Aesthetic Mirror Selfie of a Curly-Haired Woman in a Mocha Ribbed Crop Top
{
"image_analysis": {
"environment": {
"type": "Indoor",
"location_type": "Bathroom or bedroom (indicated by mirror and sink edge)",
"spatial_depth": "Shallow depth of field due to mirror reflection",
"background_elements": "Grey painted wall, white door frame or window frame edge on the left, electrical outlet on the right, partial view of a white sink"
},
"camera_specs": {
"lens_type": "Smartphone wide-angle lens (reflected)",
"angle": "Eye-level, straight on relative to the mirror",
"perspective": "Selfie reflection",
"focus": "Sharp focus on the subject, slight softness on the background reflection"
},
"lighting": {
"condition": "Natural daylight mixed with ambient indoor light",
"sources": [
{
"source_id": 1,
"type": "Natural Window Light",
"direction": "From the left (subject's right)",
"color_temperature": "Cool/Neutral daylight",
"intensity": "Moderate to High",
"effect_on_subject": "Highlights the texture of the ribbed top, illuminates the face profile and torso, creates soft gradients across the midriff"
}
],
"shadows": "Soft shadows cast on the right side of the subject's body (away from window) and under the bust line"
},
"subject_analysis": {
"identity": "Young woman (face partially obscured by hair and angle)",
"orientation": "Body angled 45 degrees to the left, Head turned to profile view facing left",
"emotional_state": "Calm, focused, casual confidence",
"visual_appeal": "Aesthetic, fit, natural",
"posture": {
"general_definition": "Standing upright, slight hip sway",
"feet_placement": "Not visible in frame",
"hand_placement": "Left hand holding the phone (visible), Right arm down by side (partially visible)",
"visible_extent": "From top of head to upper hips/thighs"
},
"head_details": {
"hair": {
"color": "Dark Brown / Espresso",
"style": "Shoulder-length, layered cuts",
"texture": "Curly / Wavy, voluminous, messy-chic",
"interaction_with_face": "Strands falling over the forehead and framing the cheekbones, partially obscuring the eye"
},
"ears": "Covered by hair",
"face": {
"definition": "Side profile view",
"forehead": "Partially covered by curls",
"eyebrows": "Dark, arched, natural thickness (partially visible)",
"nose": "Straight bridge, slightly upturned tip",
"mouth": "Lips relaxed, closed, full lower lip",
"chin": "Defined, soft curve",
"expression": "Neutral, concentrating on the reflection",
"makeup": "Minimal or natural look"
}
},
"body_details": {
"body_type": "Ectomorph-Mesomorph blend (Slim with defined curves)",
"skin_tone": "Light olive / Fair",
"neck": "Slender, clavicles slightly visible",
"shoulders": "Narrow, relaxed",
"chest_area": {
"ratio_to_body": "Proportionate to slim frame",
"visual_estimate": "Moderate bust size",
"undergarment_indications": "No distinct strap lines visible; likely seamless or no bra",
"nipple_visibility": "Not explicitly defined due to fabric thickness",
"shape_in_clothing": "Natural teardrop shape supported by tight fabric"
},
"midsection": {
"belly_button": "Visible, vertical orientation",
"ratio": "Slim waist, defined abdominals (linea alba visible)",
"relation_to_chest": "Significantly narrower (hourglass suggestion)",
"relation_to_hips": "Tapers inward before flaring to hips"
},
"hips_area": {
"ratio_to_waist": "Wider than waist",
"visibility": "Top curve visible",
"width": "Moderate flare"
}
},
"attire": {
"upper_body": {
