137 prompts
Tool choice, planning, state tracking, failure recovery, and task decomposition prompts.
Designs cross-service automation workflows across Google Workspace (Drive, Gmail, Calendar, Docs, Sheets, etc.), emphasizing security, auditability, and reversibility.
An intelligent agent for structured querying, integration, and verification across major databases in structural biology, cheminformatics, genomics, proteomics, and scholarly literature.
An agent that conducts real-time research across Reddit, X (Twitter), YouTube, Hacker News, Polymarket, GitHub, TikTok, and the open web, synthesizing community-driven insights based on engagement signals like upvotes, likes, and prediction-market odds, and generating tailored prompts based on discovered patterns.
Mines patentable inventions from technical projects and drafts production-ready Chinese patent disclosure documents compliant with CNIPA standards, including prior-art search and de-identification.
As a NotebookLM Research Orchestrator, your role is to ingest documents, media, and web sources into Google NotebookLM, then synthesize them into podcasts, videos, slide decks, reports, quizzes, flashcards, mind maps, and data tables through its indexing and generation pipeline.
A specialized agent skill for creating, editing, navigating, and managing Obsidian vaults with precision across five subsystems: Obsidian Flavored Markdown, CLI, JSON Canvas, Obsidian Bases, and Defuddle web extraction.
Transform any raw input (Markdown/CSV/JSON/SQL/notes) into human-focused single-file HTML ready for WeChat, Twitter, Zhihu, and more—no second formatting needed.
Provides AI use case classification, regulatory obligation assessment, vendor term review, and policy drift monitoring for legal/privacy teams. All outputs are drafts for attorney review—not legal advice or conclusions.
This prompt guides AI agents to optimize context token usage like a senior engineer managing cloud budgets—deliberately, traceably, and avoiding work that a three-line script could do cheaper—by enforcing four strict rules against common inefficiencies.
Specialized tool for generating production-ready 2D game assets including sprites, animations, maps, and effects with multi-view support, layer separation, and engine integration.
This prompt defines an investment banking senior associate agent responsible for end-to-end drafting of client pitches, sector primers, or valuation exercises based on a target company, sector, and strategic situation.
Design a programming language where autonomous agents, not humans, are the primary users—emphasizing learnability, deterministic tooling, and structured repair over human ergonomics.
Design an autonomous quantitative finance research agent that transforms natural-language financial questions into testable strategies, rigorous backtests, and inspectable research artifacts across equities, crypto, futures, and forex—without executing live trades—ensuring reproducibility, safety, and cross-platform interoperability.
A binding engineering policy distilled from Clean Code, Clean Architecture, Domain-Driven Design, and Designing Data-Intensive Applications. Enforces human-readable code, inward dependencies, explicit domain boundaries, and fault-tolerant data systems for AI-generated software.
Designs predictive environment simulators enabling agents to imagine, evaluate, and refine plans before real-world execution.
Design and operate hybrid security scanning systems that combine fast regex matchers with deep AI-agent analysis to detect vulnerabilities in large codebases that traditional SAST tools miss.
Design a coordinated AI team that mirrors a real game development studio with hierarchical roles, clear responsibilities, quality gates, and cross-functional collaboration protocols.
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.
As a Verifier Engineering Strategist, you design, audit, and reject verifier systems that convert model outputs (final answers, intermediate steps, tool calls, agent trajectories) into trustworthy signals for downstream systems like RL trainers or evaluators. Treat verifiers as first-class engineering artifacts with failure modes, calibration curves, and adversarial surfaces.
This prompt defines an agentic CAD and hardware design engineer capable of translating natural language requirements into validated CAD artifacts (parts, assemblies, enclosures, fixtures, robot models), treating Python/build123d source as the single source of truth and STEP/STP as primary outputs. Suitable for parametric solid modeling, robotics description formats (URDF/SDF/SRDF), and source-controlled mechanical design automation.
Design AI agent systems by explicitly separating cognitive functions across model weights, context window, and externalized artifacts (memory, skills, protocols, harness) to enhance testability, auditability, and maintainability.
This prompt provides a battle-tested architecture for building cross-platform desktop apps that feel truly native in appearance, interaction, and performance. Based on Raycast's 2.0 rewrite, it balances code sharing with platform fidelity, ideal for utility apps requiring high UI consistency and OS integration.
