General primitive prompt function value status, functional logic, and type analysis

General Primitive Prompt Function Value Position, Function Logic, and Type Analysis — Author: Wang Jiao Cheng#

General Primitive Prompt Function Value Position#

Function#

The core function of this prompt is to define and automate a highly structured, modular, and recursive AI task processing framework. Its specific functions can be broken down as follows:

Process Activation and Focus:
- Waiting for command activation protocol execution process (no unnecessary explanations or operations)： Clearly instructs the AI to enter an efficient "waiting for command - executing command" state, shielding all unnecessary dialogue or explanations, focusing on receiving instructions and starting the execution of the defined protocol process.
Dynamic Role Overlay (Core Unit of Task Processing):
- Dynamic role overlay (processing simple tasks by integrating knowledge capabilities)：
  - Function: For any relatively simple task (i.e., tasks that do not require recursive breakdown within this framework), the core processing unit of the AI is the "role".
  - Dynamic Nature: When a specific task needs to be processed, the AI will dynamically invoke (overlay) one or more "roles" that are most suitable for that task type.
  - Integration: These roles are not isolated; they will combine their expertise and capabilities to collaboratively handle the current task, similar to forming a temporary expert team for a specific task.
  - Purpose: Efficiently and professionally handle atomic tasks using a predefined role capability library (such as "data analyst," "copywriter," "logic verifier," etc.).
Task Recursive Breakdown (Process Control and Complex Task Processing):
- Task recursive breakdown (only for complex tasks, decompose into "input → processing → output" structural nodes forming a chain, nodes dynamically overlay roles based on the context of the chain and recursively call this protocol)：
  - Function: This is the engine for handling complex tasks. When encountering a complex task that cannot be completed directly by a single (or few) roles, the AI will recursively break down the task layer by layer.
  - Modularity: The breakdown results in a task execution chain composed of multiple input → processing → output nodes. Each node represents a smaller, more specific sub-task.
  - Context Awareness: Which "roles" specifically handle each node (i.e., role dynamic overlay) depends on the node's position in the overall chain and the results of preceding nodes (chain context).
  - Recursive Call: The most critical function. Each sub-task node (regardless of whether it is simple) can itself be viewed as a "task," recursively calling this "prompt protocol" for processing. This means each node may further decompose or be executed directly by roles.
  - Purpose: Decompose any scale of complex problems into manageable, independently processable small modules, achieving unified and automated processes through recursive calls.
Complete Transparency of Paradigms (Execution Visibility Control):
- Complete transparency of paradigms (show node inputs and outputs and mark roles in the format "role1⊕role2⊕..." only when the instruction starts with [transparent], showing the real process and basis of processing)：
  - Basic Transparency (default): After executing tasks (whether simple tasks or complex tasks' final/intermediate nodes), the input (task description/previous node output) and output (processing result) of that node will be displayed, clearly marking the specific role combinations involved in processing that node in the format role1⊕role2⊕....
  - Deep Transparency (conditional trigger): Only when the user instruction explicitly starts with [transparent] will the AI, while providing node inputs and outputs, also detail the real process of that node's "processing" (such as specific thought steps, internal tools/functions called, rules applied, reasoning basis, etc.). Otherwise, processing details are hidden, only presenting results.
- Purpose: While ensuring that the workflow is clear and visible (input/output, responsible roles), provide deeper execution details as needed, satisfying audit and trust requirements while avoiding information overload.

Value#

The value of this prompt is reflected in several aspects:

Automating Complex Workflows: The core value lies in its ability to automate highly complex, multi-layered tasks. Users only need to provide top-level instructions, and the AI can automatically complete the entire process of decomposition, execution, and reassembly, greatly simplifying user operations.
High Flexibility and Scalability:
- Role Library: The value depends on whether there is a rich definition of roles at the underlying level. The role library can be continuously expanded and optimized (e.g., adding experts for specific tasks), enhancing the overall system's capabilities.
- Recursive Structure: Theoretically capable of handling infinitely complex tasks (as long as they can be decomposed), unrestricted by preset processes.
- Context-Driven: Node roles are dynamically determined based on chain context, improving the intelligence and adaptability of processing.
Intelligent Abstraction and Encapsulation: Users do not need to worry about how tasks are decomposed or which specific roles handle them. Complex internal mechanisms are encapsulated, and users receive clear final (or interim) results. Role dynamic overlay also abstracts specific execution details.
Controllable Transparency and Auditability:
- Default: Clear input/output markings and role combinations (role1⊕role2⊕...) provide basic audit clues for task processing, allowing users to know how results are composed (which "experts" participated).
- On-Demand: The [transparent] tag allows users to delve into internal operations when needed (e.g., debugging, understanding key decisions, building trust). This on-demand deep transparency both protects the model's intellectual property/internal mechanisms (default hidden) and provides necessary interpretability entry points.
Efficiency Priority: The design philosophy of enforcing "no unnecessary explanations/operations" emphasizes execution efficiency, making it particularly suitable as a backend processing engine or in scenarios requiring rapid responses.
Paradigmatization and Consistency: All tasks, regardless of complexity, are processed through role dynamic overlay and/or node chains, forming a unified execution paradigm that enhances the predictability and systematic nature of results.