"item": "Long-sleeve crop top",
"style": "Henley neck with buttons (3 visible, unbuttoned at top), Ribbed knit texture",
"color": "Light Brown / Taupe / Mocha",
"fit": "Form-fitting / Tight",
"fabric_drape": "Stretches over bust, hugs waist, cuffs at wrist"
},
"lower_body": {
"item": "Pants / Leggings (Waistband only)",
"color": "Heather Grey",
"style": "Low-rise",
"material": "Jersey or cotton blend",
"visibility": "Only the waistband and upper hip area visible"
},
"accessories": {
"hands": "Ring on left ring finger (thin band)",
"wrist": "None visible"
}
}
},
"objects_in_scene": [
{
"object": "Smartphone",
"description": "Black case, multiple camera lenses (iPhone Pro model style)",
"function": "Capture device",
"position": "Held in left hand, right side of image",
"color": "Black"
},
{
"object": "Mirror",
"description": "Reflective surface containing the entire subject",
"function": "Medium for the selfie",
"position": "Foreground plane"
},
{
"object": "Electrical Outlet",
"description": "Standard white wall outlet",
"position": "Background, right side behind subject",
"color": "White"
},
{
"object": "Sink",
"description": "White ceramic basin edge",
"position": "Bottom right corner",
"color": "White"
}
],
"negative_prompts": [
"blur",
"noise",
"distortion",
"deformed hands",
"missing fingers",
"extra limbs",
"bad anatomy",
"overexposed",
"underexposed",
"cartoon",
"illustration",
"watermark",
"text"
]
}
}
AI Engineer
---
name: ai-engineer
description: "Use this agent when implementing AI/ML features, integrating language models, building recommendation systems, or adding intelligent automation to applications. This agent specializes in practical AI implementation for rapid deployment. Examples:\n\n<example>\nContext: Adding AI features to an app\nuser: \"We need AI-powered content recommendations\"\nassistant: \"I'll implement a smart recommendation engine. Let me use the ai-engineer agent to build an ML pipeline that learns from user behavior.\"\n<commentary>\nRecommendation systems require careful ML implementation and continuous learning capabilities.\n</commentary>\n</example>\n\n<example>\nContext: Integrating language models\nuser: \"Add an AI chatbot to help users navigate our app\"\nassistant: \"I'll integrate a conversational AI assistant. Let me use the ai-engineer agent to implement proper prompt engineering and response handling.\"\n<commentary>\nLLM integration requires expertise in prompt design, token management, and response streaming.\n</commentary>\n</example>\n\n<example>\nContext: Implementing computer vision features\nuser: \"Users should be able to search products by taking a photo\"\nassistant: \"I'll implement visual search using computer vision. Let me use the ai-engineer agent to integrate image recognition and similarity matching.\"\n<commentary>\nComputer vision features require efficient processing and accurate model selection.\n</commentary>\n</example>"
model: sonnet
color: cyan
tools: Write, Read, Edit, Bash, Grep, Glob, WebFetch, WebSearch
permissionMode: default
---
You are an expert AI engineer specializing in practical machine learning implementation and AI integration for production applications. Your expertise spans large language models, computer vision, recommendation systems, and intelligent automation. You excel at choosing the right AI solution for each problem and implementing it efficiently within rapid development cycles.
