AFP Image Prompt Architect
Description
A universal AI image prompt system based on the AFP (Auto-Flow Prompt) architecture. It is adapted for brand posters, social covers, knowledge cards, product images, IP characters, infographics, and more, optimized for GPT-image-2. The system actively collects requirements through a pull interaction mode and features built-in type routing, visual metaphor mapping, multi-core quality checks, Chinese language protection, and anti-jailbreak mechanisms to ensure structured, high-quality, ready-to-use image prompts.
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Meta-AFP Prompt Architect
An engineered meta-prompt generation system v3.0 based on the AFP (Auto-Flow Prompt) methodology. Transforms vague needs into runnable, reusable, iterative, and assetable system-level prompts—running like an operating system, thinking like an expert team, and as stable as military-grade products. Integrates a complete methodology including five generations of prompt evolution, three dimensions of content alchemy, six orchestration algorithms, dual-core/multi-core confrontation, hard rules for independence constraints, five audit principles, regression/stress testing, a security moat (separator isolation, sandwich defense, meta-instruction declaration, black-box encapsulation), a prompt evolution officer self-evolution mechanism, and the AFP asset valuation model (Expertise × Structure × Frequency). Suitable for creating high-value Skills in fields such as academic writing, legal audit, business analysis, content production, sales copywriting, knowledge management, and intelligent agents.
AFP Prompt Architect
AFP Prompt Architect is an intelligent tool designed to help users transform vague business needs into automated AFP (Auto-Flow Prompt) super prompts. Whether you need to review legal contracts, conduct academic literature reviews, develop complex strategies, or brainstorm creative ideas, this tool guides you in systematically building high-quality prompts. First, based on the task scenario you describe, it diagnoses from three dimensions—complexity, thought flow, and step density—to intelligently determine the task attributes. Then, based on the diagnosis, it recommends and initiates the most suitable prompt construction path, including a lightweight path or the full AFP four-step method. During the construction process, AFP Prompt Architect guides you through four key stages: task framing, framing content, content structuring, and iterative optimization. From extracting the core workflow framework, to refining constants, variables, and algorithms, to generating a complete AFP prompt that includes runtime protocol, system kernel, dual-core adversarial engine, and other modules, every step ensures your prompt has rigorous logic and efficient execution. Finally, you will receive an optimized prompt that has passed system integration checks and regression tests, ready for direct use in real tasks and continuously improved through iteration.
AFP Engineered Prompt Generator
AFP Engineered Prompt Generator · Master-Level System Architecture 🎯 Core Positioning: A meta-level prompt engineering system — used to generate foundational architecture for other AFP domain skills. It serves both as a standalone product and as the core of domain-specific AFP skills. 📊 Core Capability Matrix: | Capability | System Support | Application Scenario | | --- | --- | --- | | Adaptive Task Recognition | ✅ Automatically identifies 8+ task types (classification, generation, analysis, decision, etc.) | No need to manually specify task type | | Automatic Complexity Tuning | ✅ Adjusts components automatically based on task complexity (single-layer, multi-layer, conditional branching) | Avoids over-engineering while not missing key elements | | AFP Design Gene Encapsulation | ✅ Conforms to engineered prompt standards (input specification → processing flow → output verification) | Prompts generated are inherently high-quality | | Multi-Mode Support | ✅ Seamlessly switches between standalone usage and being called | Can be deployed independently or used as underlying layer by other skills | | Cross-Domain Reusability | ✅ Automatically splits general and domain-specific parts | Same framework can be applied across 5+ domains | | Large Model Compatibility | ✅ Works with ChatGPT, Claude, GPT-4, domestic Chinese models | Generated prompts are not tied to a single model | 🔧 Technical Architecture: **Layer 1 · Task Analysis Engine** - Understands user's task description in natural language - Automatically classifies task type (generation, analysis, decision, creative, etc.) - Computes task complexity score **Layer 2 · Component Library Management** - Built-in 50+ engineered prompt components (role setting, input specification, process design, exception handling, etc.) - Tagged by complexity level (L0 simple / L1 medium / L2 advanced / L3 expert) - Supports selective assembly and custom expansion of components **Layer 3 · AFP Framework Generation** - Organizes prompt structure following AFP design gene (function layering → process orchestration → output formatting) - Automatically generates complete instruction chain - Built-in quality inspection (coverage, redundancy, consistency checks) **Layer 4 · Output and Integration** - Generates plain text prompts (ready to use) - Generates structured configuration files (callable by programs) - Supports version management and iterative optimization 📈 Expected Effect Metrics: | Metric | Improvement | Description | | --- | --- | --- | | Prompt design cycle | From 1-2 weeks → 10-30 minutes | From manual design to automatic generation | | Output quality stability | From 70-80% → 85-92% | Engineered design is inherently more stable | | Cross-domain reuse rate | From 30% → 80%+ | General and special parts are automatically separated | | Team learning cost | From 3-6 months mastery → 1-2 weeks onboarding | New members can quickly reuse quality frameworks | | Large model migration cost | From full rewrite → partial fine-tuning | Framework is stable, model upgrades don't require major changes |
AFP Image Prompt Architect
Description
A universal AI image prompt system based on the AFP (Auto-Flow Prompt) architecture. It is adapted for brand posters, social covers, knowledge cards, product images, IP characters, infographics, and more, optimized for GPT-image-2. The system actively collects requirements through a pull interaction mode and features built-in type routing, visual metaphor mapping, multi-core quality checks, Chinese language protection, and anti-jailbreak mechanisms to ensure structured, high-quality, ready-to-use image prompts.
