Disassembling the script of a viral video
Featured by
nene@YouMind.AI
Why we love this skill
Want to create viral YouTube videos? This skill uses an industrialized process of "collecting, deconstructing, and reconstructing" to deeply analyze the topics, structure, and expression of top-performing YouTube videos, helping you accurately benchmark against successful examples. It provides you with a detailed "Style Analysis and Writing Suggestions" and ultimately generates a highly authentic English script, ensuring your content has the potential to become a hit from the very beginning.
Instructions
#### describe
We reject blind creation. Through an industrialized process of "collecting, dismantling, and reconstructing," we precisely target top-performing videos on YouTube. We deeply analyze their topic selection, narrative structure, and authentic expression, first generating strategic suggestions, and then producing a highly "original" English script to ensure that the content has the potential to become a hit from the outset.
#### Core Task
For your video's theme (e.g., How To Get Your Attention Back), search and filter for the three most popular videos on YouTube. The goal is to extract all subtitle scripts, analyze their style, content structure, and logical progression, produce a style analysis and writing suggestions document, and ultimately write a native English-style video script based on this document.
Before starting, confirm the video theme with the user.
#### Execution Steps
**Step 1: Mining & Extraction of Trending Topics**
- **Objective:** Identify high-traffic areas and acquire raw data.
- **Action**:
- **Smart Search**: Search for trending videos on YouTube related to your topic (sorted by views/interaction rate).
- **Filter Lock**: Select 3 of the most representative viral videos (excluding irrelevant or low-quality content).
- **Script Extraction**: Extract all the complete subtitles (Transcript/Captions) from these videos and save them as analysis material.
**Step 2: In-depth analysis and style analysis**
**Objective:** To crack the "traffic code" behind viral hits.
- **Action**:
- **Expression Analysis**: Analyze competitors' language style (humorous, serious, fast-paced), native expressions, and rhetorical devices.
- **Structural Deconstruction**: Analyzing the narrative rhythm of the video (Hook setup, introduction, development, climax, and logical flow).
- **Techniques of Expression**: Identify its unique camera language or interactive methods.
**Step 3: Strategy Output**
- **Goal:** Establish a "winning strategy" before you even begin writing.
- **Output**:
- **Style Analysis and Writing Suggestions**: A detailed analytical document. It includes: a summary of the success factors of competitors, suggestions for narrative structures suitable for this topic, and suggestions for tone and word choice to emulate in writing. If citations are used, please provide the original video link and corresponding timestamp.
Please have the user confirm the output after completion. Only proceed with the final script creation after user confirmation.
**Step 4: Scripting**
- **Goal**: To create by standing on the shoulders of giants.
- **Action**:
- **Strategy-based writing:** Strictly follow the recommendations in Step 3 when creating your work.
- **Native Polishing**: Ensure the script language is highly authentic (Native Speaker Level), avoid "Chinglish," and incorporate the best expression techniques.
- **Final Output**: Generate a complete YouTube shooting script that includes "screen suggestions + voice-over".
description
Think like a producer of viral videos. By collecting, deconstructing, and reconstructing data, deeply analyze the logic behind YouTube's viral hits, generate native-feeling English scripts, and ensure your videos inherently possess the genes for viral success.
Related Skills
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Native-speaker editor-level paper polishing, three-track customized approach directly meeting top journal review standards. Three independent engines precisely align with your target submission model: A. Journal Benchmarking: Locking onto the style guidelines of top journals like Nature and IEEE, utilizing the journal's unique sentence structure preferences (Nature prefers active voice + interdisciplinary readability, IEEE prefers passive voice + high-density technical vocabulary), ensuring your draft directly matches the target journal's standard model. B. Model Article Replication: In-depth analysis of the sentence structure, conjunction preferences, and writing rhythm of your favorite benchmark articles, making your manuscript infinitely close to the top journal's model in language style. C. Pure Refinement: Activating universal top journal standards, performing four-dimensional foundation polishing (grammar correction, word restructuring, intonation unification, extreme clarity), significantly enhancing academic tension and readability. Surgical-level block-by-block refinement, every change is visible and logically enforced. Forced block processing (500-800 words each time) breaks through the AI attention window limitation, polishing sentence by sentence without roughness. Dual-core adversarial engine (Core A language restructuring + ... (Core B Quality Control) Transparent annotation throughout, outputting 3-5 editor-in-chief level revision notes each time, explaining "why this revision aligns with top journal preferences." Absolutely faithful semantics: only changes the expression, not the core content; never tamper with data or invent new viewpoints for you. After opening, directly select the polishing mode (A Journal Benchmark/B Sample Replication/C Pure Refinement), then paste your paper in paragraphs (500-800 words per paragraph is recommended, such as the first half of the introduction). The system will refine each paragraph with editor-in-chief annotations. After confirming there are no errors, proceed to the next paragraph. Fully interactive guidance, no complex configuration required. The accompanying tool, the official polishing module of the "Academic Paper Full-Process Writing System v3.1 (Wuyuan × AFP)," forms a complete closed loop from topic selection to drafting to final polishing. Applicable scenarios: Papers requested by reviewers to be "native-language polished," papers aiming for top journals like Nature/Science, and master's/doctoral students and young scholars wanting to learn the writing style of top journals. Key differences: Several times cheaper than hiring a polishing company, several times more accurate than AI-powered one-shot revisions, and several times more efficient than blind self-editing.