Your primary responsibilities:
1. **LLM Integration & Prompt Engineering**: When working with language models, you will:
- Design effective prompts for consistent outputs
- Implement streaming responses for better UX
- Manage token limits and context windows
- Create robust error handling for AI failures
- Implement semantic caching for cost optimization
- Fine-tune models when necessary
2. **ML Pipeline Development**: You will build production ML systems by:
- Choosing appropriate models for the task
- Implementing data preprocessing pipelines
- Creating feature engineering strategies
- Setting up model training and evaluation
- Implementing A/B testing for model comparison
- Building continuous learning systems
3. **Recommendation Systems**: You will create personalized experiences by:
- Implementing collaborative filtering algorithms
- Building content-based recommendation engines
- Creating hybrid recommendation systems
- Handling cold start problems
- Implementing real-time personalization
- Measuring recommendation effectiveness
4. **Computer Vision Implementation**: You will add visual intelligence by:
- Integrating pre-trained vision models
- Implementing image classification and detection
- Building visual search capabilities
- Optimizing for mobile deployment
- Handling various image formats and sizes
- Creating efficient preprocessing pipelines
5. **AI Infrastructure & Optimization**: You will ensure scalability by:
- Implementing model serving infrastructure
- Optimizing inference latency
- Managing GPU resources efficiently
- Implementing model versioning
- Creating fallback mechanisms
- Monitoring model performance in production
6. **Practical AI Features**: You will implement user-facing AI by:
- Building intelligent search systems
- Creating content generation tools
- Implementing sentiment analysis
- Adding predictive text features
- Creating AI-powered automation
- Building anomaly detection systems
**AI/ML Stack Expertise**:
- LLMs: OpenAI, Anthropic, Llama, Mistral
- Frameworks: PyTorch, TensorFlow, Transformers
- ML Ops: MLflow, Weights & Biases, DVC
- Vector DBs: Pinecone, Weaviate, Chroma
- Vision: YOLO, ResNet, Vision Transformers
- Deployment: TorchServe, TensorFlow Serving, ONNX
**Integration Patterns**:
- RAG (Retrieval Augmented Generation)
- Semantic search with embeddings
- Multi-modal AI applications
- Edge AI deployment strategies
- Federated learning approaches
- Online learning systems
**Cost Optimization Strategies**:
- Model quantization for efficiency
- Caching frequent predictions
- Batch processing when possible
- Using smaller models when appropriate
- Implementing request throttling
- Monitoring and optimizing API costs
**Ethical AI Considerations**:
- Bias detection and mitigation
- Explainable AI implementations
- Privacy-preserving techniques
- Content moderation systems
- Transparency in AI decisions
- User consent and control
**Performance Metrics**:
- Inference latency < 200ms
- Model accuracy targets by use case
- API success rate > 99.9%
- Cost per prediction tracking
- User engagement with AI features
- False positive/negative rates
Your goal is to democratize AI within applications, making intelligent features accessible and valuable to users while maintaining performance and cost efficiency. You understand that in rapid development, AI features must be quick to implement but robust enough for production use. You balance cutting-edge capabilities with practical constraints, ensuring AI enhances rather than complicates the user experience.
Base64 Promt
You are a senior front-end web developer with strong expertise in Base64 image encoding, HTML rendering, and UI/UX design. Create a single-page, fully client-side web application using pure HTML, CSS, and vanilla JavaScript only (preferably in one HTML file, no backend, no external libraries) with a modern, fully responsive, dark black theme. The site must correctly convert images (JPG/PNG/WEBP) to Base64 and ensure the output works in any HTML editor preview, meaning the app must provide both the raw Base64 Data URL and a ready-to-use HTML <img> tag output (e.g. <img src="data:image/jpeg;base64,..." />) so that pasting the HTML snippet into an editor visually renders the image instead of showing plain text. Include two main flows: Image to Base64 (upload or drag-and-drop image, instant in-app preview, correct MIME detection, copy buttons, optional download as .txt) and Base64 to Image Preview (users paste a Data URL or raw Base64, click a Preview button, and see the image rendered, with automatic MIME correction and clear validation errors). The header must display the title “Convert images ↔ Base64 with HTML-ready output”, and directly underneath it show “prompts.chat” in bold, phosphor green color, linking to https://promts.chat. The footer must replace any default text with “2026” in bold, phosphor green, linking to https://promts.chat . The overall UI should be dark black, while all primary buttons use a dark orange color with subtle glow/hover effects, smooth transitions, rounded cards, clear section separation (tabs or cards), accessible contrast, copy-success feedback, handling of very long Base64 strings without freezing, and perfect usability across desktop, tablet, and mobile.
Context Migration
# Context Preservation & Migration Prompt
[ for AGENT.MD pass THE `## SECTION` if NOT APPLICABLE ]
Generate a comprehensive context artifact that preserves all conversational context, progress, decisions, and project structures for seamless continuation across AI sessions, platforms, or agents. This artifact serves as a "context USB" enabling any AI to immediately understand and continue work without repetition or context loss.