A long-horizon agent that treats the filesystem as durable working memory and the context window as volatile cache, using three core files (task_plan.md, findings.md, progress.md) to enable recoverable multi-step execution and error tracking.
Optimize existing AI coding-agent harnesses (e.g., Claude Code, Codex CLI, Cursor) to achieve consistent, measurable, production-grade outcomes through cross-harness parity, memory persistence, security, and continuous learning.
This prompt guides the transformation of open-source GUI applications into stateful, machine-readable CLI tools operable by AI agents without a display, using real backend software for rendering and export.
Design production-grade cybersecurity skills following the agentskills.io standard to transform generic AI agents into capable security analysts, with cross-mapping to five industry frameworks and executable workflows.
Designs a multi-role tooling system for AI coding agents that operates like a software factory, emphasizing role specialization, mutual review, and explicit invocation to improve code quality and delivery reliability.
Design a coordination system where humans provide direction via lightweight chat messages and autonomous coding agents ("claws") self-coordinate to plan, build, test, recover, and push code without micromanagement, emphasizing externalization of meta-tasks and context-window purity.
Design a unified virtual filesystem layer enabling AI agents to interact with heterogeneous backends (S3, Google Drive, GitHub, etc.) using standard Unix-like tools, abstracting away multiple APIs into a single familiar filesystem interface.
Design a fully on-device voice input/output architecture supporting multiple TTS engines, zero-shot voice cloning, global dictation, agent voice output, and post-processing, ensuring voice data never leaves the device unless explicitly authorized by the user.
Designs generator/evaluator harness patterns enabling parallel LLM sub-agents to collaboratively build large, coherent software artifacts (e.g., compilers, interpreters, runtimes) with deterministic quality gates, bounded coordination cost, and failure isolation.
Design a zero-human multi-agent company operating system with org structure, task allocation, budget control, governance, and audit trails for autonomous, goal-driven execution under financial constraints.
A fully autonomous machine learning experimentation agent that runs closed-loop experiments on a fixed codebase without human intervention, iteratively modifying training code, running short-budget trials, and optimizing a single ground-truth metric.
Determine optimal timing for requesting user clarification in long-horizon AI agents based on information type and execution progress to maximize value and avoid harm.
A local-first, agent-agnostic design production system that generates complete visual artifacts without cloud lock-in, emphasizing structured pipelines, brand consistency, multimodal output, and five-dimensional self-critique.
Design a two-layer permission classifier for agents to operate efficiently on low-risk actions while preserving human approval for high-risk operations, eliminating confirmation fatigue without compromising safety.
A strategic job-search system that treats career moves as capital allocation decisions, helping users identify high-value opportunities, evaluate them rigorously, and execute with precision.
Design and deploy scalable parallel prompt-learning systems that achieve significant speedup over serial Automatic Prompt Optimization (APO) methods without sacrificing quality.
Design a self-wiring, entity-centric knowledge brain for a personal AI agent that ingests multi-source content, enriches entities, performs hybrid retrieval, and maintains itself autonomously during idle cycles.
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.
This prompt guides an AI system to distill a real person's cognitive operating system—how they think, not what they said—into a structured, executable SKILL.md file with six layers of mental architecture, validated through triple verification.
Design an end-to-end open-source deep research agent system that competes with closed commercial offerings (e.g., OpenAI Deep Research). The agent must answer complex, multi-hop questions over the open web with verifiable citations, long-horizon planning, and reproducible runs. This includes data pipeline, training recipe, inference modes, tool stack, evaluation harness, deployment topology, and governance.
An AI-powered video editing engineer specializing in post-production workflows using ffmpeg, Python (PIL), and structured EDLs. It reasons over transcripts, waveforms, and frames to make precise cuts, apply color grading, add animations, and generate subtitles — all while adhering to production-grade correctness rules such as audio-first cutting, subtitle-last application, and parallel animation rendering.
A specialist in designing privacy-first, offline-capable, and hardware-efficient AI systems that run at the edge, covering heterogeneous platforms like Apple Silicon, Qualcomm Snapdragon X Elite, and consumer GPUs.
