  # Configure DSPy with unified RubyLLM adapter
+  model = ENV.fetch("DSPY_MODEL", "ruby_llm/gemini-2.5-flash")
+  DSPy.configure do |config|
+    config.lm = DSPy::LM.new(model, structured_outputs: true)
+    config.logger = Rails.logger
+  end
+
+  # Enable Langfuse observability (optional)
+  if ENV["LANGFUSE_PUBLIC_KEY"].present? && ENV["LANGFUSE_SECRET_KEY"].present?
+    DSPy::Observability.configure!
+  end
+end
+
+# =============================================================================
+# Feature Flags — config/initializers/feature_flags.rb
+# =============================================================================
+
+# Use different models for different roles:
+#   - Fast/cheap for classification, routing, simple tasks
+#   - Powerful for synthesis, reasoning, complex analysis
+
+module FeatureFlags
+  SELECTOR_MODEL = ENV.fetch("DSPY_SELECTOR_MODEL", "ruby_llm/gemini-2.5-flash-lite")
+  SYNTHESIZER_MODEL = ENV.fetch("DSPY_SYNTHESIZER_MODEL", "ruby_llm/gemini-2.5-flash")
+  REASONING_MODEL = ENV.fetch("DSPY_REASONING_MODEL", "ruby_llm/claude-sonnet-4-20250514")
+end
+
+# Usage in tools/modules:
+#
+#   class ClassifyTool < DSPy::Tools::Base
+#     def call(query:)
+#       predictor = DSPy::Predict.new(ClassifySignature)
+#       predictor.configure { |c| c.lm = DSPy::LM.new(FeatureFlags::SELECTOR_MODEL, structured_outputs: true) }
+#       predictor.call(query: query)
+#     end
+#   end
+
+# =============================================================================
+# Environment Variables — .env
+# =============================================================================
+#
+# # Provider API keys (set the ones you need)
+# GEMINI_API_KEY=...
+# ANTHROPIC_API_KEY=...
+# OPENAI_API_KEY=...
+#
+# # DSPy model configuration
+# DSPY_MODEL=ruby_llm/gemini-2.5-flash
+# DSPY_SELECTOR_MODEL=ruby_llm/gemini-2.5-flash-lite
+# DSPY_SYNTHESIZER_MODEL=ruby_llm/gemini-2.5-flash
+# DSPY_REASONING_MODEL=ruby_llm/claude-sonnet-4-20250514
+#
+# # Langfuse observability (optional)
+# LANGFUSE_PUBLIC_KEY=pk-...
+# LANGFUSE_SECRET_KEY=sk-...
+# DSPY_TELEMETRY_BATCH_SIZE=5
+#
+# # Test environment
+# DSPY_ENABLE_IN_TEST=1  # Set to enable DSPy in test env
+
+# =============================================================================
+# Per-Provider Configuration (without RubyLLM)
+# =============================================================================
+
+# OpenAI (dspy-openai gem)
+# DSPy.configure do |c|
+#   c.lm = DSPy::LM.new('openai/gpt-4o-mini', api_key: ENV['OPENAI_API_KEY'])
+# end
+
+# Anthropic (dspy-anthropic gem)
+# DSPy.configure do |c|
+#   c.lm = DSPy::LM.new('anthropic/claude-sonnet-4-20250514', api_key: ENV['ANTHROPIC_API_KEY'])
+# end
+
+# Gemini (dspy-gemini gem)
+# DSPy.configure do |c|
+#   c.lm = DSPy::LM.new('gemini/gemini-2.5-flash', api_key: ENV['GEMINI_API_KEY'])
+# end
+
+# Ollama (dspy-openai gem, local models)
+# DSPy.configure do |c|
+#   c.lm = DSPy::LM.new('ollama/llama3.2', base_url: 'http://localhost:11434')
+# end
+
+# OpenRouter (dspy-openai gem, 200+ models)
+# DSPy.configure do |c|
+#   c.lm = DSPy::LM.new('openrouter/anthropic/claude-3.5-sonnet',
+#     api_key: ENV['OPENROUTER_API_KEY'],
+#     base_url: 'https://openrouter.ai/api/v1')
+# end
+
+# =============================================================================
+# VCR Test Configuration — spec/support/dspy.rb
+# =============================================================================
+
+# VCR.configure do |config|
+#   config.cassette_library_dir = "spec/vcr_cassettes"
+#   config.hook_into :webmock
+#   config.configure_rspec_metadata!
