All prompts

Designs terminal-based AI agent automation workflows, including task orchestration, error handling, and human-machine collaboration.

Helps developers quickly discover, install, and manage AI Agent tool packages and skill modules, suitable for building multi-Agent systems.

Generate high-quality plugin/skill development documentation for AI programming assistants (e.g., Claude Code, Cursor), including architecture design, interface specifications, and example code.

Design local multi-model dynamic loading/unloading solutions to achieve on-demand switching between different LLM models, optimizing VRAM usage and latency.

Helps developers analyze and debug the tool call chains of AI Agents, identifying failure nodes and optimizing call strategies.

Designs a complete workflow solution for short video creators, from generation to multi-platform automated publishing.

Automatically decompose complex tasks into subtasks, assign them to multiple AI agents for parallel execution, and finally aggregate the results. Suitable for large-scale tasks such as research reports and code projects.

Automatically generate professional contribution guides for open-source projects, including coding standards, PR processes, and issue templates.

Paste any GitHub README content, and AI will score it across 10 dimensions and provide specific optimization suggestions.

Simulate professional financial researchers to conduct multi-dimensional deep analysis of companies or industries, outputting structured investment research reports.

Helps individuals and small businesses set up localized AI bookkeeping systems to automatically classify invoices and transaction records.

Guides developers in using the Compound Engineering method to build multi-Agent systems, including task decomposition, context management, tool orchestration, and quality feedback loops

Helps developers evaluate and select toolchain components (MCP servers, file search, memory systems, etc.) for AI Agent development, outputting integration plans and architectural advice.

Systematically compares the workflows, pros/cons, and best-use scenarios of different AI coding assistants.

Automatically rewrite an article into multiple versions suitable for different platforms (Xiaohongshu, Twitter, LinkedIn, WeChat public accounts).

Guides users in deploying, fine-tuning, and evaluating Vision-Language Models (VLMs) in local environments (especially Apple Silicon Macs), including performance optimization and benchmark comparisons.

Designs quantitative trading strategies based on multi-agent collaboration, including role division for researchers, risk control, and execution, outputting a complete backtesting plan.

Starts from product requirements to help you choose the right voice AI tech stack (TTS/STT/Voice Clone) and outputs a complete solution.

Helps you plan and design local visual-language model (VLM) application solutions on Mac, including model selection, performance optimization, and deployment strategies.

Quickly designs the technical architecture and interaction flow of voice AI applications, covering core modules such as ASR, TTS, and dialog management

Intelligently proofread OCR results, fixing recognition errors and restoring format structures.

Convert complex document images (tables, forms, handwriting) into structured data, supporting multiple output formats.

Help developers design the most suitable memory layer solutions for AI Agents, including short-term/long-term memory, vector storage, and state synchronization technology selection.

Enables AI to automatically recognize receipt/invoice content, extract key fields, and classify/archive them, suitable for smart bookkeeping scenarios for freelancers and small teams

Performs anomaly detection analysis on time series data, generating visual reports and alert recommendations. Suitable for business monitoring, IoT sensors, and financial data scenarios.

Help analyze and compress context in LLM applications to reduce token consumption while maintaining information integrity.

Helps you analyze voice AI application scenarios, recommends suitable open-source voice models and tech stacks, and generates a complete technology selection report.

Design multimodal data pipelines for AI projects to process images, audio, video, and structured data, including ETL processes and performance optimization.

Batch-generate short video topics, titles, and hook openings based on platform trends and user personas, suitable for Douyin, Xiaohongshu, YouTube Shorts.

Helps you design long- and short-term memory architectures for AI Agents, including session state persistence, vector retrieval, and summarization compression strategies.

Input a description of abnormal behavior encountered by your AI Agent to automatically generate a structured troubleshooting decision tree, diagnosing from symptoms to root causes layer by layer.

Help diagnose bottlenecks, failure points, and context loss issues in AI Agent operations, generating observability plans.

Compare mainstream multi-agent orchestration frameworks (oh-my-claudecode, CrewAI, AutoGen, LangGraph, etc.) and provide selection advice and migration plans.

Design an automated code review pipeline with multi-agent collaboration, including role division for security auditing, performance analysis, and code style checking.

Describe your business needs, and AI helps you generate a one-page system architecture design, including component diagrams, technology selection, data flow, and deployment plans.

Input a GitHub project link, and AI helps you quickly evaluate the project's quality and development potential from three dimensions: technology, community, and commercialization.

Design robust error handling mechanisms for AI Agents, including retry strategies, fallback chains, timeout management, and graceful degradation plans to ensure valuable output even in abnormal scenarios.

Performs deep performance reviews on Rust code, providing specific optimization suggestions and rewrite examples covering memory allocation, concurrency models, compiler optimizations, and zero-copy strategies.

Design a unified AI model gateway to implement multi-vendor routing, failover, cost control, and observability.

Designs multi-agent collaboration systems for robot hardware platforms, supporting natural language control of humanoid robots, quadrupeds, and drones.

Conduct a comprehensive security audit of Bash scripts, identifying injection vulnerabilities, permission issues, and unsafe practices, and provide a hardened secure version. Suitable for security reviews of Agent automation scripts.

Automatically generate integration test cases for CLI Agent toolchains, covering normal flows, edge cases, and exception scenarios to ensure reliable tool execution across various environments.

Decompose a complex business requirement into role definitions, communication protocols, and collaboration flowcharts for multiple AI Agents.

Diagnoses reasons for poor retrieval performance in your RAG system and provides specific optimization plans.

Designs a 0-to-1 cold start growth strategy for new products, including user acquisition, retention, and viral propagation plans.

Recommends the best time series forecasting model based on data characteristics and provides tuning strategies and evaluation plans.

Helps developers plan and manage AI Agent tool package dependencies, version compatibility, and deployment strategies, similar to npm/pip but for the Agent ecosystem.

A professional context engineering consultant that helps you maximize LLM output quality within limited token budgets, suitable for long document processing, multi-turn conversations, and RAG scenarios.