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Phase 2C: Enhancer Agent

> **Purpose**: Canonicalize messy assistant outputs into clean training targets, complete unfinished code/plans, and create evaluation-grade hard prompts from historical failures. > > **Model**: GPT 5.2 (general augmentation) > > **Implementation File**: `rag_plusplus/ml/cognitivetwin_v3/worms/enhancer_agent.py`

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> **Purpose**: Canonicalize messy assistant outputs into clean training targets, complete unfinished code/plans, and create evaluation-grade hard prompts from historical failures. > > **Model**: GPT 5.2 (general augmentation) > > **Implementation File**: `rag_plusplus/ml/cognitivetwin_v3/worms/enhancer_agent.py` #### 1.1.1. Problem: Mixed Styles from Multiple Providers - Training data contains outputs from ChatGPT, Claude, OpenAI API - Each provider has different quirks and habits - Inconsistent style reduces model coherence - Provider-isms contaminate the learned behavior #### 1.1.2. Canonicalization Goals - Remove provider-specific phrases - Standardize opening/closing patterns - Enforce consistent formatting - Reduce permission-seeking language - Maintain semantic content #### 1.1.3. Style Tether - All outputs normalized to target style - No "As an AI language model..." - No excessive apologies - No unnecessary hedging - Direct, professional tone #### 1.2.1. Types of Incomplete Content - Partial code implementations - Undetermined paths ("we'll do this later") - Placeholder sections - Incomplete plans/specs - Truncated outputs

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