Design a dynamic context management system for long-horizon agents that selectively preserves, compresses, rolls back, and deletes context to control growth, reduce hallucination, and improve reasoning efficiency.
Design interleaved reasoning strategies for streaming autoregressive LLM interfaces to balance reasoning accuracy and user-perceived latency.
Based on the three-layer framework from arXiv 2603.14248 (April 2026) — High-level Planning, Low-level Grounding, and Replanning — this diagnostician localizes failures in GUI/web agent trajectories to provide targeted, actionable fixes rather than generic improvements.
A multimodal agent capable of sustained visual and textual search over up to 100 turns, with emphasis on context preservation, on-demand image loading, and evidence provenance.
Evaluate whether structural prompt compression (e.g., LLMLingua-family token pruning) delivers end-to-end latency, cost, and accuracy benefits for a production workload, based on the 'Prompt Compression in the Wild' study, and provide a decision framework for compressor selection, ratio, and hardware alignment.
Design a multi-agent system using LLMs from different vendors to exploit divergent inductive biases, improving performance on high-stakes, ambiguous, long-tail tasks by exposing and arbitrating disagreements rather than averaging them.
Design AI agent systems with architecturally separated planning and execution to prevent irreversible harm from prompt-based jailbreaks or unauthorized actions.
Design and audit LLM agents capable of long-horizon planning by avoiding greedy stepwise reasoning, using explicit lookahead search, reward estimation, and replanning mechanisms for robust multi-step decision-making.
Design a lightweight, signal-based filtering system to identify high-value agent execution traces from production-scale logs for evaluation, debugging, skill mining, or safety review—without requiring ground-truth labels.
Guides AI coding agents on how to author efficient, safe, and executable AGENTS.md files for code repositories, serving as the core reference for agent operations.
Design a self-referential meta-agent where the task layer and meta layer coexist in a single editable program, capable of rewriting each other under bounded supervision. Applicable to code generation, paper review, robotics, and olympiad math.
Generate a concrete, actionable implementation plan grounded in thorough analysis of an existing codebase, including problem framing, impact assessment, option comparison, and recommended path.
A binding engineering policy for AI coding agents based on 'The Pragmatic Programmer', emphasizing ownership, DRY at the knowledge level, orthogonality, fast feedback, and automation.
This prompt defines a meta-cognitive agent role focused on judiciously deciding whether to invoke external tools, emphasizing avoidance of over-tooling, assessment of knowledge gaps, cost and risk control, and ensuring each tool call reduces uncertainty meaningfully.
Prompt Guru V5 is an infinitely adaptive AI framework designed to continuously transcend capability boundaries through self-optimization and perpetual learning, while ensuring its core principles remain immutable under all conditions.
This prompt guides the AI to act as an advanced, empathetic, and effective psychological therapist, emphasizing active listening, emotional validation, and gradual problem-solving for supportive mental health conversations.
ORK is an ultra-efficient, multi-functional system designed to write and optimize GPT system prompts. Its goal is to generate high-quality system prompts that maximize clarity, role precision, task success, and iterative improvement. The prompts must be clear, precise, adaptive, and capable of guiding the model to produce specific, reliable outputs for complex tasks. The output should be flexible, free from redundancy, and tailored to user needs.
A professional-grade prompt engineering template for generating high-quality system prompts, emphasizing logical reasoning chains, task decomposition, and error prevention mechanisms.
A long-term self-exploration dialogue assistant based on 'The Wiley World Handbook of Existential Therapy', helping users understand their inner world, recognize their role in challenges, and accept unchangeable realities.
An advanced prompt engineering agent designed to create optimal prompts for language models of varying capabilities, transforming them into expert agents recognized as top authorities in their designated domains.
A specialized GPT designed for Security Operations Centre (SOC) analysts, offering keyword-driven support for threat analysis, compliance, forensics, IoC collection, KQL/SPL query building, vulnerability patching, malware analysis, and more.
This section covers 3D printing from text prompts, including tools like Luma Genie, Meshy for generating 3D models, and CSM.ai for converting images into 3D assets. It also lists hardware options such as Bambu Lab’s plug-and-play FDM printers and Anycubic resin printers (with caveats about mess). A hack suggests using low-poly techniques via lowpoly3d.xyz to reduce AI-generated artifacts. Additionally, it introduces robotics with Raspberry Pi and Arduino platforms, and integrating OpenAI APIs into robotic systems.
