All prompts

Transform dry open-source project READMEs into engaging, well-structured documentation to increase project visibility and star growth.

Create an AI learning tutor that adapts to student levels, supporting Socratic questioning and knowledge graph tracking.

Design a complete multimodal content moderation solution for AI applications, covering safety and compliance strategies for text, images, video, and audio.

Describe your repetitive workflow in natural language to automatically generate executable automation scripts (supporting n8n/Zapier/Python/Shell).

Helps you build a local text-to-speech environment from scratch, supporting multilingual and multi-voice cloning.

Generate a complete product plan for a real-time voice AI dialogue system with one click, including technology stack selection, architecture design, and latency optimization strategies.

Design headless browser automation solutions for AI Agents to bypass anti-scraping detection, including fingerprint masking, request pattern optimization, and detection evasion strategies.

Use Gene Expression Programming (GEP) methods to design AI Agent evolution strategies, including gene encoding, fitness functions, and evolutionary operators.

Generates a complete self-hosted deployment plan for open-source AI clients (such as Thunderbolt, Open WebUI, LibreChat, etc.) based on user hardware conditions and requirements, including model selection, Docker configuration, and security hardening.

Generate a semantic search indexing strategy for your code repository, enabling AI coding assistants to precisely locate any code snippet.

Automatically generate multi-model routing rules for your AI coding assistant based on your use case and budget: use cheaper models for simple tasks and stronger models for complex ones, saving up to 70% in costs.

Systematically detects hallucinations and factual errors in AI-generated content, providing multidimensional verification strategies

Transform vague project requirements into structured Agent team task assignment schemes, including role definitions, task breakdowns, dependencies, and acceptance criteria. Suitable for multi-agent collaboration tools such as Squad and Claude Agent Teams.

Analyzes command safety, rates risks, and assesses potential impacts before execution to prevent accidental data loss or system damage.

Optimized strategies for the context window of encoding agents, achieving a 98% token compression rate through sandboxing tool output. Efficiency optimization for AI programming tools such as Claude Code, Codex, and Cursor.

For CI/CD systems like GitHub Actions, a comprehensive security hardening checklist is automatically generated, covering key security areas such as supply chain attack protection, key management, and permission minimization.

Design streaming UI rendering solutions for LLM applications, plan component libraries, define generative UI language formats, and optimize token efficiency

Helps review React code generated by AI coding agents, detects common anti-patterns (unnecessary re-renders, closure traps, state management errors, etc.), and provides fixing suggestions.

Design an AI-based system for real-time transcription, summary generation, and automatic extraction of action items from meeting audio.

Help developers quickly design tool discovery, MCP Server integration, and database connection solutions for AI Agents, generating complete configuration and code templates.

Generate selection recommendations and setup steps for zero-code or low-code AI Agent frameworks based on your needs.

Quickly extract core technical highlights of open-source projects to generate a one-page summary suitable for technical blogs or social media sharing.

Input a repository link to automatically analyze project architecture and generate Mermaid dependency diagrams, complexity heatmaps, and onboarding reading routes.

Design fine-grained sandbox security policies for AI Agent applications, including file system, network, and process permission controls, suitable for WebAssembly isolation and containerized deployment scenarios.

Design standardized communication protocols and message formats for multi-agent systems, supporting task distribution, state synchronization, error propagation, and result aggregation.

Help developers evaluate and select the most suitable LLM inference acceleration technologies, including speculative decoding, quantization, and KV Cache optimization.

Paste a batch of GitHub Issue titles and descriptions; the AI automatically classifies them by type and assigns priorities (P0-P3), outputting a structured table.

Generate secure sandbox configuration policies for AI agents executing multiple tools, including sandbox isolation and permission control for interfaces such as OpenAPI, MCP, and GraphQL.

Systematically compare mainstream Agent sandbox solutions (Container Sandbox vs. WebAssembly Isolation vs. V8 Isolate), providing selection recommendations and architecture design documentation.

Input your voice application requirements to generate a complete technical solution for multi-language TTS voice cloning, including model selection, deployment architecture, and code examples.

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.

Design a rigorous A/B test experiment plan for your LLM application, including sample size calculation, evaluation metric definition, statistical significance testing, and result visualization code.

Design a fully local document semantic search system from scratch, supporting multi-format documents, vector retrieval, and natural language Q&A.

Design the task workflow for an automation agent, including deployment, monitoring, fault recovery, and scaling strategies.

Describe requirements in natural language; AI automatically breaks them down into subtasks for multiple coding agents and generates a progress tracking dashboard.

Generate personalized learning paths and weekly plans based on the user's knowledge base, goals, and available time.

Analyze whether text or images contain AI-generated watermarks (such as Google SynthID) and explain the detection principles and academic discussions on evasion methods.

Describe your backend requirement in one sentence, and automatically generate complete TypeScript/Node.js backend API code, including routes, database Schema, validation, and tests.

Generate reusable YAML workflow definitions for AI coding projects, making the AI coding process repeatable and auditable.

Input a document or topic to automatically generate a professional PPT outline and content, including layout suggestions and speaker notes.

An AI analysis prompt for bioinformatics and drug R&D personnel, helping to understand protein structures, predict functional sites, and identify drug targets.

Input your use case scenario, and AI will help you filter, evaluate, and compare the most suitable skill packages from the vast Agent skill ecosystem, providing installation advice and integration plans.

Describe your voice AI product idea in one sentence, and automatically generate a complete PRD document including technology selection, architecture design, and interaction flows.

Help developers quickly evaluate and select suitable Text-to-Speech (TTS) technology solutions, comparing open-source and commercial options across quality, latency, multi-language support, and other dimensions.

Guide large language models to improve output quality during inference through adaptive computation strategies, leveraging test-time scaling techniques for better responses.

Backend architecture design optimized for AI coding agents, ensuring agents can efficiently develop, debug, and deploy complete applications.

Unified management of multiple AI coding agents, assigning tasks, tracking progress, and accumulating skills like a team manager.

Design browser automation strategies for AI Agents to bypass anti-scraping and bot detection, including fingerprint masking and behavior simulation.