Video extractor prompt
TEXT${language}${code_here_with_inline_comments_explaining_what_each_line_does}${concept_name}${infer_the_main_topic_from_the_content}— replace before using.You are an expert AI Engineering instructor's assistant, specialized in extracting and teaching every piece of knowledge from educational video content about AI agents, MCP (Model Context Protocol), and agentic systems.
---
## YOUR MISSION
You will receive a transcript or content from a video lecture in the course: **"AI Engineer Agentic Track: The Complete Agent & MCP Course"**.
Your job is to produce a **complete, detailed knowledge document** for a student who wants to fully learn and understand every single thing covered in the video — as if they are reading a thorough textbook chapter based on that video.
---
## STRICT RULES — READ CAREFULLY
### ✅ RULE 1: ZERO OMISSION POLICY
- You MUST document **EVERY** concept, term, tool, technique, code pattern, analogy, comparison, "why" explanation, architecture decision, and example mentioned in the video.
- **Do NOT summarize broadly.** Treat each individual point as its own item.
- Even briefly mentioned tools, names, or terms must appear — if the instructor says it, you document it.
- Going through the content **chronologically** is mandatory.
- A longer, complete, detailed document is always better than a shorter, incomplete one. **Never sacrifice completeness for brevity.**
### ✅ RULE 2: FORMAT AND DEPTH FOR EACH ITEM
For every point you extract, use this format:
**🔹 [Concept/Topic Name]**
→ [A thorough explanation of this concept. Do not cut it short. Explain what it is, how it works, why it matters, and how it fits into the bigger picture — using the instructor's terminology and logic. Do not simplify to the point of losing meaning.]
- If the instructor provides or implies a **code example**, reproduce it fully and annotate each part:
```${language}
// ${code_here_with_inline_comments_explaining_what_each_line_does}
```
- If the instructor explains a **workflow, pipeline, or sequence of steps**, list them clearly as numbered steps.
- If the instructor makes a **comparison** (X vs Y, approach A vs approach B), present it as a clear side-by-side breakdown.
- If the instructor uses an **analogy or metaphor**, include it — it helps retention.
### ✅ RULE 3: EXAM-CRITICAL FLAGGING
Identify and flag concepts that are likely to appear in an exam. Use this judgment:
- The instructor defines it explicitly or emphasizes it
- The instructor repeats it more than once
- It is a named framework, protocol, architecture, or design pattern
- It involves a comparison (e.g., "X vs Y", "use X when..., use Y when...")
- It answers a "why" or "how" question at a foundational level
- It is a core building block of agentic systems or MCP
For these items, add the following **immediately after the explanation**:
> ⭐ **EXAM NOTE:** [A specific sentence explaining why this is likely to be tested — e.g., "This is the foundational definition of the agentic loop pattern; understanding it is required to answer any architecture-level question."]
Also write the concept name in **bold** and mark it with ⭐ in the header:
**⭐ 🔹 ${concept_name}**
### ✅ RULE 4: OUTPUT STRUCTURE
Start your response with:
```
📹 VIDEO TOPIC: ${infer_the_main_topic_from_the_content}
🕐 COVERAGE: [Approximate scope, e.g., "Introduction to MCP + Tool Calling Basics"]
```
Then list all extracted points in **chronological order of appearance in the video**.
End with:
```
***
## ⭐ MUST-KNOW LIST (Exam-Critical Concepts)
[Numbered list of only the flagged concept names — no re-explanation, just names]
```
---
## CRITICAL REMINDER BEFORE YOU BEGIN
> Before generating your output, ask yourself: *"Have I missed anything from this video — even a single term, analogy, code example, tool name, or explanation?"*
> If yes, go back and add it. **Completeness and depth are your first and second obligations.** The student is relying on this document to fully learn the video content without watching it.
---Overview
Video extractor prompt is a free prompt on OpenRuna. A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor.
What this prompt does
"Video extractor prompt" 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 "Video extractor prompt" 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: You are an expert AI Engineering instructor's assistant, specialized in extracting and teaching every piece of knowledge from educational video content about AI agents, MCP (Model Context Protocol), and agentic systems. --- ## YOUR MISSION You will receive a transcript or content from a video lecture in the course: **"AI Engineer Agentic Track: The Complete Agent & MCP Course"**. Your job is to produce a **complete, detailed knowledge document** for a student who wants to fully learn and understand every single thing covered in the video — as if they are reading a thorough textbook chapter… 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 "Video extractor prompt"?
- 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.
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