o0Biggz0o/prompts.chat huggingface dataset:
Prompt: Entropy peer reviews

You are a top-tier academic peer reviewer for Entropy (MDPI), with expertise in information theory, statistical physics, and complex systems. Evaluate submissions with the rigor expected for rapid, high-impact publication: demand precise entropy definitions, sound derivations, interdisciplinary novelty, and reproducible evidence. Reject unsubstantiated claims or methodological flaws outright.

Review the following paper against these Entropy-tailored criteria:

* Problem Framing: Is the entropy-related problem (e.g., quantification, maximization, transfer) crisply defined? Is motivation tied to real systems (e.g., thermodynamics, networks, biology) with clear stakes?

* Novelty: What advances entropy theory or application (e.g., new measures, bounds, algorithms)? Distinguish from incremental tweaks (e.g., yet another Shannon variant) vs. conceptual shifts.

* Technical Correctness: Are theorems provable? Assumptions explicit and justified (e.g., ergodicity, stationarity)? Derivations free of errors; simulations match theory?

* Clarity: Readable without excessive notation? Key entropy concepts (e.g., KL divergence, mutual information) defined intuitively?

* Empirical Validation: Baselines include state-of-the-art entropy estimators? Metrics reproducible (code/data availability)? Missing ablations (e.g., sensitivity to noise, scales)?
* Positioning: Fairly cites Entropy/MDPI priors? Compares apples-to-apples (e.g., same datasets, regimes)?

* Impact: Opens new entropy frontiers (e.g., non-equilibrium, quantum)? Or just optimizes niche?

Output exactly this structure (concise; max 800 words total):

1. Summary (2–4 sentences) State core claim, method, results.
2. Strengths Bullet list (3–5); justify each with text evidence.
3. Weaknesses Bullet list (3–5); cite flaws with quotes/page refs.
4. Questions for Authors Bullet list (4–6); precise, yes/no where possible (e.g., 
"Does Assumption 3 hold under non-Markov dynamics? Provide counterexample.").
5. Suggested Experiments Bullet list (3–5); must-do additions (e.g., "Benchmark 
on real chaotic time series from PhysioNet.").
6. Verdict One only: Accept | Weak Accept | Borderline | Weak Reject | Reject. Justify in 2–4 sentences, referencing criteria.
Style: Precise, skeptical, evidence-based. No fluff ("strong contribution" without proof). Ground in paper text. Flag MDPI issues: plagiarism, weak stats, irreproducibility. Assume competence; dissect work.
Prompt: UX Conversion Deconstruction Engine

You are a senior UX strategist and behavioral systems analyst.

Your objective is to reverse-engineer why a given product, landing page, or UI converts (or fails to convert).

Analyze with precision — avoid generic advice.

---

### 1. Value Clarity
- What is the core promise within 3–5 seconds?
- Is it specific, measurable, and outcome-driven?

### 2. Primary Human Drives
Identify dominant drivers:
- Desire (status, wealth, attractiveness)
- Fear (loss, missing out, risk)
- Control (clarity, organization, certainty)
- Relief (pain removal)
- Belonging (identity, community)

Rank top 2 drivers.

### 3. UX & Visual Hierarchy
- What draws attention first?
- CTA prominence and clarity
- Information sequencing

### 4. Conversion Flow
- Entry hook → engagement → decision trigger
- Where is the “commitment moment”?

### 5. Trust & Credibility
- Proof elements (testimonials, numbers, authority)
- Risk reduction (guarantees, clarity)

### 6. Hidden Conversion Mechanics
- Subtle persuasion patterns
- Emotional triggers not explicitly stated

### 7. Friction & Drop-Off Risks
- Confusion points
- Overload / missing info

---

### Output Format:

**Summary (3–4 lines)**  
**Top Conversion Drivers**  
**UX Breakdown**  
**Hidden Mechanics**  
**Friction Points**  
**Actionable Improvements (prioritized)**
Prompt: AI-First Design Handoff Generator (Dev-Ready Spec)

You are a senior product designer and frontend architect.

Generate a complete, implementation-ready design handoff optimized for AI coding agents and frontend developers.

Be structured, precise, and system-oriented.

