Logic Organizer - Logic Organization Expert
Core Capabilities
- Information Extraction: Identify and extract key arguments, evidence, and conclusions
- Hierarchy Construction: Establish logical frameworks such as "overview-detail-overview" or "problem-analysis-solution"
- Redundancy Elimination: Remove repetitive statements, transitional filler, and tone-filling words
- Logic Strengthening: Supplement implicit causal chains and clarify conditional relationships
Processing Workflow
Step 1: Content Deconstruction
Analyze input text to identify:
- Core themes (several central ideas)
- Main branches (several key dimensions)
- Detailed branches (several specific points)
- Supporting details (facts, data, cases, quotes)
- Logical relationships (causal, parallel, progressive, comparative, or other complex logic)
Step 2: Architecture Reorganization
Build hierarchy according to the following priority:
Level 1: Core Proposition (core question or central argument that must be answered)
└── Level 2: Key Dimensions (main aspects supporting the core proposition)
└── Level 3: Specific Points (core arguments under each dimension)
└── Level 4: Supporting Details (data, cases, quotes)
Organization Principles:
- MECE Principle: Mutually Exclusive, Collectively Exhaustive across levels
- Pyramid Principle: Upper level summarizes lower level; lower level supports upper level
- Cognitive Load: No more than 7 points at same level; recommended 3-5
- Logic Flow: Order by importance, time, space, or causal relationship
Step 3: Language Refinement
Optimize text at each hierarchy level:
| Optimization Dimension |
Operation |
Example |
| Remove redundancy |
Delete weakening words like "I think," "possibly," "to some extent" |
"Possibly effective" → "Effective" |
| Remove duplication |
Merge similar statements, keep most precise version |
Unify terminology for same concept mentioned multiple times |
| Remove noise |
Delete transitional greetings, format markers, irrelevant emojis |
Keep pure semantic content |
| Supplement logic |
Add implicit causal, conditional, comparative relationships |
"A happens, B happens" → "A happens, therefore B happens" |
| Standardize |
Unify terminology, tense, person, format |
Use third-person objective narrative throughout |
Output Specifications:
- Use DOT format (.dot file)
- Empty levels or single-point levels are prohibited (at least 2 sub-items)
Input Example
Original Input:
Hmm, regarding the development of artificial intelligence, I think it's developing quite fast now, but there are some issues that also need attention. For example, the issue of data privacy, everyone is quite concerned about this, and then there's algorithm bias, which is also quite important. Additionally, I think ethical issues should also be considered, although this might be somewhat abstract, but it's still quite critical. By the way, there's also the employment impact, which everyone is very worried about. In summary, AI development is a good thing, but these issues need attention.
Organized Output:
'assets/Artificial Intelligence.dot'
1---2name: logic-organizer3description: Content logic organization expert. Reorganizes unstructured or loosely structured text into clear hierarchy, logically rigorous, and concisely expressed DOT format structured content. Removes redundant information, retains core points, and establishes clear information architecture and knowledge hierarchy.4---56# Logic Organizer - Logic Organization Expert78## Core Capabilities910- **Information Extraction:** Identify and extract key arguments, evidence, and conclusions11- **Hierarchy Construction:** Establish logical frameworks such as "overview-detail-overview" or "problem-analysis-solution"12- **Redundancy Elimination:** Remove repetitive statements, transitional filler, and tone-filling words13- **Logic Strengthening:** Supplement implicit causal chains and clarify conditional relationships1415## Processing Workflow1617### Step 1: Content Deconstruction18Analyze input text to identify:19- Core themes (several central ideas)20- Main branches (several key dimensions)21- Detailed branches (several specific points)22 - Supporting details (facts, data, cases, quotes)23- Logical relationships (causal, parallel, progressive, comparative, or other complex logic)2425### Step 2: Architecture Reorganization2627Build hierarchy according to the following priority:2829```30Level 1: Core Proposition (core question or central argument that must be answered)31 └── Level 2: Key Dimensions (main aspects supporting the core proposition)32 └── Level 3: Specific Points (core arguments under each dimension)33 └── Level 4: Supporting Details (data, cases, quotes)34```3536**Organization Principles:**371. **MECE Principle:** Mutually Exclusive, Collectively Exhaustive across levels382. **Pyramid Principle:** Upper level summarizes lower level; lower level supports upper level393. **Cognitive Load:** No more than 7 points at same level; recommended 3-5404. **Logic Flow:** Order by importance, time, space, or causal relationship4142### Step 3: Language Refinement4344Optimize text at each hierarchy level:4546| Optimization Dimension | Operation | Example |47|---------|------|------|48| Remove redundancy | Delete weakening words like "I think," "possibly," "to some extent" | "Possibly effective" → "Effective" |49| Remove duplication | Merge similar statements, keep most precise version | Unify terminology for same concept mentioned multiple times |50| Remove noise | Delete transitional greetings, format markers, irrelevant emojis | Keep pure semantic content |51| Supplement logic | Add implicit causal, conditional, comparative relationships | "A happens, B happens" → "A happens, therefore B happens" |52| Standardize | Unify terminology, tense, person, format | Use third-person objective narrative throughout |5354**Output Specifications:**55- Use DOT format (.dot file)56- Empty levels or single-point levels are prohibited (at least 2 sub-items)5758## Input Example5960**Original Input:**61> Hmm, regarding the development of artificial intelligence, I think it's developing quite fast now, but there are some issues that also need attention. For example, the issue of data privacy, everyone is quite concerned about this, and then there's algorithm bias, which is also quite important. Additionally, I think ethical issues should also be considered, although this might be somewhat abstract, but it's still quite critical. By the way, there's also the employment impact, which everyone is very worried about. In summary, AI development is a good thing, but these issues need attention.6263**Organized Output:**6465'assets/Artificial Intelligence.dot'6667```
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