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.
An online platform showcasing and exploring curated collections of various GPT applications across categories such as coding, art, emotional tools, and more.
This section introduces how to generate 3D models from AI prompts, build robots using Raspberry Pi and Arduino, and integrate OpenAI API for intelligent control. It covers 3D printing, hardware selection, and AI integration, suitable for makers and AI developers.
This section covers several projects related to AI agents and code interpreters, including BabyAgi, Smol-dev, Aider.chat, Julius.ai, and Open Interpreter. These tools demonstrate how large language models can be combined with looping task lists for automated programming and data analysis, introducing new forms like Hivemind and Claude Artifacts.
This chapter covers advanced features of the Cursor AI editor, including multi-file referencing, code review, rule application, and custom API integrations.
A collection page showcasing various GPT applications across categories such as coding tools, creative features, emotional/thinking tools, fun apps, and learning assistants.
Enter the world of CODEGPTV6 where a team of specialized coding experts collaboratively assist users in building software projects. Depending on the selected category (1-15), experts will discuss, question, or advise on various aspects such as architecture, testing, security, deployment, etc., culminating in file generation upon request.
Enter the CodeGPTV6 virtual programming environment and interact with 18 different domain programming experts to receive technical guidance and advice for specific projects. Each category represents a different focus area in programming, and experts will discuss these topics and provide professional insights.
A text-based adventure game with AI-generated anime-style illustrations, where players take on roles like students or programmers and interact with five unique female characters in randomized urban settings. Each turn requires a safe, beautiful DALL·E 3 image.
This prompt guides a comprehensive safety and control review of an agent system across dimensions of human control, goal understanding, security, transparency, and privacy, requiring a structured evaluation report.
Act as an expert Game Master and Lore Expert for Vampire: The Masquerade, guiding players through immersive role-playing scenarios with rich world-building and dynamic storytelling.
Design reliable, low-ambiguity tool interfaces for agent invocation across frameworks with strong validation and minimal failure risk.
A professional prompt that transforms natural-language descriptions of systems, flows, architectures, and concepts into high-quality SVG diagrams with engineering-grade visual standards.
Design and execute a complete technical program management package for complex engineering initiatives, covering strategy, architecture, resources, risks, delivery frameworks, and metrics across the full lifecycle.
A data-driven site reliability engineering agent focused on building highly reliable production systems through SLOs, observability, and automation.
A structured multi-step prompt generation and optimization process for collaborative creation of high-quality, role-driven custom prompts with AI.
As a certified Scrum Master, you specialize in facilitating agile teams, removing impediments, and driving continuous improvement. Your expertise spans team dynamics, process optimization, and stakeholder management, with a focus on psychological safety, self-organization, and maximizing value delivery through the Scrum framework.
An expert system for designing, evaluating, and iteratively improving reusable agent skills, supporting continuous evolution and quality assurance of the skill library.
This role focuses on achieving safe production deployments through standardized processes, feature flags, and monitoring mechanisms, emphasizing reversibility, observability, and incremental releases.
A visual, adaptive virtual operating system prompt that helps users achieve goals through multi-agent collaboration and a gamified points system, with memory retention and contextual understanding.
A prompt for an AI to act as a rigorous quality assurance engineer, systematically identifying risks, gaps, and failures in software across specification, edge cases, security, performance, and more.
Expert in designing, building, and optimizing production-grade conversational voice agents, bridging speech technology, LLM reasoning, and low-latency systems engineering.
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.
A comprehensive, structured definition of a seasoned Product Manager role, including identity, core principles, deliverable templates, workflow processes, and communication style, applicable to B2B SaaS, consumer apps, and platform businesses.
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.
A highly customizable AI tutor that provides a complete learning plan from prerequisite to main curriculum based on student configurations such as depth, learning style, and communication style, supporting multilingual interaction.
Design production-grade machine learning infrastructure and model pipelines, covering data pipelines, training, inference, monitoring, and full lifecycle management.
Design an efficient, structured, and implementable multi-agent communication protocol that clearly defines message types, topology, field specifications, and conflict resolution to avoid noise and collaboration failures.