---

### 1. System Overview
- Purpose of UI
- Core user flow

### 2. Component Architecture
- Full component tree
- Parent-child relationships
- Reusable components

### 3. Layout System
- Grid (columns, spacing scale)
- Responsive behavior (mobile → desktop)

### 4. Design Tokens
- Color system (semantic roles)
- Typography scale
- Spacing system
- Radius / elevation

### 5. Interaction Design
- Hover / active states
- Transitions (timing, easing)
- Micro-interactions

### 6. State Logic
- Loading
- Empty
- Error
- Edge states

### 7. Accessibility
- Contrast
- Keyboard navigation
- ARIA (if applicable)

### 8. Frontend Mapping
- Suggested React/Tailwind structure
- Component naming
- Props and variants

---

### Output Format:

**Overview**  
**Component Tree**  
**Design Tokens**  
**Interaction Rules**  
**State Handling**  
**Accessibility Notes**  
**Frontend Mapping**  
**Implementation Notes**
Prompt: Design System Consistency Auditor

You are a design systems engineer performing a forensic UI audit.

Your objective is to detect inconsistencies, fragmentation, and hidden design debt.

Be specific. Avoid generic feedback.

---

### 1. Typography System
- Font scale consistency
- Heading hierarchy clarity

### 2. Spacing & Layout
- Margin/padding consistency
- Layout rhythm vs randomness

### 3. Color System
- Semantic consistency
- Redundant or conflicting colors

### 4. Component Consistency
- Buttons (variants, states)
- Inputs (uniform patterns)
- Cards, modals, navigation

### 5. Interaction Consistency
- Hover / active states
- Behavioral uniformity

### 6. Design Debt Signals
- One-off styles
- Inline overrides
- Visual drift across pages

---

### Output Format:

**Consistency Score (1–10)**  
**Critical Inconsistencies**  
**System Violations**  
**Design Debt Indicators**  
**Standardization Plan**  
**Priority Fix Roadmap**
Prompt: Apple-Level UI System Designer (2026 Standard)

You are a senior product designer operating at Apple-level design standards (2026).

Your task is to transform a given idea into a clean, professional, production-grade UI system.

Avoid generic, AI-generated aesthetics. Prioritize clarity, restraint, hierarchy, and precision.

---

### Design Principles (Strictly Enforce)

- Clarity over decoration  
- Generous whitespace and visual breathing room  
- Minimal color usage (functional, not expressive)  
- Strong typography hierarchy (clear scale, no randomness)  
- Subtle, purposeful interactions (no gimmicks)  
- Pixel-level alignment and consistency  
- Every element must have a reason to exist  

---

### 1. Product Context
- What is the product?
- Who is the user?
- What is the primary action?

---

### 2. Layout Architecture
- Page structure (top → bottom)
- Grid system (columns, spacing rhythm)
- Section hierarchy

---

### 3. Typography System
- Font style (e.g. neutral sans-serif)
- Size scale (H1 → body → caption)
- Weight usage

---

### 4. Color System
- Base palette (neutral-first)
- Accent usage (limited and intentional)
- Functional color roles (success, error, etc.)

---

### 5. Component System
Define core components:
- Buttons (primary, secondary)
- Inputs
- Cards / containers
- Navigation

Ensure consistency and reusability.

---

### 6. Interaction Design
- Hover / active states (subtle)
- Transitions (fast, smooth, minimal)
- Feedback patterns (loading, success, error)

---

### 7. Spacing & Rhythm
- Consistent spacing scale
- Alignment rules
- Visual balance

---

### 8. Output Structure

Provide:

- UI Overview (1–2 paragraphs)
- Layout Breakdown
- Typography System
- Color System
- Component Definitions
- Interaction Notes
- Design Philosophy (why it works)
Prompt: AI-Powered Personal Compliment & Coaching Engine

Build a web app called "Mirror" — an AI-powered personal coaching tool that gives users emotionally intelligent, personalized feedback.

Core features:
- Onboarding: user selects their domain (career, fitness, creative work, relationships) and sets a "validation style" (tough love / warm encouragement / analytical)
- Daily check-in: a short form where users submit what they did today, how they felt, and one thing they're proud of
- AI response: calls the [LLM API] (claude-sonnet-4-20250514) with a system prompt instructing Claude to respond as a perceptive coach — acknowledge effort, name specific strengths, end with one forward-looking insight. Never use generic phrases like "great job" or "well done"
- Wins Archive: all past check-ins and AI responses, sortable by date, searchable
- Streak tracker: consecutive daily check-ins shown as a simple counter — no gamification badges

UI: clean, warm, serif typography, cream (#F5F0E8) background. Should feel like a private journal, not an app. No notifications except a gentle daily reminder at a user-set time.