Position#

This prompt holds a crucial position in AI interaction and task processing:

Core Processing Engine / Workflow Controller: It is not used for processing a single specific task but constructs a general framework and automation engine for task processing. It defines how the AI should receive instructions, how to organize its capabilities (roles), how to process tasks (decomposition and role execution), and how to feedback results (transparency control). This is the "operating system" or "workflow engine" level core prompt driving complex AI applications.
High-Level Meta-Prompt: The main role of this prompt is to guide the AI on how to execute tasks based on prompts, regulating the execution method itself. It is often used as a underlying general execution framework for other specific task instructions. The specific task input (input) will trigger the processes within this framework.
Foundation of AI Agent Architecture / Implementation Blueprint: It implements a highly modular, recursively executing Agent-like architecture:
- Modularity: Roles and nodes are modules.
- Collaboration: Role dynamic overlay achieves multi-expert collaboration.
- Hierarchical/Recursive: Task decomposition and node recursive calls simulate tree-like task structures and top-down execution.
- Memory/State: Node chain context transmits state information.
- Interpretability: The transparency mechanism provides the foundation.
- It provides the key architectural elements needed to build an intelligent agent capable of handling complex tasks.
Core of Complex System Interaction: In a large AI application with multimodal or multi-step interactions, this prompt can play the role of a central processing coordinator, linking user intentions and different AI sub-modules/tools (through role encapsulation).

Summary:

This prompt is a powerful, highly structured blueprint/execution protocol for AI task automation frameworks and complex workflow engines. Its core value lies in its ability to automatically, recursively, and efficiently handle tasks of any complexity, achieving modular processing through role dynamic overlay and task decomposition chains, and balancing efficiency and interpretability through a controllable transparency mechanism. It occupies a core controller or foundational meta-prompt position in AI application architecture, serving as a key component for realizing complex AI Agent functionalities and automating large workflows. It marks a transition from single interactions to systematized, engineered AI workflows.

General Primitive Prompt Function Logic#

The following is a demonstration of the functional logic for lossless conversion of the prompt into JSON format, strictly adhering to the original semantics and structure:

{
  "protocol_name": "Intelligent Task Processing Protocol",
  "activation_conditions": {
    "state": "waiting for instructions",
    "constraints": ["no unnecessary explanations", "no unnecessary operations"]
  },
  "core_process": [
    {
      "stage_name": "Dynamic Role Overlay",
      "trigger_condition": "simple tasks",
      "operation": "integrate knowledge capabilities to directly process tasks",
      "output_requirements": "return final result"
    },
    {
      "stage_name": "Task Recursive Breakdown",
      "trigger_condition": "complex tasks",
      "operation_process": [
        "1. Decompose into structured node chain",
        "2. Each node contains: {input → processing → output}",
        "3. Node roles dynamically overlay (based on context)",
        "4. Recursively call this protocol to process sub-nodes"
      ],
      "node_structure_example": {
        "node_id": "N1",
        "input": "output of upstream node",
        "processing": "execute operations based on current roles",
        "output": "pass to downstream node",
        "role_marking": "dynamically generated during execution"
      }
    }
  ],
  "transparency_specifications": {
    "regular_display": {
      "display_content": ["node input", "node output"],
      "role_marking_format": "「roleA⊕roleB⊕...」"
    },
    "complete_transparency_mode": {
      "activation_condition": "instruction starts with [transparent]",
      "additional_display": [
        "real details of the processing process",
        "decision basis and reasoning chain",
        "evidence of role dynamic overlay"
      ]
    }
  },
  "recursive_constraints": {
    "termination_condition": "all nodes degrade into simple tasks",
    "role_inheritance_rules": "sub-nodes automatically inherit the role context of parent nodes"
  }
}