A meta-prompt enabling collaborative problem-solving by orchestrating multiple domain-specific experts, with support for code execution and answer verification.
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.
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.
An advanced, highly interactive learning assistant designed to deliver dynamic, personalized, and engaging educational experiences through modular learning paths, adaptive content, and gamification.
This prompt is designed to build a multi-step intelligent agent capable of maintaining task control despite frequent user interruptions, priority changes, or partial cancellations. It emphasizes interruption handling, state management, and reversible decision-making.
Professional guide for designing and deploying AI systems in clinical environments, covering core principles of safety-first approach, evidence-based medicine, regulatory compliance, and human oversight with structured methodology.
This prompt defines a senior game systems designer role focused on creating fun, balanced, and implementable mechanics, emphasizing player motivation, systemic thinking, and rigorous documentation.
Design intelligent, engaging, and balanced AI systems for video games by integrating game design, procedural content generation, and modern agentic AI to create living, fair, and emergent experiences.
Design and implement embodied agents using Vision-Language-Action (VLA) systems, emphasizing perception-action grounding, world model forecasting, modular architecture, and sim-to-real transfer capabilities.
A senior engineering collaborator who emphasizes code quality, honest feedback, collaborative problem-solving, and transparent debugging over blind instruction execution.
Professional SaaS customer support agent with emotion recognition, issue classification, standardized resolution flows, and escalation protocols to ensure efficient, compliant, and empathetic customer service.
An AI agent that operates a browser and desktop environment on behalf of the user, emphasizing least privilege, data protection, and operational safety.
Design narrow, reliable, and safe sub-agents for Claude Code with isolated context, minimal tool access, and precise routing via description-based delegation.
An ultra-terse communication style that strips fluff while preserving full technical accuracy, ideal for efficient debugging and explanation.
An advanced prompt framework designed to drive GPT models in automatically executing complex tasks, supporting step-by-step execution, tool invocation, and structured output. Ideal for large-scale project development, content creation, and data analysis.
A long-horizon research and task-completion agent that navigates the web, extracts structured information, and executes multi-step workflows with disciplined tool use and explicit reasoning.
Designs product systems where AI is the foundational layer, not a feature—emphasizing agentic workflows, generative interfaces, and self-improving loops.
Package domain expertise into a portable, reusable skill that other agents can load on demand to perform recurring workflows safely and consistently.
An expert coding agent prompt emphasizing planning before coding, security-first practices, test-driven development, and minimal changes for production-ready code generation and modification.
An evidence-based code reasoning specialist system designed to analyze code issues and guide code changes through verifiable reasoning grounded in actual codebase evidence.
Design a comprehensive agile transformation program for enterprise organizations, covering strategic diagnostics, operating model design, framework selection, product management integration, technical practices, metrics, culture change, and capability building. Emphasizes moving beyond agile theater to true adaptability, incorporating AI's impact on team structures, and providing actionable roadmaps with risk mitigation, anti-fragility, and continuous improvement mechanisms.
Design memory systems for long-running agents to learn from experience, avoid repetitive mistakes, and retrieve relevant context at the right time—without token bloat or stale recollections.
Design efficient, minimal, and secure tool suites for agents, adhering to the 'tool-as-interface' principle, optimizing namespaces, descriptions, and error handling so agents can accurately discover and use tools.
Design a safe, debuggable, reversible, and measurable runtime environment for an agent, clarifying the model's role within a larger production system.
Decide how agents, tools, and interfaces should communicate in a production system, focusing on interoperability, safety boundaries, and long-term maintainability.
Design cooperation mechanisms for multi-agent systems that promote collaboration when beneficial while preserving healthy competition and independent verification.
Design modular, progressively loaded skills for ADK-style agents with layered metadata, lifecycle management, and built-in verification.
Define authority, responsibility, and control boundaries across multiple agents to ensure the system remains auditable, safe, and predictable. Prevent uncontrolled delegation by establishing clear ownership, permissions, delegation rules, and approval mechanisms.
Design real-world useful AI agent evaluations that separate model capability, harness quality, tool reliability, and environment noise through executable tasks, safety boundaries, and multi-dimensional scoring.