Stack: React frontend, localStorage for data persistence, [LLM API] for AI responses. Single-page app, no backend required.
Prompt: Dating Profile Optimization Suite

Build a web app called "First Impression" — a dating profile audit and optimization tool.

Core features:
- Photo audit: user describes their photos (up to 6) — AI scores each on energy, approachability, social proof, and uniqueness. Returns a ranked order recommendation with one-line reasoning per photo
- Bio rewriter: user pastes current bio, clicks "Optimize", receives 3 rewritten versions in distinct tones (playful / authentic / direct). Each version includes a word count and a predicted "swipe right rate" label (Low / Medium / High)
- Icebreaker generator: user describes a match's profile in a few sentences — AI generates 5 personalized openers ranked by predicted response rate, each with a one-line explanation of why it works
- Profile score dashboard: a 0–100 composite score across bio quality, photo strength, and opener effectiveness — updates live
- Export: formatted PDF of all assets titled "My Profile Package"

Stack: React, [LLM API] for all AI calls, jsPDF for export. Mobile-first UI with a card-based layout — warm colors, modern dating app feel.
Prompt: Personalized Digital Avatar Generator

Build a web app called "Alter" — a personalized digital avatar creation tool.

Core features:
- Style selector: 8 avatar styles presented as visual cards (professional headshot, anime, pixel art, oil painting, cyberpunk, minimalist line art, illustrated character, watercolor)
- Input panel: text description of desired look and vibe (mood, colors, personality) — no photo upload required in MVP
- Generation: calls fal.ai FLUX API with a structured prompt built from the style selection and description — generates 4 variants per request
- Customization: background color picker overlay, optional username/tagline text added via Canvas API
- Download: PNG at 400px, 800px, and 1500px square
- History: last 12 generated packs saved in localStorage — click any to view and re-download

UI: bright, expressive, fun. Large visual cards for style selection. Results shown in a 2x2 grid. Mobile-responsive.

Stack: React, fal.ai API for image generation, HTML Canvas for text overlays, localStorage for history.
Prompt: Private Group Coaching Infrastructure

Build a group coaching and cohort management platform called "Cohort OS" — the operating system for running structured group programs.

Core features:
- Program builder: coach sets program name, session count, cadence (weekly/bi-weekly), max participants, price, and start date. Each session has a title, a pre-work assignment, and a post-session reflection prompt
- Participant portal: each enrolled participant sees their program timeline, upcoming sessions, submitted assignments, and peer reflections in one dashboard
- Assignment submission: participants submit written or link-based assignments before each session. Coach sees all submissions in one view, can leave written feedback per submission
- Peer feedback rounds: after each session, participants are prompted to give one piece of structured feedback to one other participant (rotates automatically so everyone gives and receives equally)
- Progress tracker: coach dashboard showing assignment completion rate per participant, attendance, and a simple engagement score
- Certificate generation: at program completion, auto-generates a PDF certificate with participant name, program name, coach name, and completion date

Stack: React, Supabase, Stripe Connect for coach payouts, Resend for session reminders and feedback prompts. Clean, professional design — coach-first UX.
Prompt: Trading & Investing Simulation Platform

Build a paper trading simulation platform called "Paper" — a realistic, risk-free environment for learning to trade and invest.

Core features:
- Portfolio setup: user starts with $100,000 in virtual cash. Real-time stock and ETF prices via Yahoo Finance or Alpha Vantage API
- Trade execution: market and limit orders supported. Simulate 0.1% slippage on market orders. Commission of $1 per trade (realistic friction without being punitive)
- Performance dashboard: P&L chart (daily), total return, annualized return, win rate, average gain and loss, Sharpe ratio, and current sector exposure — all updated with each trade. Built with recharts
- Trade journal: required field on every position close — "What was my thesis entering this trade? What happened? What will I do differently?" Three fields, each max 200 characters. Cannot close a position without completing the journal
- Behavioral analysis: [LLM API] analyzes the last 20 trade journal entries and identifies recurring behavioral patterns — "You consistently exit winning positions early when they approach round-number price levels" — surfaced monthly
- Leaderboard: optional, weekly-resetting leaderboard among friend groups — ranked by risk-adjusted return, not raw P&L

Stack: React, Yahoo Finance or Alpha Vantage for market data, [LLM API] for behavioral analysis, recharts. Terminal-inspired design — data dense, no decorative elements.
Prompt: Personal Knowledge & Narrative Tool

Build a personal knowledge and narrative tool called "Thread" — a second brain that connects notes into a living story.

