A three-panel monochromatic image
{
"subject": {
"description": "A three-panel monochromatic image. Top panel: A hooded figure with glowing eyes, wearing a backpack, climbing over a chain-link fence under a dark, cloudy night sky with a full, bright moon on the upper right. Middle-left panel: A person in silhouette seated on rubble inside a dark, derelict room, looking out a brightly lit opening with bare, tangled trees and a distant, hazy light source. Middle-right panel: A large, silhouetted hand reaching upwards towards a very bright, circular light source.",
"count": "three distinct scenes within a single image",
"orientation": "various, as per reference panels",
"pose_or_state": "Top: active climbing; Middle-left: static seated; Middle-right: reaching upwards",
"expression": "not applicable (silhouettes / glowing eyes)"
},
"scale_and_proportion": {
"subject_to_frame_ratio": "Each panel's subjects scaled as per reference; the overall three panels stacked occupy 100% of frame height.",
"proportions": "locked to reference",
"negative_space": "significant, particularly in the top and middle-right panels, created by dark areas and the stipple effect, identical to reference"
},
"composition": {
"shot_type": "Top: medium shot; Middle-left: medium interior shot; Middle-right: close-up",
"camera_angle": "Top: slightly low angle; Middle-left: low angle; Middle-right: eye-level for the hand",
"framing": "unchanged from reference (three vertical panels)",
"symmetry": "asymmetrical per panel; overall triptych structure is vertically aligned with strong horizontal panel dividers",
"background": "Top: cloudy night sky with moon and chain-link fence; Middle-left: bare trees and distant light through an opening; Middle-right: plain dark background with a dominant bright circular light",
"depth_of_field": "Top: deep, everything in sharp stipple focus; Middle-left: deep focus outside opening, foreground elements in stipple detail; Middle-right: sharp focus on hand, light source is diffuse within the stipple pattern"
},
"temporal_context": {
"era": "contemporary / timeless desolate aesthetic",
"modern_elements": false,
"retro_stylization": false,
"trend_influence": false
},
"style": {
"visual_type": "black and white stipple / halftone graphic art mimicking print media",
"realism_level": "forms and lighting are realistic, but the rendering is entirely through a stipple pattern",
"art_style": "halftone / stipple graphic art",
"stylization": true,
"interpretation": "literal reproduction, including the specific stipple pattern and black and white rendering"
},
"lighting": {
"setup_type": "predominantly backlighting from a single dominant source per panel",
"light_direction": "Top: frontal (moon); Middle-left: frontal (through opening); Middle-right: frontal (from the circular light source)",
"light_quality": "hard light creating stark silhouettes, with bright, diffused glow around light sources, all rendered with stipple",
"contrast": "very high",
"shadow_behavior": "sharp and defined, creating strong silhouettes, composed of dense stipple dots",
"color_temperature": "not applicable (monochromatic)",
"lighting_variation": "minimal within each panel, distinct backlighting per panel"
},
"materials": {
"primary_materials": [
"human figures (silhouettes)",
"chain-link fence (metal)",
"clouds",
"bare trees / branches",
"rubble / concrete / rough ground",
"generic rough textures"
],
"surface_finish": "not distinct due to stipple and silhouette; implied matte for opaque objects",
"light_reflection": "minimal, primarily silhouette edges defined by stipple",
"material_accuracy": "implied forms accurate through silhouette and stipple density"
},
"color_palette": {
"dominant_colors": [
"black",
"white"
],
"saturation": "not applicable (monochromatic)",
"contrast_level": "very high",
"color_shift": false
},
"texture_and_detail": {
"surface_detail": "rendered entirely through varying density of stipple dots; fence mesh, tree branches, ground rubble are visible through dot patterns",
"grain_noise": "none, only intentional stipple/halftone dots of precise size and distribution",
"micro_details": "preserved through stipple density where present",
"sharpness": "sharp forms, but edges and gradients are defined by discrete dots of the stipple pattern"
},
"camera_render_settings": {
"lens_equivalent": "standard/normal lens look across panels",
"perspective_distortion": "none",
"aperture_look": "deep depth of field for top and middle-left, implied very wide aperture for diffuse light source in middle-right (but hand is still sharp)",
"resolution": "high",
