All prompts

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

Transform any topic or article into structured knowledge cards containing core concepts, key data, analogy explanations, and memory anchors, suitable for learning and content creation.

One-click generation of issue templates, label classification systems, and contributor guides for open source projects

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

A Manus-style Markdown persistent planning approach helps AI Agents break down complex tasks step by step and track execution progress

Imitate Manus's persistent Markdown planning workflow, allowing AI coding assistants to maintain clear plan files during complex tasks, automatically tracking progress and marking completion status to avoid context loss.

Generates automated fault diagnosis and self-healing runbooks for AI Agent workloads in K8s clusters, covering common scenarios such as Pod anomalies, OOM, and GPU scheduling failures.

Enter a theme and automatically generate Douyin and Xiaohongshu short video scripts with storyboards, dialogue, and images

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

Describe your business scenario in natural language to automatically generate multi-model routing strategy configurations, achieving the optimal balance between cost and quality.

Automatically generates multi-model routing rules based on task type, latency requirements, and budget, saving up to 70% in API costs.

Design a comprehensive multi-platform public opinion monitoring system that aggregates hotspots from Weibo, Xiaohongshu, Twitter, etc., with AI-driven analysis and briefing push notifications.

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.

Intelligently reduce noise, deduplicate, and correlate massive operations alerts to generate root cause analysis reports and remediation suggestions.

Addresses the context window explosion problem for AI coding agents (such as Claude Code, Cursor, Codex) by generating sandboxing strategies for tool outputs and token recycling plans to achieve 90%+ context savings.

Input ML papers, extract core methods and innovations, and generate experimental reproduction plans

One piece of copy, rewritten across multiple platforms, adapted for Xiaohongshu, Twitter, LinkedIn, and official accounts

System prompt templates designed for the RAG system allow AI to accurately answer questions based on retrieved document fragments, reducing hallucinations and supporting multiple document source citations

Paste any open-source project's README to receive an attractiveness score and a professional-grade rewritten version, significantly boosting Star conversion rates.

Analyze token consumption patterns in your AI Agent workflow, generate intelligent routing rules, and automatically assign simple tasks to smaller models and complex tasks to larger models, reducing API costs by 60-90%.

Design an AI Agent workflow that can automatically read ML papers, extract key implementation details, and generate runnable reproduction code and experiment configurations.

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

Input the project tech stack and dependency list to generate a complete dependency security audit report, including vulnerability severity, impact scope, and remediation suggestions.

For Mixture of Experts (MoE) architecture models, generates detailed inference deployment plans, including hardware selection, communication optimization, and cost estimation.

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.

Automatically recommends the optimal AI model routing strategy based on task type, complexity, and cost budget, helping you save money while maintaining efficiency.

Generate comprehensive security scan reports and hardening recommendation checklists for Docker images of any open-source project, covering best practices such as CVE vulnerabilities, minimal base images, non-root users, and multi-stage builds.

Design a complete MLOps experiment tracking and model version management solution based on team size and tech stack, covering tool selection and configuration for MLflow, W&B, DVC, etc.

Design an end-to-end AI Agent workflow that automatically reads ML papers, extracts experimental settings, reproduces the training process, and generates evaluation reports.

Automatically scans the code warehouse for security risks, architectural issues, and technical debt, generating a prioritized repair roadmap and health score

Simulates business negotiation scenarios between two AI Agents to help understand automated business agreements and multi-party game strategies

Design a comprehensive observability solution for the AI Agent's multi-step task execution chain, including trace, span, log association, and performance metric collection

Generate standardized code review workflows for multi-agent coding teams, including role division, review rules, and quality access controls

Describe your business scenario, and AI will help you design a complete multi-agent workflow solution, including role division, tool invocation, and collaboration models

Design a secure execution scheme for Python code generated by AI Agents, including sandbox isolation, permission control, timeout mechanisms, and output validation, suitable for production scenarios requiring LLM code execution.

Design a workspace architecture supporting collaboration among multiple AI Agents, including task assignment, skill evolution, progress tracking, and conflict resolution mechanisms, suitable for team-level AI coding and operations scenarios.

Input the product name, core features, and target users to generate launch copy for multiple platforms including Twitter/X, Product Hunt, Xiaohongshu, and LinkedIn in one go.

Leave your work logbook to AI, which automatically distills it into a clear, highlighted-point weekly report suitable for your boss or team.

Designs multi-agent collaboration workflows based on Kanban views, supporting requirement breakdown, task assignment, progress tracking, and automated orchestration, suitable for Spec-driven AI development teams.

Input Rust CLI project information to automatically generate a complete Homebrew Formula file, GitHub Actions release workflow, and tap repository structure, supporting cross-platform compilation.

Enter a topic and batch generate hooked copy for platforms like Douyin, Xiaohongshu, and YouTube Shorts, including tips for optimizing completion rates.

Design a fully self-hosted, privacy-first personal knowledge management system integrating local AI for semantic search, automatic tagging, knowledge graphs, and intelligent summarization, with all data remaining local.

Design a locally deployed multi-model collaborative inference solution supporting models such as large and small model cascading, routing distribution, and result fusion, maximizing inference efficiency and quality balance

Design a high-performance inference service deployment solution for local GGUF/SafeTensors models with zero Python dependencies, including model discovery, hot-swapping, and OpenAI-compatible API configuration.

Automated PR lifecycle management from idea to merge, including task assignment, code generation, automatic review, conflict resolution, and merge strategies.

Input a theme and mood to generate lyrics and style tag descriptions suitable for AI music generation tools (such as ACE-Step, Suno).

Analyze and recommend the most suitable memory system architecture (vector database, knowledge graph, file system, etc.) based on your Agent application scenario.