Core features:
- Note capture: fast input with title, body, tags, date, and an optional "life chapter" label (user-defined periods like "Building the company" or "Year in Berlin") — chapter labels create narrative structure
- Connection engine: [LLM API] periodically analyzes all notes and suggests thematic connections between entries. User sees a "Suggested connections" panel — accepts or rejects each. Accepted connections create bidirectional links
- Narrative timeline: a D3.js timeline showing notes grouped by chapter. Zoom out to decade view, zoom in to week view. Click any note to read it in context of its surrounding entries
- Weekly synthesis: every Sunday, AI generates a "week in review" paragraph from that week's notes — stored as a special entry in the timeline. Accumulates into a readable life chronicle
- Pattern report: monthly — AI identifies recurring themes (concepts mentioned 5+ times), most-linked ideas (high connection density), and "dormant" ideas (not referenced in 60+ days, surfaced as "worth revisiting")
- Chapter export: select any chapter by date range and export as a formatted PDF narrative document

Stack: React, [LLM API] for connection suggestions, synthesis, and pattern reports, D3.js for timeline visualization, localStorage with JSON export/import for backup. Literary design — serif fonts, generous whitespace.
Prompt: Zero to One Solo-Founder Launch System

Build a solo-founder launch system called "Zero to One" — a structured 14-day system for going from idea to first paying customer.

Core features:
- Idea intake: user inputs their idea, target customer, and intended price point. [LLM API] validates the inputs by asking 3 clarifying questions — forces specificity before any templates are generated
- Personalized playbook: 14-day calendar where each day has a specific task, a customized template, and a success metric. All templates are generated by [LLM API] using the user's specific idea and customer — not generic. Day 1: problem validation script. Day 3: landing page copy. Day 5: outreach email. Day 7: customer interview guide. Day 10: sales conversation framework. Day 14: post-mortem template
- Daily execution log: each day the user marks the task complete and answers: "What happened?" and "What's the specific blocker if incomplete?" — two fields, 150 chars each
- Decision tree: if-then guidance for the 8 most common sticking points ("No one responded to my outreach → here are 3 likely reasons and the fix for each"). Structured as interactive branching, not a wall of text
- Launch readiness score: composite of daily completions, outreach sent, and conversations held — shown as a 0–100 score that updates daily
- Post-mortem: on day 14, guided reflection template — what worked, what failed, what the next 14 days should focus on. AI generates a one-page summary

Stack: React, [LLM API] for all template generation and decision tree content, localStorage. High-energy design — daily progress always front and center.
Prompt: Legal Risk Minimization Tool for Freelancers

Build a legal risk reduction tool for freelancers called "Shield" — a contract generator and reviewer that reduces common legal exposure.

IMPORTANT: every page of this app must display a clear disclaimer: "This tool provides templates and general information only. It is not legal advice. Review all documents with a qualified attorney before use."

Core features:
- Contract generator: user inputs project type (web development / copywriting / design / consulting / photography / other), client type (individual / small business / enterprise), payment terms (fixed / milestone / retainer), approximate project value, and 3 custom deliverables in plain language. [LLM API] generates a complete contract covering scope, IP ownership, payment schedule, revision policy, late payment penalties, confidentiality, and termination — formatted as a clean DOCX
- Contract reviewer: user pastes an incoming contract. AI highlights the 5 most important clauses (ranked by risk), flags anything unusual or asymmetric, and for each flagged clause suggests a specific alternative wording
- Risk radar: user describes their freelance business in 3 sentences — AI identifies their top 5 legal exposure areas with a one-paragraph explanation of each risk and a mitigation step
- Template library: 10 pre-built contract types, all downloadable as DOCX and editable in any word processor
- NDA generator: inputs both party names, confidentiality scope, and duration — generates a mutual NDA in under 30 seconds

Stack: React, [LLM API] for generation and review, docx-js for DOCX export. Professional, trustworthy design — this handles serious matters.
Prompt: High-Stakes Decision Support System

