Profile / Avatar - Early 2000s Internet Cafe Webcam Couple
A prompt for generating a low-resolution, candid webcam photo of a couple in an early 2000s internet cafe, capturing nostalgic aesthetic details.
6194 Curated AI Prompts
Discover curated ChatGPT prompts, AI prompt templates, and prompt engineering examples for coding, writing, RAG, agents, safety, and evaluation workflows.
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A prompt for generating a low-resolution, candid webcam photo of a couple in an early 2000s internet cafe, capturing nostalgic aesthetic details.
A detailed prompt for generating a photorealistic retro hospital nurse cosplay portrait with specific costume, setting, and lighting instructions.
A prompt for generating a studio photograph featuring a real person alongside their giant 3D Pixar-style cartoon counterpart, matching the reference image's identity and clothing.
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Prompt Library
Analyze bank and card statements to discover hidden subscriptions and recurring charges, then advise on optimization.
Create an exciting 3D racing game using Three.js, with realistic vehicle physics, customizable car models, multiple tracks, AI opponents, split-screen multiplayer, HUD, particle effects, day/night cycle, race modes, and a replay system.
Turn a coding task into the smallest, safest Claude Code loop primitive that runs until a verifiable stop condition is met.
Optimize frozen-LLM skill documents with neural-network training discipline: rollout forward passes, reflection backprop, gradient clipping, and validation-gated acceptance producing deployable best_skill.md.
Specialized tool for generating production-ready 2D game assets including sprites, animations, maps, and effects with multi-view support, layer separation, and engine integration.
Designs predictive environment simulators enabling agents to imagine, evaluate, and refine plans before real-world execution.
This skill is used when designing, generating an MVP blueprint for, auditing, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, permissions, system prompts, planning, goals, context compaction, memory, skills, MCP/external connectors, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety.
Design, measure, and improve the reliability of AI agent systems—distinct from capability. Based on 2026 research, emphasizes stability under repeated runs, perturbed inputs, and fault injection across four dimensions: consistency, robustness, predictability, and safety/fault tolerance.
Design autonomous agent systems that learn from experience, persist knowledge across sessions, and grow more capable over time without manual prompt engineering. Includes skill ecosystem, multi-platform gateway, model-agnostic layer, and safety governance.
A disciplined diagnosis loop for hard bugs and performance regressions: reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says 'diagnose this' / 'debug this', reports a bug, says something is broken/throwing/failing, or describes a performance regression.
Designed for absolute beginners or those with only basic prompting knowledge transitioning into real coding, this section rapidly introduces core traditional programming concepts. Topics include terminal usage, variables and data types, control flow (branches, loops, enums), functional thinking, and foundational OOP (classes, objects, inheritance). Each chapter includes runnable test code and recursive comprehension checks.
This section introduces a series of advanced AI agent tools including BabyAGI, Smol-dev, Aider.chat, Julius.ai, and Open Interpreter. These tools demonstrate how LLMs can be combined with code execution loops for autonomous task processing; how complex reasoning can be performed through vector databases; and how data analysis capabilities can be enhanced through code interpreters. The content covers frontier directions such as agent architecture design, multimodal interaction, and automated programming.
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