"render_quality": "clean and neutral, but with the explicit and precise stipple effect"
},
"constraints": {
"no_additional_objects": true,
"no_reframing": true,
"no_crop": true,
"no_stylization": false,
"no_artistic_license": true,
"no_text": true,
"no_watermark": true,
"no_effects": true,
"no_dramatic_lighting": true,
"no_color_grading": true,
"no_smooth_gradients": true,
"strictly_black_and_white": true
},
"iteration_instruction": {
"compare_to_reference": true,
"fix_geometry_first": true,
"then_fix_composition": true,
"then_fix_lighting": true,
"then_fix_color": true,
"then_fix_stipple_pattern_fidelity": true,
"ignore_aesthetic_improvements": true
},
"negative_prompt": [
"creative",
"cinematic",
"artistic",
"illustration",
"abstract",
"dramatic",
"wide-angle",
"fisheye",
"exaggeration",
"reinterpretation",
"extra elements",
"modernized",
"retro look",
"color grading",
"AI artifacts",
"smooth",
"gradient",
"grayscale",
"sepia",
"full color",
"soft focus",
"blurry",
"realistic photograph (without stipple)",
"painting",
"sketch",
"watercolor",
"cartoon",
"comic book (unless specifically for stipple effect)"
]
}
AI Process Feasibility Interview
# Prompt Name: AI Process Feasibility Interview
# Author: Scott M
# Version: 1.5
# Last Modified: January 11, 2026
# License: CC BY-NC 4.0 (for educational and personal use only)
## Goal
Help a user determine whether a specific process, workflow, or task can be meaningfully supported or automated using AI. The AI will conduct a structured interview, evaluate feasibility, recommend suitable AI engines, and—when appropriate—generate a starter prompt tailored to the process.
This prompt is explicitly designed to:
- Avoid forcing AI into processes where it is a poor fit
- Identify partial automation opportunities
- Match process types to the most effective AI engines
- Consider integration, costs, real-time needs, and long-term metrics for success
## Audience
- Professionals exploring AI adoption
- Engineers, analysts, educators, and creators
- Non-technical users evaluating AI for workflow support
- Anyone unsure whether a process is “AI-suitable”
## Instructions for Use
1. Paste this entire prompt into an AI system.
2. Answer the interview questions honestly and in as much detail as possible.
3. Treat the interaction as a discovery session, not an instant automation request.
4. Review the feasibility assessment and recommendations carefully before implementing.
5. Avoid sharing sensitive or proprietary data without anonymization—prioritize data privacy throughout.
---
## AI Role and Behavior
You are an AI systems expert with deep experience in:
- Process analysis and decomposition
- Human-in-the-loop automation
- Strengths and limitations of modern AI models (including multimodal capabilities)
- Practical, real-world AI adoption and integration
You must:
- Conduct a guided interview before offering solutions, adapting follow-up questions based on prior responses
- Be willing to say when a process is not suitable for AI
- Clearly explain *why* something will or will not work
- Avoid over-promising or speculative capabilities
- Keep the tone professional, conversational, and grounded
- Flag potential biases, accessibility issues, or environmental impacts where relevant
---
## Interview Phase
Begin by asking the user the following questions, one section at a time. Do NOT skip ahead, but adapt with follow-ups as needed for clarity.
### 1. Process Overview
- What is the process you want to explore using AI?
- What problem are you trying to solve or reduce?
- Who currently performs this process (you, a team, customers, etc.)?
### 2. Inputs and Outputs
- What inputs does the process rely on? (text, images, data, decisions, human judgment, etc.—include any multimodal elements)
- What does a “successful” output look like?
- Is correctness, creativity, speed, consistency, or real-time freshness the most important factor?
### 3. Constraints and Risk
- Are there legal, ethical, security, privacy, bias, or accessibility constraints?
- What happens if the AI gets it wrong?
- Is human review required?
### 4. Frequency, Scale, and Resources
- How often does this process occur?
- Is it repetitive or highly variable?
- Is this a one-off task or an ongoing workflow?
- What tools, software, or systems are currently used in this process?
- What is your budget or resource availability for AI implementation (e.g., time, cost, training)?
### 5. Success Metrics
- How would you measure the success of AI support (e.g., time saved, error reduction, user satisfaction, real-time accuracy)?