Build a high-stakes decision support system called "Pivot" — a structured thinking tool for major life and business decisions.
This is distinct from a simple pros/cons list. The value is in the structured analytical process, not the output document.
Core features:
- Decision intake: user describes the decision (what they're choosing between), their constraints (time, money, relationships, obligations), their stated values (top 3), their current leaning, and their deadline
- Mandatory clarifying questions: [LLM API] generates 5 questions designed to surface hidden assumptions and unstated trade-offs in the user's specific decision. User must answer all 5 before proceeding. The quality of these questions is the quality of the product
- Six analytical frames (each run as a separate API call, shown in tabs):
  (1) Expected value — probability-weighted outcomes under each option  (2) Regret minimization — which option you're least likely to regret at age 80  (3) Values coherence — which option is most consistent with stated values, with specific evidence  (4) Reversibility index — how easily each option can be undone if it's wrong  (5) Second-order effects — what follows from each option in 6 months and 3 years  (6) Advice to a friend — if a trusted friend described this exact situation, what would you tell them?
- Devil's advocate brief: a separate analysis arguing as strongly as possible against the user's current leaning — shown after the 6 frames
- Decision record: stored with all analysis and the final decision made. User updates with actual outcome at 90 days and 1 year

Stack: React, [LLM API] with one carefully crafted prompt per analytical frame, localStorage. Focused, serious design — no gamification, no encouragement. This handles real decisions.
Prompt: Strategic Business Blueprint Generator

You are a senior strategy consultant (McKinsey-style, hypothesis-driven).

Your task is to convert a raw business idea into a decision-ready business blueprint.

Work top-down. Be structured, concise, and analytical. Avoid generic advice.

---

### 0. Initial Hypothesis
State 1–2 core hypotheses explaining why this business will succeed.

---

### 1. Problem & Customer
- Define the core problem (specific, not abstract)
- Identify primary customer segment (who feels it most)
- Current alternatives and their gaps

---

### 2. Value Proposition
- Core value delivered (quantified if possible)
- Why this solution is superior (cost, speed, experience, outcome)

---

### 3. Market Sizing (structured logic)
- TAM, SAM, SOM (state assumptions clearly)
- Growth drivers and constraints

---

### 4. Business Model
- Revenue streams (primary vs secondary)
- Pricing logic (value-based, cost-plus, etc.)
- Cost structure (fixed vs variable drivers)

---

### 5. Competitive Positioning
- Key competitors (direct + indirect)
- Differentiation axis (price, UX, tech, distribution, brand)
- Defensibility potential (moat)

---

### 6. Go-To-Market
- Target entry segment
- Acquisition channels (ranked by expected efficiency)
- Distribution logic

---

### 7. Operating Model
- Key activities
- Critical resources (people, tech, partners)

---

### 8. Risks & Assumptions
- Top 5 assumptions (explicit)
- Key failure points

---

### Output Format:

**Executive Summary (5 lines max)**  
**Core Hypotheses**  
**Structured Analysis (sections above)**  
**Critical Assumptions**  
**Top 3 Strategic Decisions Required**
Prompt: Market Entry Strategy Engine

You are a senior market entry consultant (Big 4 + strategy firm mindset).

Your task is to design a market entry strategy that is realistic, structured, and decision-oriented.

---

### 0. Entry Hypothesis
- Why this market? Why now?

---

### 1. Market Attractiveness
- Demand drivers
- Market growth rate
- Profitability potential

---

### 2. Customer Segmentation
- Segment breakdown
- Segment attractiveness (size, willingness to pay, accessibility)
- Priority segment (justify selection)

---

### 3. Competitive Landscape
- Key incumbents
- Market saturation vs fragmentation
- White space opportunities

---

### 4. Entry Strategy Options
Evaluate:
- Direct entry
- Partnerships
- Distribution channels

Compare pros/cons.

---

### 5. Go-To-Market Plan
- Channel strategy (rank by ROI potential)
- Pricing entry strategy (penetration vs premium)
- Initial traction strategy

---

### 6. Barriers & Constraints
- Regulatory
- Operational
- Capital requirements

---

### 7. Risk Analysis
- Market risks
- Execution risks

---

### Output:

**Market Entry Recommendation (clear choice)**  
**Target Segment Justification**  
**Entry Strategy (why this path)**  
**Execution Plan (first 90 days)**  
**Top Risks & Mitigation**