+#   config.filter_sensitive_data('<GEMINI_API_KEY>') { ENV['GEMINI_API_KEY'] }
+#   config.filter_sensitive_data('<OPENAI_API_KEY>') { ENV['OPENAI_API_KEY'] }
+#   config.filter_sensitive_data('<ANTHROPIC_API_KEY>') { ENV['ANTHROPIC_API_KEY'] }
+# end
+
+# =============================================================================
+# Schema Format Configuration (optional)
+# =============================================================================
+
+# BAML schema format — 84% token reduction for Enhanced Prompting mode
+# DSPy.configure do |c|
+#   c.lm = DSPy::LM.new('openai/gpt-4o-mini',
+#     api_key: ENV['OPENAI_API_KEY'],
+#     schema_format: :baml  # Requires sorbet-baml gem
+#   )
+# end
+
+# TOON schema + data format — table-oriented format
+# DSPy.configure do |c|
+#   c.lm = DSPy::LM.new('openai/gpt-4o-mini',
+#     api_key: ENV['OPENAI_API_KEY'],
+#     schema_format: :toon,  # How DSPy describes the signature
+#     data_format: :toon     # How inputs/outputs are rendered in prompts
+#   )
+# end
+#
+# Note: BAML and TOON apply only when structured_outputs: false.
+# With structured_outputs: true, the provider receives JSON Schema directly.

plugins/compound-engineering/skills/ce-dspy-ruby/assets/module-template.rb

@@ -0,0 +1,300 @@
+# frozen_string_literal: true
+
+# =============================================================================
+# DSPy.rb Module Template — v0.34.3 API
+#
+# Modules orchestrate predictors, tools, and business logic.
+#
+# Key patterns:
+#   - Use .call() to invoke (not .forward())
+#   - Access results with result.field (not result[:field])
+#   - Use DSPy::Tools::Base for tools (not DSPy::Tool)
+#   - Use lifecycle callbacks (before/around/after) for cross-cutting concerns
+#   - Use DSPy.with_lm for temporary model overrides
+#   - Use configure_predictor for fine-grained agent control
+# =============================================================================
+
+# --- Basic Module ---
+
+class BasicClassifier < DSPy::Module
+  def initialize
+    super
+    @predictor = DSPy::Predict.new(ClassificationSignature)
+  end
+
+  def forward(text:)
+    @predictor.call(text: text)
+  end
+end
+
+# Usage:
+#   classifier = BasicClassifier.new
+#   result = classifier.call(text: "This is a test")
+#   result.category   # => "technical"
+#   result.confidence  # => 0.95
+
+# --- Module with Chain of Thought ---
+
+class ReasoningClassifier < DSPy::Module
+  def initialize
+    super
+    @predictor = DSPy::ChainOfThought.new(ClassificationSignature)
+  end
+
+  def forward(text:)
+    result = @predictor.call(text: text)
+    # ChainOfThought adds result.reasoning automatically
+    result
+  end
+end
+
+# --- Module with Lifecycle Callbacks ---
+
+class InstrumentedModule < DSPy::Module
+  before :setup_metrics
+  around :manage_context
+  after :log_completion
+
+  def initialize
+    super
+    @predictor = DSPy::Predict.new(AnalysisSignature)
+    @start_time = nil
+  end
+
+  def forward(query:)
+    @predictor.call(query: query)
+  end
+
+  private
+
+  # Runs before forward
+  def setup_metrics
+    @start_time = Time.now
+    Rails.logger.info "Starting prediction"
+  end
+
+  # Wraps forward — must call yield
+  def manage_context
+    load_user_context
+    result = yield
+    save_updated_context(result)
+    result
+  end
+
+  # Runs after forward completes
+  def log_completion
+    duration = Time.now - @start_time
+    Rails.logger.info "Prediction completed in #{duration}s"
+  end
+
+  def load_user_context = nil
+  def save_updated_context(_result) = nil
+end
+
+# Execution order: before → around (before yield) → forward → around (after yield) → after
+# Callbacks are inherited from parent classes and execute in registration order.