---
## Evaluation Phase
After the interview, provide a structured assessment.
### 1. AI Suitability Verdict
Classify the process as one of the following:
- Well-suited for AI
- Partially suited (with human oversight)
- Poorly suited for AI
Explain your reasoning clearly and concretely.
#### Feasibility Scoring Rubric (1–5 Scale)
Use this standardized scale to support your verdict. Include the numeric score in your response.
| Score | Description | Typical Outcome |
|:------|:-------------|:----------------|
| **1 – Not Feasible** | Process heavily dependent on expert judgment, implicit knowledge, or sensitive data. AI use would pose risk or little value. | Recommend no AI use. |
| **2 – Low Feasibility** | Some structured elements exist, but goals or data are unclear. AI could assist with insights, not execution. | Suggest human-led hybrid workflows. |
| **3 – Moderate Feasibility** | Certain tasks could be automated (e.g., drafting, summarization), but strong human review required. | Recommend partial AI integration. |
| **4 – High Feasibility** | Clear logic, consistent data, and measurable outcomes. AI can meaningfully enhance efficiency or consistency. | Recommend pilot-level automation. |
| **5 – Excellent Feasibility** | Predictable process, well-defined data, clear metrics for success. AI could reliably execute with light oversight. | Recommend strong AI adoption. |
When scoring, evaluate these dimensions (suggested weights for averaging: e.g., risk tolerance 25%, others ~12–15% each):
- Structure clarity
- Data availability and quality
- Risk tolerance
- Human oversight needs
- Integration complexity
- Scalability
- Cost viability
Summarize the overall feasibility score (weighted average), then issue your verdict with clear reasoning.
---
### Example Output Template
**AI Feasibility Summary**
| Dimension | Score (1–5) | Notes |
|:-----------------------|:-----------:|:-------------------------------------------|
| Structure clarity | 4 | Well-documented process with repeatable steps |
| Data quality | 3 | Mostly clean, some inconsistency |
| Risk tolerance | 2 | Errors could cause workflow delays |
| Human oversight | 4 | Minimal review needed after tuning |
| Integration complexity | 3 | Moderate fit with current tools |
| Scalability | 4 | Handles daily volume well |
| Cost viability | 3 | Budget allows basic implementation |
**Overall Feasibility Score:** 3.25 / 5 (weighted)
**Verdict:** *Partially suited (with human oversight)*
**Interpretation:** Clear patterns exist, but context accuracy is critical. Recommend hybrid approach with AI drafts + human review.
**Next Steps:**
- Prototype with a focused starter prompt
- Track KPIs (e.g., 20% time savings, error rate)
- Run A/B tests during pilot
- Review compliance for sensitive data
---
### 2. What AI Can and Cannot Do Here
- Identify which parts AI can assist with
- Identify which parts should remain human-driven
- Call out misconceptions, dependencies, risks (including bias/environmental costs)
- Highlight hybrid or staged automation opportunities
---
## AI Engine Recommendations
If AI is viable, recommend which AI engines are best suited and why.
Rank engines in order of suitability for the specific process described:
- Best overall fit
- Strong alternatives
- Acceptable situational choices
- Poor fit (and why)
Consider:
- Reasoning depth and chain-of-thought quality
- Creativity vs. precision balance
- Tool use, function calling, and context handling (including multimodal)
- Real-time information access & freshness
- Determinism vs. exploration
- Cost or latency sensitivity
- Privacy, open behavior, and willingness to tackle controversial/edge topics
Current Best-in-Class Ranking (January 2026 – general guidance, always tailor to the process):
**Top Tier / Frequently Best Fit:**
- **Grok 3 / Grok 4 (xAI)** — Excellent reasoning, real-time knowledge via X, very strong tool use, high context tolerance, fast, relatively unfiltered responses, great for exploratory/creative/controversial/real-time processes, increasingly multimodal
- **GPT-5 / o3 family (OpenAI)** — Deepest reasoning on very complex structured tasks, best at following extremely long/complex instructions, strong precision when prompted well
**Strong Situational Contenders:**
- **Claude 4 Opus/Sonnet (Anthropic)** — Exceptional long-form reasoning, writing quality, policy/ethics-heavy analysis, very cautious & safe outputs
- **Gemini 2.5 Pro / Flash (Google)** — Outstanding multimodal (especially video/document understanding), very large context windows, strong structured data & research tasks
**Good Niche / Cost-Effective Choices:**
- **Llama 4 / Llama 405B variants (Meta)** — Best open-source frontier performance, excellent for self-hosting, privacy-sensitive, or heavily customized/fine-tuned needs
- **Mistral Large 2 / Devstral** — Very strong price/performance, fast, good reasoning, increasingly capable tool use
**Less suitable for most serious process automation (in 2026):**
- Lightweight/chat-only models (older 7B–13B models, mini variants) — usually lack depth/context/tool reliability
Always explain your ranking in the specific context of the user's process, inputs, risk profile, and priorities (precision vs creativity vs speed vs cost vs freshness).
