All prompts

Based on the target company, position, and interview round, it generates high-quality interview rhetorical questions to help candidates demonstrate deep thinking and genuine interest during the rhetorical process

Based on users' learning habits, tool preferences, and knowledge areas, a complete personal knowledge management system solution is designed, including tool selection, workflow design, and a progressive roadmap

The best solution is recommended from dozens of free LLM API services based on requirements, along with examples of integrated code.

Designed incremental data processing and persistent memory pipelines for long-cycle AI Agents, supporting efficient state recovery and contextual recall

Design a local knowledge base management solution based on Markdown files, combined with AI to achieve intelligent retrieval, automatic tagging, bidirectional link recommendations, and knowledge graph visualization

Enter any open-source project README content, and AI uses structured templates to help you understand the core of the project in just one minute: what problem is solved, technical architecture, applicable scenarios, difficulty to get started, and competitor comparisons.

Describe your task requirements, and the AI will analyze which model to use (GPT-4o/Claude/Gemini/DeepSeek/local models), offering recommendations and reasons from four dimensions: cost, speed, quality, and privacy.

Guide to deploying efficient TTS speech synthesis services in a pure CPU environment, without the need for a GPU. Suitable for voice application development in edge devices, embedded systems, or cost-sensitive scenarios.

Describe your document types and scale to generate a complete local semantic search system deployment plan, including embedding model selection, vector database configuration, and search API code.

Evaluate whether a device is suitable for running local large models and generate a complete deployment plan including performance estimates and optimization suggestions.

Automatically generate the optimal .cursor/rules configuration file based on your project's tech stack and team conventions, ensuring the AI coding assistant perfectly fits your project.

Design a browser automation solution for AI Agents, covering practical strategies such as anti-fingerprint detection, behavior simulation, and CAPTCHA bypass.

Automatically generate SEO-optimized series of blog posts, tutorials, and comparative analyses based on open-source projects to build a content matrix that drives organic traffic growth.

Design a knowledge graph-driven intelligent Q&A system based on the GraphRAG architecture, supporting multi-hop reasoning and entity-relation extraction.

Input a product name and a list of competitors to automatically generate a structured SWOT analysis report, featuring in-depth comparisons across strengths, weaknesses, opportunities, and threats.

Input a core message to instantly generate different styles of copy adapted for platforms like Xiaohongshu, Twitter, LinkedIn, and WeChat Official Accounts.

Systematically review code change plans generated by AI coding agents, providing structured feedback to ensure code quality and architectural consistency.

Upload suspicious file descriptions or characteristics; AI analyzes the true file type, detects spoofing, assesses potential security risks, and provides disposal recommendations.

Design a complete workflow for task assignment, skill reuse, and progress tracking for AI coding agent teams, suitable for team-level agent management scenarios.

Product requirements, technical architecture, and deployment plan for building an open-source AI voice synthesis studio from scratch, supporting voice cloning and style transfer.

System prompt template for centrally managing multiple AI coding agents, enabling automatic task assignment, progress tracking, and result aggregation.

Simulate a senior architect conducting a comprehensive review of technical solutions, providing scores and improvement suggestions from dimensions such as feasibility, scalability, security, and cost.

Input a note topic to automatically generate 10 Xiaohongshu-style viral titles and cover copies, including high click-through rate elements like emojis, numbers, and pain-point keywords.

Helps you systematically evaluate GraphRAG solutions, providing comprehensive comparative analysis from graph construction and retrieval strategies to deployment costs.

Generates a weekly multi-platform content calendar including titles, copy, and optimal posting times based on topics and target audiences.

Analyzes technical debt in codebases, categorizes it by priority, and generates an executable repayment plan suitable for team sprint planning.

Help you design an AI Agent capable of self-learning and growth, automatically expanding its skill tree starting from seed skills.

Automatically generate standardized CHANGELOGs and user-friendly release notes based on Git commit history and PR lists.

Help you optimize local LLM inference performance on Mac, including memory management, batch processing configuration, and SSD caching strategies.

Help you design, develop, and debug Claude Code plugins, including complete project structures and best practices.

Deeply compare the performance, ecosystem, and applicable scenarios of Rust async runtimes like Tokio, async-std, and smol, outputting a selection report.

Generate a complete automated PR review process for open-source projects, including code quality checks, security scanning, test coverage requirements, and auto-merge strategy configuration.

Design a complete canary release strategy for LLM applications, including model version switching, traffic allocation strategies, rollback mechanisms, and effect evaluation metric systems.

Describe the 3D scene you want in natural language, and the AI will help you generate detailed scene descriptions and parameter configurations usable for generative 3D models.

Describe your application interface and user flows; AI generates end-to-end automated test scripts based on visual recognition, without relying on DOM selectors.

Design a graph-structured RAG system that combines knowledge graph entity-relation reasoning with vector retrieval to improve complex Q&A quality.

Design anomaly detection rules for AI Agent systems, including monitoring strategies for abnormal token consumption, tool call anomalies, loop detection, and security boundary breaches.

Evaluate and design speculative decoding solutions for LLM inference, comparing the performance of different draft models and verification strategies.

Input multiple open-source TTS model names to automatically generate a multi-dimensional comparative evaluation framework covering audio quality, multilingual support, voice cloning, latency, and deployment difficulty.

Input model service architecture information to automatically generate a gray release operation manual, including canary deployment, traffic switching, rollback strategies, and monitoring alerts.

Input AI application requirements to generate a deployment solution comparison covering Web/Desktop/Mobile, including a decision matrix for cost, performance, and privacy dimensions.

Configure Speculative Decoding for your LLM inference service to achieve 2-5x inference acceleration via methods like Block Diffusion, including model selection, parameter tuning, and performance benchmarking.

Generate task assignment rules, file locking strategies, and code merge conflict prevention plans for multi-agent coding teams, enabling AI coding teams to collaborate efficiently like real development teams.

Input your LLM inference scenario parameters to generate a speculative decoding acceleration plan evaluation report, including draft model selection, batching strategy, expected speedup ratio, and deployment recommendations.

Input GitHub repository information to automatically generate production-grade docker-compose.yml, Dockerfile, .env templates, and health check configurations, supporting GPU passthrough and reverse proxies.

Input your speech synthesis requirements to automatically generate a complete open-source TTS model comparison evaluation plan and Python test script.

Rapidly build multi-agent collaborative workflows using the official OpenAI Agents SDK, including a complete code template for agent definitions, handoff mechanisms, guardrails, and tool integration.

Automatically decomposes complex tasks into executable sub-tasks and generates execution plans with dependencies, suitable for multi-agent collaboration scenarios.