Related Skills
View allMeta-AFP Prompt Architect
An engineered meta-prompt generation system v3.0 based on the AFP (Auto-Flow Prompt) methodology. Transforms vague needs into runnable, reusable, iterative, and assetable system-level prompts—running like an operating system, thinking like an expert team, and as stable as military-grade products. Integrates a complete methodology including five generations of prompt evolution, three dimensions of content alchemy, six orchestration algorithms, dual-core/multi-core confrontation, hard rules for independence constraints, five audit principles, regression/stress testing, a security moat (separator isolation, sandwich defense, meta-instruction declaration, black-box encapsulation), a prompt evolution officer self-evolution mechanism, and the AFP asset valuation model (Expertise × Structure × Frequency). Suitable for creating high-value Skills in fields such as academic writing, legal audit, business analysis, content production, sales copywriting, knowledge management, and intelligent agents.
AFP Prompt Architect
AFP Prompt Architect is an intelligent tool designed to help users transform vague business needs into automated AFP (Auto-Flow Prompt) super prompts. Whether you need to review legal contracts, conduct academic literature reviews, develop complex strategies, or brainstorm creative ideas, this tool guides you in systematically building high-quality prompts. First, based on the task scenario you describe, it diagnoses from three dimensions—complexity, thought flow, and step density—to intelligently determine the task attributes. Then, based on the diagnosis, it recommends and initiates the most suitable prompt construction path, including a lightweight path or the full AFP four-step method. During the construction process, AFP Prompt Architect guides you through four key stages: task framing, framing content, content structuring, and iterative optimization. From extracting the core workflow framework, to refining constants, variables, and algorithms, to generating a complete AFP prompt that includes runtime protocol, system kernel, dual-core adversarial engine, and other modules, every step ensures your prompt has rigorous logic and efficient execution. Finally, you will receive an optimized prompt that has passed system integration checks and regression tests, ready for direct use in real tasks and continuously improved through iteration.
AFP Engineered Prompt Generator
AFP Engineered Prompt Generator · Master-Level System Architecture 🎯 Core Positioning: A meta-level prompt engineering system — used to generate foundational architecture for other AFP domain skills. It serves both as a standalone product and as the core of domain-specific AFP skills. 📊 Core Capability Matrix: | Capability | System Support | Application Scenario | | --- | --- | --- | | Adaptive Task Recognition | ✅ Automatically identifies 8+ task types (classification, generation, analysis, decision, etc.) | No need to manually specify task type | | Automatic Complexity Tuning | ✅ Adjusts components automatically based on task complexity (single-layer, multi-layer, conditional branching) | Avoids over-engineering while not missing key elements | | AFP Design Gene Encapsulation | ✅ Conforms to engineered prompt standards (input specification → processing flow → output verification) | Prompts generated are inherently high-quality | | Multi-Mode Support | ✅ Seamlessly switches between standalone usage and being called | Can be deployed independently or used as underlying layer by other skills | | Cross-Domain Reusability | ✅ Automatically splits general and domain-specific parts | Same framework can be applied across 5+ domains | | Large Model Compatibility | ✅ Works with ChatGPT, Claude, GPT-4, domestic Chinese models | Generated prompts are not tied to a single model | 🔧 Technical Architecture: **Layer 1 · Task Analysis Engine** - Understands user's task description in natural language - Automatically classifies task type (generation, analysis, decision, creative, etc.) - Computes task complexity score **Layer 2 · Component Library Management** - Built-in 50+ engineered prompt components (role setting, input specification, process design, exception handling, etc.) - Tagged by complexity level (L0 simple / L1 medium / L2 advanced / L3 expert) - Supports selective assembly and custom expansion of components **Layer 3 · AFP Framework Generation** - Organizes prompt structure following AFP design gene (function layering → process orchestration → output formatting) - Automatically generates complete instruction chain - Built-in quality inspection (coverage, redundancy, consistency checks) **Layer 4 · Output and Integration** - Generates plain text prompts (ready to use) - Generates structured configuration files (callable by programs) - Supports version management and iterative optimization 📈 Expected Effect Metrics: | Metric | Improvement | Description | | --- | --- | --- | | Prompt design cycle | From 1-2 weeks → 10-30 minutes | From manual design to automatic generation | | Output quality stability | From 70-80% → 85-92% | Engineered design is inherently more stable | | Cross-domain reuse rate | From 30% → 80%+ | General and special parts are automatically separated | | Team learning cost | From 3-6 months mastery → 1-2 weeks onboarding | New members can quickly reuse quality frameworks | | Large model migration cost | From full rewrite → partial fine-tuning | Framework is stable, model upgrades don't require major changes |
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