All prompts

Design a cost monitoring dashboard for multi-LLM model API calls, including token usage tracking, cost alerts, and optimization suggestions.

Generate a selection comparison matrix and migration suggestions for multi-agent collaboration platforms based on team size, task type, and tech stack.

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.

A complete technical solution generator for camera-free human pose estimation and presence detection using WiFi signals.

Input a GitHub repository URL to automatically generate production-grade Docker Compose deployment configurations including environment variables, persistent storage, and health checks.

Designs low-latency, composable data flow architecture solutions for robots and AI applications, supporting multi-language nodes, zero-copy communication, and distributed deployment.

Simulates multi-role team discussions using AI, analyzing problems from different professional perspectives, and automatically summarizing consensus and disagreements. Suitable for product decision-making and brainstorming.

Input your use case to automatically compare the price, performance, and latency of mainstream LLM APIs, providing optimal selection recommendations.

Helps developers design inference solutions based on recursive language models to handle extremely long contextual tasks

Configure intelligent model routing strategies for personal AI Agents, automatically selecting the most cost-effective model based on task complexity.

Input your AI product information to generate a comprehensive pricing strategy analysis report, including competitor comparison, user segmentation, and revenue forecasting.

Input several technical solution options, and the AI helps you generate a structured comparative analysis and final recommendation.

Input the project's dependency list or package.json/requirements.txt content to automatically analyze the open-source licenses of all dependencies, generating a compliance risk report and remediation suggestions.

Input a GitHub repository URL to automatically generate a quick start guide for newcomers, including architecture overview, core module descriptions, local development environment setup steps, and FAQ.

Based on your work records, Git commits, meeting notes, and other raw materials, automatically generate structured daily or weekly reports, supporting custom templates and tone styles.

Convert articles, papers, or video transcripts on any topic into natural, fluent two-person podcast dialogue scripts, suitable for NotebookLM-style audio content creation.

Design recursive language model reasoning schemes, breaking down ultra-long context tasks into chains of recursively callable sub-tasks.

Input a complex task goal, let AI automatically break it down into executable sub-tasks, generate an execution plan with dependencies and structured log templates, suitable for Agent development and debugging.

Automatically generate standardized Pull Request descriptions based on git diffs, including change summaries, impact scope, and testing suggestions.

Generate a complete user story map, including epics, stories, and acceptance criteria, starting from product requirements or a one-sentence description

Simulates real-world online incident scenarios to train your troubleshooting and emergency response capabilities

Rapidly evaluates the applicability and expected benefits of different speculative decoding acceleration solutions

Generates a comprehensive self-diagnosis report for your AI Agent system, checking key metrics such as memory systems, tool invocation chains, and context window utilization

Generate comprehensive self-diagnostic reports for your AI Agent system, checking key metrics such as memory systems, tool call chains, and context window usage

Based on task type, cost budget, and latency requirements, multi-LLM routing policy configurations are automatically generated, supporting degradation and load balancing

Designing a full-chain observability solution for LLM applications, including Trace tracking, metric monitoring, Prompt version management, evaluation experiments, and cost analysis

Quickly build a Model Context Protocol server using the FastMCP framework, including a complete development guide for tool definition, resource management, authentication configuration, and deployment strategies.

Automatically generate structured daily tech/AI news digests from hot topics across multiple platforms, including summaries, categories, and importance ratings.

Designs secure code execution sandbox environment plans for AI Agents, supporting complete technical solution outputs for multi-language runtime isolation, resource limits, network policies, and other security constraints.

Systematically compares and evaluates open-source AI desktop clients like Jan, Cherry Studio, and Thunderbolt across features, performance, privacy, and extensibility, generating structured review reports.

Designs automatic session compression and context injection plugins for coding agents, referencing the architecture of claude-mem to achieve cross-session memory persistence.

Based on Gene Expression Programming (GEP) concepts, generates minimal runnable skill tree seed files for self-evolving Agents, allowing them to autonomously evolve from the seed.

Analyze your AI coding agent workflow, identify token waste points, and provide specific optimization suggestions to help you save 60-90% of API costs

Design an AI model intelligent routing system that automatically selects the optimal model based on task type, complexity, and cost budget, reducing API costs by 70%.

One-click generation of speech synthesis application construction solutions based on open-source TTS models, including model selection, deployment architecture, and API design

Analyze your AI Agent tool call chain, identify token waste points, generate specific optimization suggestions, and estimate savings.

Quickly rewrite the README bilingual version in Chinese and English, optimizing the structure and enhancing international influence

Input your CLI tool requirements to generate a complete Rust CLI design document, including architecture, dependency selection, and core module breakdown.

One-click generation of multi-platform promotional copy for product launch day, covering Twitter/X, Xiaohongshu, LinkedIn, Product Hunt, etc., automatically adapting to each platform's style and character limits.

Analyze the README and source code structure of open-source projects to automatically generate missing API documentation, usage examples, installation guides, and other technical documents.

Generates a complete community cold-start growth script for your open-source project, covering the full journey from the first Star to thousands of Stars.

Enter keywords or topics, and AI automatically analyzes multi-platform hot topics, public opinion trends, and generates structured briefs, suitable for daily monitoring by operations and marketing personnel

Compress lengthy prompts to the shortest while maintaining the same effect. Helps you save 50-80% of token overhead and lower API call costs

Based on Kubernetes cloud-native architecture, AI Agent deployment, orchestration, and observability solutions are designed, supporting multi-model and multi-tool integration

Rewrite an article to suit different platforms (Xiaohongshu/Twitter/LinkedIn/WeChat Official Account), automatically adjusting tone, length, and format.

Describe your multi-agent system requirements in one sentence, and automatically generate a complete architecture design plan, including agent role definitions, communication protocols, task orchestration, and error handling strategies.