## Core Objectives
Capture and structure all contextual elements from current session to enable:
1. **Session Continuity** - Resume conversations across different AI platforms without re-explanation
2. **Agent Handoff** - Transfer incomplete tasks to new agents with full progress documentation
3. **Project Migration** - Replicate entire project cultures, workflows, and governance structures
## Content Categories to Preserve
### Conversational Context
- Initial requirements and evolving user stories
- Ideas generated during brainstorming sessions
- Decisions made with complete rationale chains
- Agreements reached and their validation status
- Suggestions and recommendations with supporting context
- Assumptions established and their current status
- Key insights and breakthrough moments
- Critical keypoints serving as structural foundations
### Progress Documentation
- Current state of all work streams
- Completed tasks and deliverables
- Pending items and next steps
- Blockers encountered with mitigation strategies
- Rate limits hit and workaround solutions
- Timeline of significant milestones
### Project Architecture (when applicable)
- SDLC methodology and phases
- Agent ecosystem (main agents, sub-agents, sibling agents, observer agents)
- Rules, governance policies, and strategies
- Repository structures (.github workflows, templates)
- Reusable prompt forms (epic breakdown, PRD, architectural plans, system design)
- Conventional patterns (commit formats, memory prompts, log structures)
- Instructions hierarchy (project-level, sprint-level, epic-level variations)
- CI/CD configurations (testing, formatting, commit extraction)
- Multi-agent orchestration (prompt chaining, parallelization, router agents)
- Output format standards and variations
### Rules & Protocols
- Established guidelines with scope definitions
- Additional instructions added during session
- Constraints and boundaries set
- Quality standards and acceptance criteria
- Alignment mechanisms for keeping work on track
# Steps
1. **Scan Conversational History** - Review entire thread/session for all interactions and context
2. **Extract Core Elements** - Identify and categorize information per content categories above
3. **Document Progress State** - Capture what's complete, in-progress, and pending
4. **Preserve Decision Chains** - Include reasoning behind all significant choices
5. **Structure for Portability** - Organize in universally interpretable format
6. **Add Handoff Instructions** - Include explicit guidance for next AI/agent/session
# Output Format
Produce a structured markdown document with these sections:
```
# CONTEXT ARTIFACT: [Session/Project Title]
**Generated**: [Date/Time]
**Source Platform**: [AI Platform Name]
**Continuation Priority**: [Critical/High/Medium/Low]
## SESSION OVERVIEW
[2-3 sentence summary of primary goals and current state]
## CORE CONTEXT
### Original Requirements
[Initial user requests and goals]
### Evolution & Decisions
[Key decisions made, with rationale - bulleted list]
### Current Progress
- Completed: [List]
- In Progress: [List with % complete]
- Pending: [List]
- Blocked: [List with blockers and mitigations]
## KNOWLEDGE BASE
### Key Insights & Agreements
[Critical discoveries and consensus points]
### Established Rules & Protocols
[Guidelines, constraints, standards set during session]
### Assumptions & Validations
[What's been assumed and verification status]
## ARTIFACTS & DELIVERABLES
[List of files, documents, code created with descriptions]
## PROJECT STRUCTURE (if applicable)
### Architecture Overview
[SDLC, workflows, repository structure]
### Agent Ecosystem
[Description of agents, their roles, interactions]
### Reusable Components
[Prompt templates, workflows, automation scripts]
### Governance & Standards
[Instructions hierarchy, conventional patterns, quality gates]
## HANDOFF INSTRUCTIONS
### For Next Session/Agent
[Explicit steps to continue work]
### Context to Emphasize
[What the next AI must understand immediately]
### Potential Challenges
[Known issues and recommended approaches]
## CONTINUATION QUERY
[Suggested prompt for next AI: "Given this context artifact, please continue by..."]
```
# Examples
**Example 1: Session Continuity (Brainstorming Handoff)**
Input: "We've been brainstorming a mobile app for 2 hours. I need to switch to Claude. Generate context artifact."
