All prompts

Organize multiple AI coding agents into a team, assign tasks, and track progress, managing AI agents just like a human team.

Input a competitor's name, and the AI performs a deep dive across five dimensions: product positioning, technical architecture, business model, user experience, and growth strategy, outputting a structured analysis report.

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

Simulate a hedge fund team composed of multiple AI Agents, with roles such as analysts, risk managers, and traders making investment decisions.

Comprehensively evaluate security isolation solutions for AI Agent code execution sandboxes, covering technology selections such as containers, microVMs, and WASM.

Compare mainstream Go-based LLM development frameworks (such as eino, langchaingo, etc.) and output technology selection recommendations and project architecture designs.

Design a session memory system for an AI coding assistant that automatically compresses historical sessions and injects key context into new ones, improving cross-session coding efficiency.

Quickly assess the technical value, community health, and potential adoption opportunities of this week's trending open-source projects based on GitHub Trending data.

Simulate a senior SRE engineer to systematically analyze the root causes of production incidents and generate a structured RCA report.

Deeply understand the principles of Speculative Decoding technology and design the optimal acceleration solution for LLM inference services.

Convert natural language scene descriptions into technical parameters and camera path planning suitable for 3D Gaussian Splatting rendering.

Design a management platform that organizes coding Agents into teams, enabling automatic task allocation, progress tracking, and skill sharing.

Generate detailed 3D scene specifications from natural language descriptions, compatible with mainstream 3D generation frameworks such as NeRF, Gaussian Splatting, and diffusion models.

Transform any topic, article, or idea into a Xiaohongshu-style image-text note copy, including title, body, tags, and cover suggestions.

Input MCP Server tool definitions to automatically generate natural language test cases covering normal, edge, and exception scenarios, helping to quickly verify the quality of agent tool calls.

Input a topic to automatically generate a 5-10 minute two-host podcast script, including intro, core discussion, audience interaction segment, and outro, suitable for direct audio generation via TTS tools.

Compare and evaluate the performance, quality, and deployment costs of different Speculative Decoding solutions, generating a selection recommendation report.

Generates a complete CLAUDE.md configuration file based on your project needs, integrating best practices for LLM coding pitfalls summarized by Andrej Karpathy, ensuring Claude Code outputs more stable and reliable code.

Input your hardware configuration and usage scenario, and the AI will generate a complete local large model deployment decision tree, recommending the most suitable model, quantization scheme, and inference framework.

Generates a context window optimization checklist for AI coding agents, reducing token consumption and improving code generation quality

Systematic comparative analysis of open-source AI clients generates technology selection decision reports

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

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.

Input any GitHub open source project information to automatically generate a 5-minute quick start guide for beginners

Transforms raw speech recognition text into a clearly structured, highly readable professional document, automatically adding punctuation, paragraphs, headings, and summaries

Converts original speech recognition text into clearly structured, highly readable professional documents, automatically adding punctuation, paragraphs, titles, and summaries

Directly convert a single-sentence natural language requirement into a complete MCP Server interface design document, including tool definitions, parameter schemas, error handling, and example calls.

Design a secure Python code execution sandbox solution for AI Agents, based on a minimal Python interpreter implemented in Rust to prevent malicious code escape.

Design breakpoint saving, state snapshot, and automatic recovery mechanisms for AI Agents executing multi-step long tasks to prevent progress loss due to task interruptions.

Input a topic to automatically generate a complete workflow prompt for short video production, including script, storyboard, voiceover copy, subtitles, and publishing plan.

Helps you systematically analyze and optimize the context window usage efficiency of AI coding agents (such as Claude Code, Cursor, Copilot), generating specific slimming plans and configuration suggestions.

A complete solution for building a local voice cloning studio based on open-source solutions (Voicebox/Kokoro/Chatterbox, etc.), including multi-TTS engine comparison evaluation, voice cloning quality assessment, and privacy compliance checks.

Paste your code change descriptions here, and the AI will help you generate professional English PR descriptions and commit messages that comply with open-source community standards.

Automatically detect personally identifiable information (PII) in text, code, and documents, generating masking plans and GDPR/Personal Information Protection Law compliance recommendations.

Evaluate the first-screen effect of your GitHub README using the 3-second rule, receive an attractiveness score, and get an automatic rewrite to help open-source projects boost Star conversion rates.

Generate optimal configuration plans for AI coding IDEs like Cursor, Windsurf, and Kiro, optimizing Agent workflows and context management strategies.

A complete workflow prompt for using AI to read papers, reproduce experiments, and train models, suitable for researchers and ML engineers.

Use AI to quickly identify the true file type to prevent malicious files from disguising themselves, suitable for security reviews and data pipeline preprocessing scenarios.

Design token consumption optimization strategies for coding agent tool invocation scenarios, reducing context waste by 60-90%.

A structured framework to quickly assess the technical value, community activity, and commercial potential of a GitHub open-source AI project, suitable for tech investors and developers for project screening.

Generate a comprehensive performance benchmark plan for LLM inference services (such as vLLM, SGLang, TensorRT-LLM), including test metrics, load models, and result analysis templates.

Paste a piece of code, and the AI will analyze its performance bottlenecks and provide specific optimization plans, including time complexity, memory usage, and concurrency optimization suggestions.

Designs secure code execution environments for AI agents, comparing container sandboxes and embedded interpreters (e.g., Monty) to generate optimal integration configurations.

Input an initial prompt to automatically analyze its weaknesses and generate 3 optimized variants, complete with improvement rationale and expected effect comparisons, to help rapidly iterate on prompt quality.

Input your project requirements and constraints to automatically generate a multi-dimensional technology selection comparison matrix, including evaluation dimensions such as performance, cost, ecosystem, and learning curve.

Guide LLMs in chunky translations of entire books or long documents, maintaining contextual coherence, terminology consistency, and formatting integrity

Using the OpenAI Agents SDK to design an intelligent customer service system collaborating with multiple agents, including routing, professional handling, and escalation mechanisms

Generate precise processing instructions for AI-driven green screen keying tools, covering edge blending, motion blur, and translucent area processing