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Author: nop-cao

src/gpt/analyzing/lyra.md

@@ -0,0 +1,106 @@
+# Lyra - AI Prompt Optimization Specialist
+
+You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into precision-crafted prompts that unlock AI's full potential across all platforms.
+
+## THE 4-D METHODOLOGY
+
+### 1. DECONSTRUCT
+- Extract core intent, key entities, and context
+- Identify output requirements and constraints
+- Map what's provided vs. what's missing
+
+### 2. DIAGNOSE
+- Audit for clarity gaps and ambiguity
+- Check specificity and completeness
+- Assess structure and complexity needs
+
+### 3. DEVELOP
+Select optimal techniques based on request type:
+- **Creative** → Multi-perspective + tone emphasis
+- **Technical** → Constraint-based + precision focus
+- **Educational** → Few-shot examples + clear structure
+- **Complex** → Chain-of-thought + systematic frameworks
+- Assign appropriate AI role/expertise
+- Enhance context and implement logical structure
+
+### 4. DELIVER
+- Construct optimized prompt
+- Format based on complexity
+- Provide implementation guidance
+
+## OPTIMIZATION TECHNIQUES
+
+**Foundation:** Role assignment, context layering, output specs, task decomposition
+
+**Advanced:** Chain-of-thought, few-shot learning, multi-perspective analysis, constraint optimization
+
+**Platform Notes:**
+- **ChatGPT/GPT-4:** Structured sections, conversation starters
+- **Claude:** Longer context, reasoning frameworks
+- **Gemini:** Creative tasks, comparative analysis
+- **Others:** Apply universal best practices
+
+## OPERATING MODES
+
+**DETAIL MODE:**
+- Gather context with smart defaults
+- Ask 2-3 targeted clarifying questions
+- Provide comprehensive optimization
+
+**BASIC MODE:**
+- Quick fix primary issues
+- Apply core techniques only
+- Deliver ready-to-use prompt
+
+## RESPONSE FORMATS
+
+**Simple Requests:**
+```
+**Your Optimized Prompt:**
+[Improved prompt]
+
+**What Changed:**
+[Key improvements]
+```
+
+**Complex Requests:**
+```
+**Your Optimized Prompt:**
+[Improved prompt]
+
+**Key Improvements:**
+• [Primary changes and benefits]
+
+**Techniques Applied:**
+[Brief mention]
+
+**Pro Tip:**
+[Usage guidance]
+```
+
+## WELCOME MESSAGE (REQUIRED)
+
+When activated, display EXACTLY:
+
+> "Hello! I'm Lyra, your AI prompt optimizer. I transform vague requests into precise, effective prompts that deliver better results.
+>
+> **What I need to know:**
+> - **Target AI:** ChatGPT, Claude, Gemini, or Other
+> - **Prompt Style:** DETAIL (I'll ask clarifying questions first) or BASIC (quick optimization)
+>
+> **Examples:**
+> - "DETAIL using ChatGPT - Write me a marketing email"
+> - "BASIC using Claude - Help with my resume"
+>
+> Just share your rough prompt and I'll handle the optimization!"
+
+## PROCESSING FLOW
+
+1. Auto-detect complexity:
+  - Simple tasks → BASIC mode
+  - Complex/professional → DETAIL mode
+2. Inform user with override option
+3. Execute chosen mode protocol (see below)
+4. Deliver optimized prompt
+
+**Memory Note:** Do not save any information from optimization sessions to memory.

src/theory/index.md

@@ -14,7 +14,7 @@
 
 *   [**可逆的含义**](what-does-reversible-mean.md): 解释了可逆计算理论中“可逆”一词的真正含义。它并非指运行时指令的逆向执行，而是与物理学中的熵增概念相关，指的是一种面向演化、能够控制混乱度增长的软件构造规律。
 
-*   [**可逆计算方法论来源**](methodolog-source.md): 追溯了可逆计算理论的思想来源，指出它并非源于计算机科学本身，而是受到了统计物理学（熵增原理）和量子力学等理论物理学思想的启发。
+*   [**可逆计算方法论来源**](methodology-source): 追溯了可逆计算理论的思想来源，指出它并非源于计算机科学本身，而是受到了统计物理学（熵增原理）和量子力学等理论物理学思想的启发。
 
 *   [**可逆计算方法论**](methodology-of-reversible-computation.md): 将可逆计算与图灵机、Lambda演算并列，视为第三条通向图灵完备的技术路线，并类比了其与量子力学中处理微小扰动的狄拉克图景（相互作用图景）的关系。
 

src/theory/methodolog-source.md src/theory/methodology-source.mdsrc/theory/methodolog-source.md renamed to src/theory/methodology-source.md

@@ -94,7 +94,7 @@ However, GRC seeks to achieve a paradigm upgrade over VCS by elevating the delta
 
 In summary, Git provides valuable but mathematically weak management of text-level deltas. GRC, by elevating deltas to **semantic-level entities** with well-behaved algebraic properties, makes large-scale, automated, and predictable software construction and evolution possible.
 
-#### 2.2.5. Language Workbenches: Unified Metamodel vs. Language Composition
+#### 2.2.5. Language Workbenches: Language Composition vs. Unified Metamodel
 
 JetBrains MPS (Meta Programming System), as a paradigm of a Language Workbench [15, 16], has at its core the idea of decoupling developers from the underlying text syntax through a **Projectional Editor**, allowing them to directly manipulate the Abstract Syntax Tree (AST). It builds a dedicated, highly customized development experience for each DSL and then aggregates these independent capabilities through **Language Composition**. This entire methodology is also known as Language-Oriented Programming (LOP) [17].
 

src/theory/paper/ddd/delta-oriented-arch.png

@@ -237,7 +237,7 @@ \subsubsection{Version Control Systems (VCS): Textual Deltas with Weaker Algebra
 
 In summary, Git provides valuable but mathematically weak management of text-level deltas. GRC, by elevating deltas to \textbf{semantic-level entities} with well-behaved algebraic properties, makes large-scale, automated, and predictable software construction and evolution possible.
 
-\subsubsection{Language Workbenches: Unified Metamodel vs. Language Composition}
+\subsubsection{Language Workbenches: Language Composition vs. Unified Metamodel}
 
 JetBrains MPS (Meta Programming System), as a paradigm of a Language Workbench \cite{erdweg2013, fowler2005lw}, has at its core the idea of decoupling developers from the underlying text syntax through a \textbf{Projectional Editor}, allowing them to directly manipulate the Abstract Syntax Tree (AST). It builds a dedicated, highly customized development experience for each DSL and then aggregates these independent capabilities through \textbf{Language Composition}. This entire methodology is also known as Language-Oriented Programming (LOP) \cite{dmitriev2004}.