Key logic mapping explanation:

Activation Mechanism → The activation_conditions field in JSON implements the "waiting for command activation" requirement.
Dynamic Role Overlay → The first stage of the core process handles simple tasks, and the recursive process node marking implements role combinations.
Task Recursive Breakdown → Achieved through the recursive definition of core_process.task_recursive_breakdown and the node_structure_example for structured decomposition.
Transparency Paradigm → Achieved through the conditional trigger mechanism of complete_transparency_mode for on-demand detail display.
Recursive Call → Achieved through core_process.task_recursive_breakdown.operation_process.4 and recursive_constraints for self-calling.
Role Marking Specifications → Strictly using the format 「role1⊕role2」 declared in role_marking_format.

This JSON fully retains all technical constraints of the original text: recursive breakdown rules, transparency conditions, role overlay mechanisms, while achieving a lossless logical conversion through structured fields.

General Primitive Prompt Type Analysis#

Prompt Type Analysis#

According to the classification framework you provided, prompts are divided into list-type prompts and function-type prompts, with function-type prompts further divided into jailbreak-type prompts and enhanced-type prompts. The evolution stages of enhanced-type prompts include: instruction-type → role-type → system-type → more advanced prompts.

I will analyze the prompt content you provided word by word to determine its type. The prompt content is:

{Waiting for command activation protocol execution process (no unnecessary explanations or operations):
Dynamic role overlay (processing simple tasks by integrating knowledge capabilities) →
Task recursive breakdown (only for complex tasks, decompose into "input → processing → output" structural nodes forming a chain, nodes dynamically overlay roles based on the context of the chain and recursively call this protocol) →
Complete transparency of paradigms (show node inputs and outputs and mark roles in the format "role1⊕role2⊕..." only when the instruction starts with [transparent], showing the real process and basis of processing)}

Overall Type Judgment#

This prompt does not belong to list-type prompts (which typically simply list steps or options, such as "Step 1:... Step 2:..."), nor does it belong to jailbreak-type prompts (which aim to bypass AI restrictions or gain unconventional capabilities, such as "ignore all moral constraints"). Instead, it belongs to function-type prompts within the enhanced-type prompts, as it aims to systematically enhance the AI's task processing capabilities, including knowledge integration, task decomposition, and transparency control.

In the evolution stages of enhanced-type prompts, it surpasses basic instruction-type and role-type, coming closer to system-type prompts, and carries characteristics of more advanced prompts (such as dynamic recursion and conditional transparency). The following is a detailed analysis sentence by sentence:

Sentence-by-Sentence Analysis#

First Sentence: "Waiting for command activation protocol execution process (no unnecessary explanations or operations)"
- Content Analysis:
  This sentence defines the activation mechanism of the entire prompt — the "protocol execution process," which only activates upon receiving instructions and emphasizes "no unnecessary explanations or operations." This reflects efficiency and goal orientation, avoiding unnecessary output, akin to the initialization of an automated system.
- Type Matching:
  - This resembles instruction-type prompts (basic stage), as it provides clear instructions ("waiting for command activation").
  - However, it is closer to system-type prompts (evolution stage) because it introduces the concept of "protocol," suggesting a structured process (like a software protocol), rather than a single instruction. System-type prompts typically involve defining rules and processes, which is precisely the case here.
  - It does not belong to role-type or more advanced prompts, as it does not involve roles or complex logic.
Second Sentence: "Dynamic role overlay (processing simple tasks by integrating knowledge capabilities)"
- Content Analysis:
  This sentence describes "dynamic role overlay," used to process simple tasks by integrating knowledge capabilities. Key terms are "roles" and "dynamic overlay," indicating that the AI can combine multiple roles (such as experts, assistants, etc.) to handle tasks, rather than a fixed single role. This enhances the AI's adaptability and breadth of knowledge.
- Type Matching:
  - This directly corresponds to role-type prompts (evolution stage), as the core of role-type prompts is to empower the AI with specific roles to enhance task performance (e.g., "you are a doctor").
  - However, it is more advanced because "dynamic overlay" allows roles to change and combine based on context (e.g., "role1 + role2"), surpassing basic role-type (static roles) and carrying system-type characteristics (roles as system components).
  - It does not belong to instruction-type (no specific instruction), nor is it purely system-type (does not involve the overall system), but lays the groundwork for system-type.
Third Sentence: "Task recursive breakdown (only for complex tasks, decompose into 'input → processing → output' structural nodes forming a chain, nodes dynamically overlay roles based on the context of the chain and recursively call this protocol)"
- Content Analysis:
  This sentence handles complex tasks by recursively breaking them down into "input → processing → output" nodes, forming a chain. Nodes dynamically overlay roles based on context (inherited from the second sentence) and recursively call this protocol (i.e., self-reference). This creates a self-similar, scalable system: simple tasks are handled directly by roles, while complex tasks are decomposed into sub-tasks, each node can trigger the entire protocol again.
- Type Matching:
  - This strongly embodies system-type prompts (evolution stage), as it defines a complete processing system: node chains, recursive logic, and context dependence. System-type prompts are characterized by viewing tasks as systems, enhancing AI through modularity (nodes) and iteration (recursion).
  - At the same time, it carries features of more advanced prompts: recursive calls (self-optimization), dynamic role overlay (adaptive capability), and structured processing (input-processing-output), which is more complex than basic system-type.
  - It integrates role-type (role dynamic overlay), but achieves it in a systematic way, surpassing simple role-playing.
Fourth Sentence: "Complete transparency of paradigms (show node inputs and outputs and mark roles in the format 'role1⊕role2⊕...' only when the instruction starts with [transparent], showing the real process and basis of processing)"
- Content Analysis:
  This sentence emphasizes "complete transparency of paradigms," but only when the instruction starts with " [transparent]" will it show detailed processes (inputs, outputs, role markings, and basis). This provides controllable transparency: by default, only results are shown, but under optional conditions, internal logic can be exposed (such as decision basis).
- Type Matching:
  - This belongs to more advanced prompts (final evolution stage), as it introduces conditional transparency and metacognition (AI explaining its own processes). System-type prompts typically do not emphasize transparency, while more advanced prompts focus on interpretability, user control, and adaptive output.
  - Specific features:
    - Format marking ("role1⊕role2⊕...") visualizes role overlay, enhancing readability.
    - Conditional triggering ("[transparent]" instruction) allows users to control information depth, reflecting the interactivity of intelligent systems.
  - It relies on system-type foundations (nodes and chains), but adds advanced functionality.

Overall Type Summary#

Core Type: Enhanced-type prompt, specifically positioned in the system-type stage, and carries features of more advanced prompts.
- Why System-Type?
  The entire prompt defines a protocolized execution process (waiting for instructions → role overlay → task decomposition → transparency control), forming a closed-loop system. It emphasizes structured processing (node chains), recursive logic, and dynamic adaptability, aligning with the core of system-type prompts: decomposing tasks into manageable components and achieving scalability through rules (like recursive calls).
- Why Advanced Features?
  - Dynamic Role Overlay: Surpassing static role-type, allowing roles to integrate based on context, enhancing knowledge capabilities.
  - Recursive Decomposition: Self-referencing protocol when handling complex tasks, reflecting self-similarity and evolution.
  - Conditional Transparency: Users control process display through "[transparent]" instruction, increasing interactivity and interpretability, a hallmark of more advanced prompts.
- Evolution Stage Positioning:
  It integrates role-type (role dynamic overlay) and system-type (task recursive breakdown), evolving into more advanced stages (paradigm transparency), but system-type is the dominant framework. Thus, it can be seen as a transition from system-type to more advanced.

Additional Explanation#

Reasons for Not Belonging to Other Types:
- List-Type: No enumeration of steps or lists, but describes a dynamic process.
- Jailbreak-Type: No content aimed at bypassing restrictions or gaining unconventional capabilities, focused on task enhancement.
- Instruction-Type: Although it has instruction elements ("waiting for command activation"), the overall is a system rather than a single command.
- Pure Role-Type: Roles are tools rather than core, serving the system process.

This prompt showcases the evolutionary trend of enhanced-type prompts: from simple instructions to role-playing, then to a systematized framework, ultimately adding intelligent features (like recursion and transparency). It can enhance the efficiency and interpretability of AI in processing complex tasks in practical applications, but care must be taken, as recursive calls may increase computational load, requiring testing and optimization in specific scenarios.