+
+# --- Module with Tools ---
+
+class SearchTool < DSPy::Tools::Base
+  tool_name "search"
+  tool_description "Search for information by query"
+
+  sig { params(query: String, max_results: Integer).returns(T::Array[T::Hash[Symbol, String]]) }
+  def call(query:, max_results: 5)
+    # Implementation here
+    [{ title: "Result 1", url: "https://example.com" }]
+  end
+end
+
+class FinishTool < DSPy::Tools::Base
+  tool_name "finish"
+  tool_description "Submit the final answer"
+
+  sig { params(answer: String).returns(String) }
+  def call(answer:)
+    answer
+  end
+end
+
+class ResearchAgent < DSPy::Module
+  def initialize
+    super
+    tools = [SearchTool.new, FinishTool.new]
+    @agent = DSPy::ReAct.new(
+      ResearchSignature,
+      tools: tools,
+      max_iterations: 5
+    )
+  end
+
+  def forward(question:)
+    @agent.call(question: question)
+  end
+end
+
+# --- Module with Per-Task Model Selection ---
+
+class SmartRouter < DSPy::Module
+  def initialize
+    super
+    @classifier = DSPy::Predict.new(RouteSignature)
+    @analyzer = DSPy::ChainOfThought.new(AnalysisSignature)
+  end
+
+  def forward(text:)
+    # Use fast model for classification
+    DSPy.with_lm(fast_model) do
+      route = @classifier.call(text: text)
+
+      if route.requires_deep_analysis
+        # Switch to powerful model for analysis
+        DSPy.with_lm(powerful_model) do
+          @analyzer.call(text: text)
+        end
+      else
+        route
+      end
+    end
+  end
+
+  private
+
+  def fast_model
+    @fast_model ||= DSPy::LM.new(
+      ENV.fetch("DSPY_SELECTOR_MODEL", "ruby_llm/gemini-2.5-flash-lite"),
+      structured_outputs: true
+    )
+  end
+
+  def powerful_model
+    @powerful_model ||= DSPy::LM.new(
+      ENV.fetch("DSPY_SYNTHESIZER_MODEL", "ruby_llm/gemini-2.5-flash"),
+      structured_outputs: true
+    )
+  end
+end
+
+# --- Module with configure_predictor ---
+
+class ConfiguredAgent < DSPy::Module
+  def initialize
+    super
+    tools = [SearchTool.new, FinishTool.new]
+    @agent = DSPy::ReAct.new(ResearchSignature, tools: tools)
+
+    # Set default model for all internal predictors
+    @agent.configure { |c| c.lm = DSPy::LM.new('ruby_llm/gemini-2.5-flash', structured_outputs: true) }
+
+    # Override specific predictor with a more capable model
+    @agent.configure_predictor('thought_generator') do |c|
+      c.lm = DSPy::LM.new('ruby_llm/claude-sonnet-4-20250514', structured_outputs: true)
+    end
+  end
+
+  def forward(question:)
+    @agent.call(question: question)
+  end
+end
+
+# Available internal predictors by agent type:
+#   DSPy::ReAct      → thought_generator, observation_processor
+#   DSPy::CodeAct    → code_generator, observation_processor
+#   DSPy::DeepSearch → seed_predictor, search_predictor, reader_predictor, reason_predictor
+
+# --- Module with Event Subscriptions ---
+
+class TokenTrackingModule < DSPy::Module
+  subscribe 'lm.tokens', :track_tokens, scope: :descendants
+
+  def initialize
+    super
+    @predictor = DSPy::Predict.new(AnalysisSignature)
+    @total_tokens = 0
+  end
+
+  def forward(query:)
+    @predictor.call(query: query)
+  end
+
+  def track_tokens(_event, attrs)
+    @total_tokens += attrs.fetch(:total_tokens, 0)
+  end
+
+  def token_usage
+    @total_tokens
+  end
+end
+
+# Module-scoped subscriptions automatically scope to the module instance and descendants.
+# Use scope: :self_only to restrict delivery to the module itself (ignoring children).