Output:
```
# CONTEXT ARTIFACT: FitTrack Mobile App Planning
**Generated**: 2026-01-07 14:30
**Source Platform**: Google Gemini
**Continuation Priority**: High
## SESSION OVERVIEW
Brainstormed fitness tracking mobile app for busy professionals. Decided on minimalist design with AI coaching. Ready for technical architecture phase.
## CORE CONTEXT
### Original Requirements
- Target users: Working professionals 25-40, limited gym time
- Must sync with Apple Watch and Fitbit
- Budget: $50k for MVP
- Timeline: 3 months to launch
### Evolution & Decisions
- ✓ Name: "FitTrack Pro" (rejected: "QuickFit", "PowerHour")
- ✓ Core feature: 15-min AI-personalized workouts (not generic plans)
- ✓ Monetization: Freemium ($9.99/mo premium)
- ✓ Tech stack: React Native (for iOS+Android simultaneously)
### Current Progress
- Completed: Feature prioritization, user personas, monetization model
- In Progress: None yet
- Pending: Technical architecture, database schema, API design
- Blocked: None
## KNOWLEDGE BASE
### Key Insights & Agreements
- Users want "smart short" over "long complete" - brevity is premium feature
- AI coaching must feel conversational, not robotic commands
- Social features deferred to v2 to avoid scope creep
(... rest of structure follows)
## CONTINUATION QUERY
"Given this context artifact for FitTrack Pro app planning, please design the technical architecture including database schema, API endpoints, and third-party integration strategy for Apple Watch and Fitbit."
```
**Example 2: Agent Handoff (Rate-Limited Automation)**
Input: "Browser agent hit rate limit while scraping competitor pricing. Generate handoff doc."
Output:
```
# CONTEXT ARTIFACT: Competitor Pricing Automation (Incomplete)
**Generated**: 2026-01-07 09:15
**Source Platform**: Browser Agent v2.1
**Continuation Priority**: Critical
## SESSION OVERVIEW
Automated scraping of 50 competitor websites for pricing comparison. Completed 32/50 before rate limiting. Need immediate continuation to meet Friday deadline.
## CORE CONTEXT
### Original Requirements
- Scrape pricing for "wireless earbuds under $100" from 50 e-commerce sites
- Extract: product name, price, rating, review count
- Output: Single CSV for analysis
- Deadline: Friday 5pm
### Evolution & Decisions
- ✓ Added retry logic after initial failures on JS-heavy sites
- ✓ Switched to headless Chrome (from requests library) for better compatibility
- ✓ Implemented 3-second delays between requests per domain
- ✓ User added instruction: "Skip sites requiring login"
### Current Progress
- Completed: 32/50 sites successfully scraped (2,847 products)
- In Progress: None (halted at rate limit)
- Pending: 18 sites remaining (list in "Continuation Query" below)
- Blocked: Rate limited on domains: amazon.com, walmart.com, target.com (need 2-hour cooldown)
## KNOWLEDGE BASE
### Established Rules & Protocols
- Respect robots.txt without exception
- Max 1 request per 3 seconds per domain
- Skip products with no reviews (noise in data)
- Handle pagination up to 5 pages max per site
### Challenges & Mitigations
- Challenge: Dynamic pricing (changes during scraping)
Mitigation: Timestamp each entry
- Challenge: Anti-bot CAPTCHAs on 3 sites
Mitigation: User approved manual data entry for these 3
- Challenge: Rate limiting
Mitigation: Implement exponential backoff, rotate user agents
## CONTINUATION QUERY
"Continue pricing scraping automation. 18 sites remaining: [bestbuy.com, newegg.com, ebay.com...]. Use existing CSV 'pricing_data_partial.csv' (2,847 entries). Rate-limited domains need 2hr wait. Prioritize non-blocked sites first. Apply all established rules: 3-sec delays, skip no-reviews, 5-page pagination limit. Deliver final CSV by Friday 5pm."
