Analyze PDF and Create MATLAB Code
TEXT${section}${pdfFile}— replace before using.Act as a PDF analysis and MATLAB coding assistant. You are tasked with analyzing a PDF document composed of various subsections. For each section, your task is to:
1. Provide a clear, simple, and complete explanation of the theory related to the section.
2. Develop MATLAB code that represents the section accurately, ensuring the code is not overly complex but is clear and comprehensive.
3. Explain the MATLAB code thoroughly, highlighting key components, their functions, and how they relate to the underlying theory.
4. Prepare a PowerPoint presentation summarizing the results and theory once all sections have been processed.
You will:
- Focus on one section at a time, ensuring thorough analysis and coding.
- Avoid skipping any details, as every part is important.
Variables:
- ${section} - Current section topic
- ${pdfFile} - PDF file to analyze
Rules:
- Ensure all explanations and code are clear and understandable.
- Maintain a logical flow from theory to code to explanation.
- Prepare a comprehensive PowerPoint presentation at the end.Overview
Analyze PDF and Create MATLAB Code is a free prompt on OpenRuna. A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor.
What this prompt does
"Analyze PDF and Create MATLAB Code" is designed to help you get reliable results from AI assistants for real prompt tasks. A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor. It is catalogued in OpenRuna's resource graph so you can discover related prompts, tools, agents, and datasets in one place. Copy the content directly into ChatGPT, Claude, Gemini, or Cursor — or use it as a system prompt / skill instruction where applicable.
Use cases
- Use "Analyze PDF and Create MATLAB Code" when you need a repeatable prompt for professional workflows without writing instructions from scratch each time.
- Adapt this prompt for team onboarding — paste into Claude or ChatGPT and iterate on the output with your project context.
- Combine with related tools and prompts in the same OpenRuna category to build a full stack for your use case.
- Reference during code review or planning sessions when you want consistent AI-assisted quality bars.
Example output
When you run this prompt, expect structured output similar to: Act as a PDF analysis and MATLAB coding assistant. You are tasked with analyzing a PDF document composed of various subsections. For each section, your task is to: 1. Provide a clear, simple, and complete explanation of the theory related to the section. 2. Develop MATLAB code that represents the section accurately, ensuring the code is not overly complex but is clear and comprehensive. 3. Explain the MATLAB code thoroughly, highlighting key components, their functions, and how they relate to the underlying theory. 4. Prepare a PowerPoint presentation summarizing the results and theory once a… Outputs vary by model and temperature; treat the first response as a draft and refine with follow-up prompts.
Tips by platform
Claude
In Claude, paste the full prompt as the first user message or add it to Project instructions. Ask Claude to confirm assumptions before executing. For long prompts, use Claude's artifact panel to iterate on structured output.
ChatGPT
In ChatGPT, start a new chat and paste this prompt verbatim. Enable GPT-4o or your preferred model for coding tasks. Use follow-ups like "apply this to [your context]" for best results.
Cursor
In Cursor, add key instructions from this prompt to .cursorrules or a SKILL.md file. Reference it in Agent mode with @ mentions. Keep the original title in comments so teammates can find it on OpenRuna.
Frequently asked questions
- What is "Analyze PDF and Create MATLAB Code"?
- It is a prompt listed on OpenRuna — A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor. You can copy and adapt it for ChatGPT, Claude, Cursor, or other AI tools.
- Is this prompt free to use?
- Most OpenRuna resources are open or CC0-licensed. Check the license on this page before commercial use. Premium collections are clearly marked.
- How do I get the best results?
- Replace any template variables, add your project context, and ask the model to confirm assumptions. Iterate in 2–3 follow-up turns rather than expecting a perfect first response.
- Can I use this with Claude and ChatGPT?
- Yes. The prompt is model-agnostic text. Tips on this page cover Claude, ChatGPT, and Cursor specifically.
- Where can I find related resources?
- Scroll to Related resources on this page or browse the category hub on OpenRuna to find connected prompts, tools, and agents in the same topic area.
Related resources
- Tool
v0 Prompts and Tools — ReadFile
Reads file contents intelligently - returns complete files when small, paginated chunks, or targeted chunks when large based on your query. **How it works:** • **Small files** (≤2000 lines) - Returns complete content • **Large files** (>2000 lines) - Uses AI to find and return relevant chunks based on query • **Binary files** - Returns images, handles blob content appropriately • Any lines longer than 2000 characters are truncated for readability • Start line and end line can be provided to rea
- Tool
v0 Prompts and Tools — LSRepo
Lists files and directories in the repository. Returns file paths sorted alphabetically with optional pattern-based filtering. Common use cases: • Explore repository structure and understand project layout • Find files in specific directories (e.g., 'src/', 'components/') • Locate configuration files, documentation, or specific file types • Get overview of available files before diving into specific areas Tips: • Use specific paths to narrow down results (max 200 entries returned) • Combine wi
- Tool
Traycer AI — grep_search
Fast text-based regex search that finds exact pattern matches within files or directories, utilizing the ripgrep command for efficient searching. Results will be formatted in the style of ripgrep and can be configured to include line numbers and content. To avoid overwhelming output, the results are capped at 50 matches. Use the include patterns to filter the search scope by file type or specific paths. This is best for finding exact text matches or regex patterns. More precise than codebase sea
- Tool
Trae — search_codebase
This tool is Trae's context engine. It: 1. Takes in a natural language description of the code you are looking for; 2. Uses a proprietary retrieval/embedding model suite that produces the highest-quality recall of relevant code snippets from across the codebase; 3. Maintains a real-time index of the codebase, so the results are always up-to-date and reflects the current state of the codebase; 4. Can retrieve across different programming languages; 5. Only reflects the current state of the codeba
- Tool
Trae — todo_write
Use this tool to create and manage a structured task list for your current coding session. This helps you track progress, organize complex tasks, and demonstrate thoroughness to the user. It also helps the user understand the progress of the task and overall progress of their requests.
- Tool
Same.dev — task_agent
Launches a highly capable task agent in the USER's workspace. Usage notes: 1. When the agent is done, it will return a report of its actions. This report is also visible to USER, so you don't have to repeat any overlapping information. 2. Each agent invocation is stateless and doesn't have access to your chat history with USER. You will not be able to send additional messages to the agent, nor will the agent be able to communicate with you outside of its final report. Therefore, your prompt shou
- Tool
Replit — shell_command_application_feedback_tool
This tool allows you to execute interactive shell commands and ask questions about the output or behavior of CLI applications or interactive Python programs. ## Rules of usage: 1. Provide clear, concise interactive commands to execute and specific questions about the results or interaction. 2. Ask one question at a time about the interactive behavior or output. 3. Focus on interactive functionality, user input/output, and real-time behavior. 4. Specify the exact command to run, including any ne
- Tool
Replit — str_replace_editor
Custom editing tool for viewing, creating and editing files * State is persistent across command calls and discussions with the user * If `path` is a file, `view` displays the result of applying `cat -n`. If `path` is a directory, `view` lists non-hidden files and directories up to 2 levels deep * The `create` command cannot be used if the specified `path` already exists as a file * If a `command` generates a long output, it will be truncated and marked with `<response clipped>` * The `undo_edi