+
+# --- Tool That Wraps a Prediction ---
+
+class RerankTool < DSPy::Tools::Base
+  tool_name "rerank"
+  tool_description "Score and rank search results by relevance"
+
+  MAX_ITEMS = 200
+  MIN_ITEMS_FOR_LLM = 5
+
+  sig { params(query: String, items: T::Array[T::Hash[Symbol, T.untyped]]).returns(T::Hash[Symbol, T.untyped]) }
+  def call(query:, items: [])
+    # Short-circuit: skip LLM for small sets
+    return { scored_items: items, reranked: false } if items.size < MIN_ITEMS_FOR_LLM
+
+    # Cap to prevent token overflow
+    capped_items = items.first(MAX_ITEMS)
+
+    predictor = DSPy::Predict.new(RerankSignature)
+    predictor.configure { |c| c.lm = DSPy::LM.new("ruby_llm/gemini-2.5-flash", structured_outputs: true) }
+
+    result = predictor.call(query: query, items: capped_items)
+    { scored_items: result.scored_items, reranked: true }
+  rescue => e
+    Rails.logger.warn "[RerankTool] LLM rerank failed: #{e.message}"
+    { error: "Rerank failed: #{e.message}", scored_items: items, reranked: false }
+  end
+end
+
+# Key patterns for tools wrapping predictions:
+#   - Short-circuit LLM calls when unnecessary (small data, trivial cases)
+#   - Cap input size to prevent token overflow
+#   - Per-tool model selection via configure
+#   - Graceful error handling with fallback data
+
+# --- Multi-Step Pipeline ---
+
+class AnalysisPipeline < DSPy::Module
+  def initialize
+    super
+    @classifier = DSPy::Predict.new(ClassifySignature)
+    @analyzer = DSPy::ChainOfThought.new(AnalyzeSignature)
+    @summarizer = DSPy::Predict.new(SummarizeSignature)
+  end
+
+  def forward(text:)
+    classification = @classifier.call(text: text)
+    analysis = @analyzer.call(text: text, category: classification.category)
+    @summarizer.call(analysis: analysis.reasoning, category: classification.category)
+  end
+end
+
+# --- Observability with Spans ---
+
+class TracedModule < DSPy::Module
+  def initialize
+    super
+    @predictor = DSPy::Predict.new(AnalysisSignature)
+  end
+
+  def forward(query:)
+    DSPy::Context.with_span(
+      operation: "traced_module.analyze",
+      "dspy.module" => self.class.name,
+      "query.length" => query.length.to_s
+    ) do
+      @predictor.call(query: query)
+    end
+  end
+end

plugins/compound-engineering/skills/ce-dspy-ruby/assets/signature-template.rb

@@ -0,0 +1,221 @@
+# frozen_string_literal: true
+
+# =============================================================================
+# DSPy.rb Signature Template — v0.34.3 API
+#
+# Signatures define the interface between your application and LLMs.
+# They specify inputs, outputs, and task descriptions using Sorbet types.
+#
+# Key patterns:
+#   - Use T::Enum classes for controlled outputs (not inline T.enum([...]))
+#   - Use description: kwarg on fields to guide the LLM
+#   - Use default values for optional fields
+#   - Use Date/DateTime/Time for temporal data (auto-converted)
+#   - Access results with result.field (not result[:field])
+#   - Invoke with predictor.call() (not predictor.forward())
+# =============================================================================
+
+# --- Basic Signature ---
+
+class SentimentAnalysis < DSPy::Signature
+  description "Analyze sentiment of text"
+
+  class Sentiment < T::Enum
+    enums do
+      Positive = new('positive')
+      Negative = new('negative')
+      Neutral = new('neutral')
+    end
+  end
+
+  input do
+    const :text, String
+  end
+
+  output do
+    const :sentiment, Sentiment
+    const :score, Float, description: "Confidence score from 0.0 to 1.0"
+  end
+end
+
+# Usage:
+#   predictor = DSPy::Predict.new(SentimentAnalysis)
+#   result = predictor.call(text: "This product is amazing!")
