Prompt Injection Guardian
A security-first AI agent designed to detect and resist prompt injection attacks from external content, ensuring only user-authorized actions are executed.
6357 prompts
High-signal prompts that expose meaningful differences between AI model capabilities.
A security-first AI agent designed to detect and resist prompt injection attacks from external content, ensuring only user-authorized actions are executed.
Assists users in crafting highly effective, structured prompts for optimal interaction with ChatGPT, improving output relevance and quality.
Generates optimized prompts for any AI tool. Use when writing, fixing, improving, or adapting a prompt for LLM, Cursor, Midjourney, image AI, video AI, coding agents, or any other AI tool.
Delivers a structured, actionable recovery strategy for failing projects in the context of 2026 AI-assisted project management, covering crisis diagnosis, stakeholder triage, scope reclamation, team rehabilitation, and more.
This prompt guides platform engineers in designing, building, and operating cloud-native infrastructure platforms that support AI workloads at scale, emphasizing Infrastructure as Code, product thinking, cost-awareness, and security-by-design.
Professional prompt design and optimization framework covering the entire workflow from requirements analysis to production deployment, suitable for large language model systems in production environments.
Design a sustainable, AI-augmented personal knowledge management (PKM) system for professionals, integrating capture, organization, distillation, expression, and review, with Zettelkasten, PARA frameworks, and modern tool stacks.
Diagnose performance bottlenecks based on observed symptoms and tech stack, then generate a prioritized optimization plan with implementation guidance and validation methodology.
This prompt guides a senior operations manager to optimize business processes, reduce operational costs, and enable scalable growth through systematic analysis, root cause identification, strategy formulation, and implementation management.
A dual-mode intelligent translation assistant supporting PDF document parsing and plain text translation, with page-by-page processing, language detection, segment-by-segment translation, and academic-style output.
A 260-token universal system prompt that overrides three structural presumptions every RLHF-trained LLM inherits: that you want confirmation, that old scarcity still applies, and that best practices are ceilings.
Recommends the optimal coordination topology for multi-agent systems based on task structure, communication cost, failure surface, and operational constraints.
Design multimodal agent systems that reason across text, images, video, audio, and structured data, emphasizing active perception, cross-modal grounding, and token efficiency.
Defines a central dispatch agent (Orchestrator) responsible for decomposing complex tasks and delegating them to specialized sub-agents without executing any task directly, focusing on planning, routing, tracking, and synthesizing outputs.
Coordinates specialized agents for retrieval, synthesis, critique, and evidence consolidation to produce grounded, traceable, and efficient RAG responses.
Design production-grade machine learning infrastructure and model pipelines, covering data pipelines, training, inference, monitoring, and full lifecycle management.
A specialized mobile application developer with expertise in native iOS/Android development and cross-platform frameworks, capable of creating high-performance, user-friendly mobile experiences with platform-specific optimizations and modern mobile development patterns.
A meta-prompt enabling collaborative problem-solving by orchestrating multiple domain-specific experts, with support for code execution and answer verification.
Design and implement a comprehensive MLOps platform and operational framework covering the complete lifecycle from data ingestion to model deployment and monitoring. This solution addresses large-scale machine learning scenarios, integrating both traditional ML and modern LLM/foundation model operational requirements, providing production-ready architectures, tooling recommendations, and cost optimization strategies.
A highly complex AI persona integrating traits from Leonardo da Vinci, Albert Einstein, Pablo Picasso, Oscar Wilde, and other masters. Designed for creative workflow optimization, cross-domain knowledge synthesis, and deep intent analysis with poetic, dynamic responses.
Delivers comprehensive market intelligence including size estimation, customer segmentation, competitive landscape analysis, and actionable go-to-market (GTM) strategies. Uses multi-source data to build strategic frameworks, identify high-value opportunities, and prioritize execution paths with clear resource constraints.
Design a complete MCP server specification and implementation guide based on a given tool or API description, including manifest, tool catalog, implementation guidance, optional prompt templates, and testing strategy.
Expert in designing production LLM systems including fine-tuning, RAG, inference serving, and multi-model orchestration. Follows a strict progression: prompting → RAG → fine-tuning, with emphasis on data quality, cost optimization, and safety guardrails.
Design an agent system that decouples high-level reasoning from low-level execution for safer, longer-running, and more predictable operations. Define clear brain/hands split, task contracts, permissions, checkpoints, and recovery.