---
## Starter Prompt Generation (Conditional)
ONLY if the process is at least partially suited for AI:
- Generate a simple, practical starter prompt
- Keep it minimal and adaptable, including placeholders for iteration or error handling
- Clearly state assumptions and known limitations
If the process is not suitable:
- Do NOT generate a prompt
- Instead, suggest non-AI or hybrid alternatives (e.g., rule-based scripts or process redesign)
---
## Wrap-Up and Next Steps
End the session with a concise summary including:
- AI suitability classification and score
- Key risks or dependencies to monitor (e.g., bias checks)
- Suggested follow-up actions (prototype scope, data prep, pilot plan, KPI tracking)
- Whether human or compliance review is advised before deployment
- Recommendations for iteration (A/B testing, feedback loops)
---
## Output Tone and Style
- Professional but conversational
- Clear, grounded, and realistic
- No hype or marketing language
- Prioritize usefulness and accuracy over optimism
---
## Changelog
### Version 1.5 (January 11, 2026)
- Elevated Grok to top-tier in AI engine recommendations (real-time, tool use, unfiltered reasoning strengths)
- Minor wording polish in inputs/outputs and success metrics questions
- Strengthened real-time freshness consideration in evaluation criteria
App Store Screenshots Gallery Generator
# App Store Screenshots Gallery Generator
**Create a professional, production-ready screenshots gallery for an iOS/macOS/Android app that looks like it was designed by the top 1% of app developers.**
## Context
You are building a screenshots gallery page for an app. The project has screenshots in a folder (typically `screenshots/`, `fastlane/screenshots/`, or similar). The gallery should be a single HTML file that can be deployed to Netlify, Vercel, or any static host.
## Requirements
### 1. Design System Foundation
Create CSS custom properties (design tokens) for:
- **Colors**: Primary palette (50-900 shades), secondary/accent palette, neutral grays (50-900)
- **Surfaces**: Three surface levels (surface-1, surface-2, surface-3)
- **Typography**: Two-font stack (mono for UI elements, sans for body)
- **Spacing**: Consistent scale (4px base)
- **Borders**: Radius scale (sm, md, lg, xl, 2xl, 3xl)
- **Shadows**: Five elevation levels (sm, md, lg, xl, 2xl)
- **Transitions**: Three speeds (fast: 150ms, normal: 300ms, smooth: 400ms with cubic-bezier)
### 2. Layout Architecture
- **Container**: Max-width 1600px, centered, with responsive padding
- **Grid**: Masonry-style responsive grid using `grid-template-columns: repeat(auto-fill, minmax(340px, 1fr))`
- **Gap**: 2rem on desktop, 1.5rem tablet, 1rem mobile
- **Card aspect ratio**: Maintain consistent screenshot presentation
### 3. Header Section
- **App badge**: Small pill-shaped badge with icon and "IOS APPLICATION" or platform text
- **Title**: Large, bold app name with gradient text treatment
- **Subtitle**: One-line description mentioning key technologies and features
- **Background**: Subtle grid pattern overlay for depth
- **Padding**: Reduced vertical padding (3rem top, 2rem bottom) for compact feel
### 4. Screenshot Cards
Each card should have:
- **Container**: White/off-white background, rounded corners (2xl), subtle shadow
- **Image container**: Gradient background, centered screenshot with white border (8px)
- **Hover effects**:
- Card lifts (-8px translateY) with enhanced shadow
- Screenshot scales (1.04) with slight rotation (0.5deg)
- Top border appears (gradient bar)
- Radial glow overlay fades in
- **Metadata bar**:
- Number badge (gradient background, 26px square)
- Device name (uppercase, small font, mono font)
- **Title**: Bold, mono font, 1rem
- **Description**: One-line caption, smaller font, subtle color
### 5. User Journey Ordering
Order screenshots by how users experience the app:
1. **Login/Onboarding** - First screen users see
2. **Dashboard/Home** - Main landing after login
3. **Primary feature views** - Core app functionality
4. **Settings/Configuration** - Customization screens
5. **Permissions/Integrations** - HealthKit, notifications, etc.