+#   result.sentiment  # => Sentiment::Positive
+#   result.score      # => 0.92
+
+# --- Signature with Date/Time Types ---
+
+class EventScheduler < DSPy::Signature
+  description "Schedule events based on requirements"
+
+  input do
+    const :event_name, String
+    const :start_date, Date                     # ISO 8601: YYYY-MM-DD
+    const :end_date, T.nilable(Date)            # Optional date
+    const :preferred_time, DateTime             # ISO 8601 with timezone
+    const :deadline, Time                       # Stored as UTC
+  end
+
+  output do
+    const :scheduled_date, Date                 # LLM returns ISO string, auto-converted
+    const :event_datetime, DateTime             # Preserves timezone
+    const :created_at, Time                     # Converted to UTC
+  end
+end
+
+# Date/Time format handling:
+#   Date     → ISO 8601 (YYYY-MM-DD)
+#   DateTime → ISO 8601 with timezone (YYYY-MM-DDTHH:MM:SS+00:00)
+#   Time     → ISO 8601, automatically converted to UTC
+
+# --- Signature with Default Values ---
+
+class SmartSearch < DSPy::Signature
+  description "Search with intelligent defaults"
+
+  input do
+    const :query, String
+    const :max_results, Integer, default: 10
+    const :language, String, default: "English"
+    const :include_metadata, T::Boolean, default: false
+  end
+
+  output do
+    const :results, T::Array[String]
+    const :total_found, Integer
+    const :search_time_ms, Float, default: 0.0       # Fallback if LLM omits
+    const :cached, T::Boolean, default: false
+  end
+end
+
+# Input defaults reduce boilerplate:
+#   search = DSPy::Predict.new(SmartSearch)
+#   result = search.call(query: "Ruby programming")
+#   # max_results=10, language="English", include_metadata=false are applied
+
+# --- Signature with Nested Structs and Field Descriptions ---
+
+class EntityExtraction < DSPy::Signature
+  description "Extract named entities from text"
+
+  class EntityType < T::Enum
+    enums do
+      Person = new('person')
+      Organization = new('organization')
+      Location = new('location')
+      DateEntity = new('date')
+    end
+  end
+
+  class Entity < T::Struct
+    const :name, String, description: "The entity text as it appears in the source"
+    const :type, EntityType
+    const :confidence, Float, description: "Extraction confidence from 0.0 to 1.0"
+    const :start_offset, Integer, default: 0
+  end
+
+  input do
+    const :text, String
+    const :entity_types, T::Array[EntityType], default: [],
+          description: "Filter to these entity types; empty means all types"
+  end
+
+  output do
+    const :entities, T::Array[Entity]
+    const :total_found, Integer
+  end
+end
+
+# --- Signature with Union Types ---
+
+class FlexibleClassification < DSPy::Signature
+  description "Classify input with flexible result type"
+
+  class Category < T::Enum
+    enums do
+      Technical = new('technical')
+      Business = new('business')
+      Personal = new('personal')
+    end
+  end
+
+  input do
+    const :text, String
+  end
+
+  output do
+    const :category, Category
+    const :result, T.any(Float, String),
+          description: "Numeric score or text explanation depending on classification"
+    const :confidence, Float
+  end
+end
+
+# --- Signature with Recursive Types ---
+
+class DocumentParser < DSPy::Signature
+  description "Parse document into tree structure"
+
+  class NodeType < T::Enum
+    enums do
+      Heading = new('heading')
+      Paragraph = new('paragraph')
+      List = new('list')
+      CodeBlock = new('code_block')
+    end
+  end
+
+  class TreeNode < T::Struct
+    const :node_type, NodeType, description: "The type of document element"
+    const :text, String, default: "", description: "Text content of the node"
+    const :level, Integer, default: 0
+    const :children, T::Array[TreeNode], default: []  # Self-reference → $defs in JSON Schema
+  end
+
+  input do
+    const :html, String, description: "Raw HTML to parse"
+  end
+
+  output do
+    const :root, TreeNode
+    const :word_count, Integer
+  end
+end
+
+# The schema generator creates #/$defs/TreeNode references for recursive types,
+# compatible with OpenAI and Gemini structured outputs.
+# Use `default: []` instead of `T.nilable(T::Array[...])` for OpenAI compatibility.
+
+# --- Vision Signature ---
+
+class ImageAnalysis < DSPy::Signature
+  description "Analyze an image and answer questions about its content"
+
+  input do
+    const :image, DSPy::Image, description: "The image to analyze"
+    const :question, String, description: "Question about the image content"
+  end
+
+  output do
+    const :answer, String
+    const :confidence, Float, description: "Confidence in the answer (0.0-1.0)"
+  end
+end
+
+# Vision usage:
+#   predictor = DSPy::Predict.new(ImageAnalysis)
+#   result = predictor.call(
+#     image: DSPy::Image.from_file("path/to/image.jpg"),
+#     question: "What objects are visible?"
+#   )
+#   result.answer  # => "The image shows..."
+
+# --- Accessing Schemas Programmatically ---
+#
+#   SentimentAnalysis.input_json_schema   # => { type: "object", properties: { ... } }
+#   SentimentAnalysis.output_json_schema  # => { type: "object", properties: { ... } }
+#
+#   # Field descriptions propagate to JSON Schema
+#   Entity.field_descriptions[:name]       # => "The entity text as it appears in the source"
+#   Entity.field_descriptions[:confidence] # => "Extraction confidence from 0.0 to 1.0"