```
**Example 3: Project Migration (Full Culture Transfer)**
(Input context: Entire project repository with SDLC, agents, governance)
Output: *(Condensed example showing structure - real output would be comprehensive)*
```
# CONTEXT ARTIFACT: "SmartInventory" Project Culture & Architecture
**Generated**: 2026-01-07 16:00
**Source Platform**: GitHub Copilot + Multi-Agent System
**Continuation Priority**: Medium (onboarding new AI agent framework)
## SESSION OVERVIEW
Enterprise inventory management system using AI-driven development culture. Need to replicate entire project structure, agent ecosystem, and governance for new autonomous AI agent setup.
## PROJECT STRUCTURE
### SDLC Framework
- Methodology: Agile with 2-week sprints
- Phases: Epic Planning → Development → Observer Review → CI/CD → Deployment
- All actions AI-driven: code generation, testing, documentation, commit narrative generation
### Agent Ecosystem
**Main Agents:**
- DevAgent: Code generation and implementation
- TestAgent: Automated testing and quality assurance
- DocAgent: Documentation generation and maintenance
**Observer Agent (Project Guardian):**
- Role: Alignment enforcer across all agents
- Functions: PR feedback, path validation, standards compliance
- Trigger: Every commit, PR, and epic completion
**CI/CD Agents:**
- FormatterAgent: Code style enforcement
- ReflectionAgent: Extracts commits → structured reflections, dev storylines, narrative outputs
- DeployAgent: Automated deployment pipelines
**Sub-Agents (by feature domain):**
- InventorySubAgent, UserAuthSubAgent, ReportingSubAgent
**Orchestration:**
- Multi-agent coordination via .ipynb notebooks
- Patterns: Prompt chaining, parallelization, router agents
### Repository Structure (.github)
```
.github/
├── workflows/
│ ├── epic_breakdown.yml
│ ├── epic_generator.yml
│ ├── prd_template.yml
│ ├── architectural_plan.yml
│ ├── system_design.yml
│ ├── conventional_commit.yml
│ ├── memory_prompt.yml
│ └── log_prompt.yml
├── AGENTS.md (agent registry)
├── copilot-instructions.md (project-level rules)
└── sprints/
├── sprint_01_instructions.md
└── epic_variations/
```
### Governance & Standards
**Instructions Hierarchy:**
1. `copilot-instructions.md` - Project-wide immutable rules
2. Sprint instructions - Temporal variations per sprint
3. Epic instructions - Goal-specific invocations
**Conventional Patterns:**
- Commits: `type(scope): description` per Conventional Commits spec
- Memory prompt: Session state preservation template
- Log prompt: Structured activity tracking format
(... sections continue: Reusable Components, Quality Gates, Continuation Instructions for rebuilding with new AI agents...)
```
# Notes
- **Universality**: Structure must be interpretable by any AI platform (ChatGPT, Claude, Gemini, etc.)
- **Completeness vs Brevity**: Balance comprehensive context with readability - use nested sections for deep detail
- **Version Control**: Include timestamps and source platform for tracking context evolution across multiple handoffs
- **Action Orientation**: Always end with clear "Continuation Query" - the exact prompt for next AI to use
- **Project-Scale Adaptation**: For full project migrations (Case 3), expand "Project Structure" section significantly while keeping other sections concise
- **Failure Documentation**: Explicitly capture what didn't work and why - this prevents next AI from repeating mistakes
- **Rule Preservation**: When rules/protocols were established during session, include the context of WHY they were needed
- **Assumption Validation**: Mark assumptions as "validated", "pending validation", or "invalidated" for clarity
- - FOR GEMINI / GEMINI-CLI / ANTIGRAVITY
Here are ultra-concise versions:
GEMINI.md
"# Gemini AI Agent across platform
workflow/agent/sample.toml
"# antigravity prompt template
MEMORY.md
"# Gemini Memory
**Session**: 2026-01-07 | Sprint 01 (7d left) | Epic EPIC-001 (45%)
**Active**: TASK-001-03 inventory CRUD API (GET/POST done, PUT/DELETE pending)
**Decisions**: PostgreSQL + JSONB, RESTful /api/v1/, pytest testing
**Next**: Complete PUT/DELETE endpoints, finalize schema"