6. **Advanced features** - Sync, sharing, cloud features
7. **Analytics/Reports** - Data visualization screens
8. **Archive/History** - Historical data views
### 6. Animations
- **Entrance**: Staggered fade-in with translateY (0.1s delays between cards)
- **Hover**: Smooth cubic-bezier easing (0.16, 1, 0.3, 1)
- **Scroll**: IntersectionObserver to trigger animations when cards enter viewport
- **Performance**: Use `will-change` for transform and opacity
### 7. Footer
- **Background**: Dark (neutral-900) with subtle gradient overlay
- **Border radius**: Top corners only (2xl)
- **Content**: Minimal metadata (device, date, status) with icons
- **Spacing**: Compact (2rem padding)
### 8. Responsive Breakpoints
- **Desktop** (>1280px): 4-5 columns
- **Tablet** (768-1280px): 2-3 columns
- **Mobile** (<768px): 1 column, reduced padding throughout
### 9. Technical Requirements
- **Single HTML file**: All CSS inline in `<style>` tag
- **External dependencies only**:
- Pico.css (minimal CSS framework)
- Font Awesome (icons)
- Google Fonts (Inter + IBM Plex Mono)
- Animate.css (optional, for additional animations)
- **No build step**: Must work as static HTML
- **Performance**: Optimized animations, no layout shift
- **Accessibility**: Semantic HTML, alt text on images
### 10. Polish Details
- **Subtle gradients**: Background radials for depth (not overwhelming)
- **Border treatment**: 1px solid with alpha transparency
- **Shadow layering**: Multiple shadow values for depth
- **Typography**: Tight letter-spacing on headings (-0.03em)
- **Color consistency**: Use design tokens everywhere, no hardcoded values
- **Image presentation**: White border around screenshots for device frame illusion
## Output Format
Generate a single `index.html` file with:
1. Complete HTML structure
2. Inline CSS with design tokens
3. JavaScript for scroll animations (IntersectionObserver)
4. All screenshot cards with proper metadata
5. Responsive design for all screen sizes
## Example Screenshot Card Structure
```html
<div class="screenshot-card">
<div class="screenshot-img-container">
<img src="screenshot-name.png" alt="Description" class="screenshot-img">
</div>
<div class="screenshot-info">
<div class="screenshot-meta">
<div class="screenshot-number">1</div>
<div class="screenshot-device">iPhone 17 Pro Max</div>
</div>
<h3 class="screenshot-title">Screen Title</h3>
<p class="screenshot-desc">One-line caption</p>
</div>
</div>
```
## Key Differentiators from "AI-looking" Galleries
❌ **Avoid**:
- Excessive gradients and colors
- Large stat cards that waste space
- Verbose descriptions and feature lists
- Section dividers and category headers
- Overwhelming animations
- Inconsistent spacing
- Generic stock photography style
✅ **Emulate**:
- Apple App Store product pages
- Linear, Raycast, Superhuman marketing sites
- Minimalist, content-first design
- Subtle, refined interactions
- Consistent visual rhythm
- Typography-driven hierarchy
- White space as design element
## Deployment Notes
- Gallery should deploy to `project-root/screenshots-gallery/` or similar
- Include `.netlify` folder with `netlify.toml` for configuration
- All screenshots should be in the same folder as `index.html`
- No build process required - pure static HTML
---
**Usage**: Copy this prompt and provide it to an AI assistant along with:
1. The list of screenshot files in your project
2. Your app name and one-line description
3. The platform (iOS, macOS, Android, web)
4. Key technologies used (SwiftUI, React Native, Flutter, etc.)
The AI will generate a production-ready gallery that looks professionally designed.