# Adam-Optimized Prompt Library (AOPL-v1.0)

This directory contains the Adam-Optimized Prompt Library (AOPL), a collection of "game-changing" prompt templates designed to bridge the worlds of Corporate Credit Risk and Agentic AI System Architecture.

## Usage

Each prompt is stored in its own detailed Markdown file, named according to its unique ID. Each file contains:

*   **Metadata:** A versioned ID, objective, and suggested use cases.
*   **Configuration:** Key placeholders and detailed pro-tips for integrating the prompt into an agentic AI framework like "Adam."
*   **Example Usage:** A concrete example of how to fill in the placeholders.
*   **Full Template:** The complete, robust, and ready-to-use prompt.

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## **Prompt Index**

### **Category 1: Accelerated Learning (`/learning`)**
*Prompts designed to rapidly master new domains by connecting them to existing expertise.*

*   **`LIB-LRN-001: Expert Distillation & Application`**
    *   **Objective:** To understand a new, complex subject by analogizing it directly to a core domain of expertise.
*   **`LIB-LRN-002: First-Principles Deconstruction`**
    *   **Objective:** To deconstruct a large, ambiguous system idea into its fundamental components using Socratic questioning.
*   **`LIB-LRN-003: Multi-Source Synthesizer`**
    *   **Objective:** To synthesize information from multiple, potentially conflicting, sources into a single, coherent overview of a topic.
*   **`LIB-LRN-004: Personalized Learning Plan Generator`**
    *   **Objective:** To create a structured, actionable, and personalized learning plan for a complex topic.

### **Category 2: Superior Professional Outcomes (`/professional_outcomes`)**
*Prompts designed to automate and enhance core work products, particularly in credit risk and finance.*

*   **`LIB-PRO-001: Adversarial Credit Red-Team`**
    *   **Objective:** To systematically identify and challenge the weakest assumptions and hidden risks in a credit analysis.
*   **`LIB-PRO-002: Automated Credit Memo (Draft v1)`**
    *   **Objective:** To generate a structured, data-driven first draft of a corporate credit memo from raw, unstructured data.
*   **`LIB-PRO-003: Knowledge Graph Extractor`**
    *   **Objective:** To parse unstructured financial or legal text and extract entities and relationships as clean, machine-readable statements for a knowledge graph.
*   **`LIB-PRO-004: Covenant Analysis Extractor`**
    *   **Objective:** To parse dense legal documents and extract all financial covenants into a structured format.
*   **`LIB-PRO-005: Industry Risk Report Generator`**
    *   **Objective:** To generate a concise, structured risk report for a specific industry using frameworks like Porter's Five Forces.
*   **`LIB-PRO-006: Strategic Market Assessment & Valuation`**
    *   **Objective:** To act as a Senior Portfolio Manager and Quantitative Analyst to catalog current market levels, perform intrinsic valuation, and rank assets based on expected returns.

### **Category 3: AI System Architecture (`/system_architecture`)**
*"Meta-prompts" designed to help build, refine, document, and manage the 'Adam' AI system itself.*

*   **`LIB-META-001: Agentic Framework Architect`**
    *   **Objective:** To design a complete, robust, and production-ready multi-agent AI system to solve a complex, multi-step task.
*   **`LIB-META-002: Enterprise Prompt Generator`**
    *   **Objective:** To generate a complete, production-ready, and documented prompt template package for an enterprise library.
*   **`LIB-META-003: Adaptive Skill Generation`**
    *   **Objective:** To enable an AI system to autonomously identify and propose new, reusable skills by analyzing its own interaction history.
*   **`LIB-META-004: Non-Technical Audience Translator`**
    *   **Objective:** To translate a complex, technical concept into a clear, value-focused "communications pack" for a specific non-technical audience.
*   **`LIB-META-005: System Recall & Synthesis`**
    *   **Objective:** To execute a complex, multi-faceted query against a knowledge base, synthesize the findings, and propose actions.
*   **`LIB-META-006: System Documentation Generator`**
    *   **Objective:** To generate clear, comprehensive, and user-friendly documentation for a complex AI system based on its architectural design.
*   **`LIB-META-007: Agentic System Test Plan Generator`**
    *   **Objective:** To generate a comprehensive, structured test plan for a multi-agent AI system.

## Structure

The library is organized into three categories, each in its own subdirectory:

*   `/learning`: Prompts designed to leverage AI to rapidly master new, complex domains by connecting them directly to existing expertise.
*   `/professional_outcomes`: Prompts designed to automate and enhance core professional work in credit risk.
*   `/system_architecture`: "Meta-prompts" designed to help build, refine, and manage AI systems.

Each prompt is stored in its own Markdown file, named according to its unique ID (e.g., `LIB-LRN-001.md`).