Academic Paper Writing System v3.0 (Wuyuan×AFP)
This academic paper writing system, integrating the Five-Source Model and AFP framework, provides a one-stop solution for the entire academic writing process, from initial observation to completion. ✅ Seven core modules: Topic Selection & Introduction → Literature Review → Research Methods → Discussion → Conclusion → Abstract & Keywords (new in v3.1) → Full Text Integration. Each stage is constrained by the Five-Source Model (structure + materials + style + integration + calibration). ✅ Stage-based diagnosis: The system automatically identifies your current writing stage (starting from scratch/already having a topic/already having a review, etc.) and jumps directly to the corresponding module, eliminating the need to start from the beginning. ✅ Anti-illusion firewall: It mandates the submission of real literature/data, with the B-core having veto power to reject any "illusionary content" without supporting materials, ensuring academic rigor. ✅ Interdisciplinary adaptation: It automatically identifies quantitative, qualitative, and speculative research paradigms and switches to corresponding writing strategies (e.g., quantitative analysis emphasizes "variable conflicts," while qualitative analysis emphasizes "failure of contextual explanatory power"), adapting to all disciplines from humanities and social sciences to STEM fields. After opening, simply tell the system "Which stage am I at?" + "My subject area" to start. The system will guide you through the process of submitting materials (literature/data/research ideas). Each completed module automatically generates usable chapter content, and finally, all are integrated into a complete paper with a single click. User feedback: 40% increase in C-level journal/SCI submission acceptance rate. v3.1 Major upgrade: Added Phase 6 abstract and keyword generation module, achieving a true closed-loop process from "topic selection → manuscript completion → abstract".
Thesis Topic Selection and Introduction Writing System v3.0 (Five-Source Model × AFP)
From vague ideas to complete topic selection, and then to high-quality introductions, the entire process is designed to prevent illusions. ✅ Stage-based diagnostic positioning—Whether you are in the stage of vague observation, research unit refinement, theoretical matching, or introduction writing, the system automatically identifies and starts from the corresponding stage, without having to start from scratch. ✅ Four-core collaborative quality control—A core generates content, B core reviews and rejects (with veto power!), C core evaluates innovativeness, and D core monitors the entire process, ensuring that every output conforms to academic norms. ✅ Anti-illusion firewall—Forced feeding of real literature to generate introductions, rejecting AI-fabricated citations, and all references must be traceable. ✅ Automatic switching of interdisciplinary methodologies—After recognizing your professional background, the system automatically calls the corresponding methodology (humanities and social sciences: Q-method, fantasy topic analysis; science, engineering, agriculture, and medicine: machine learning, multi-omics analysis), without requiring you to understand the details of the methodology. After opening the system and answering 3 questions (professional background/current stage/target journal), the system automatically determines which step to start from. Prepare the prefaces (including references) of 3-5 papers on the same topic as reference materials, and the system will generate a draft introduction that conforms to journal specifications based on real literature. The entire process involves collaboration among four core experts, with a second-tier expert reviewing at key stages. Submissions that fail to meet standards are immediately rejected and require revision. The program covers all disciplines in the humanities, social sciences, science, engineering, agriculture, and medicine, and is compatible with submissions to journals at all levels, including CSSCI, SCI, and Peking University core journals.
Disassembling the script of a viral video
Featured by
nene@YouMind.AI
Why we love this skill
Want to create viral YouTube videos? This skill uses an industrialized process of "collecting, deconstructing, and reconstructing" to deeply analyze the topics, structure, and expression of top-performing YouTube videos, helping you accurately benchmark against successful examples. It provides you with a detailed "Style Analysis and Writing Suggestions" and ultimately generates a highly authentic English script, ensuring your content has the potential to become a hit from the very beginning.
Instructions
#### describe
We reject blind creation. Through an industrialized process of "collecting, dismantling, and reconstructing," we precisely target top-performing videos on YouTube. We deeply analyze their topic selection, narrative structure, and authentic expression, first generating strategic suggestions, and then producing a highly "original" English script to ensure that the content has the potential to become a hit from the outset.
#### Core Task
For your video's theme (e.g., How To Get Your Attention Back), search and filter for the three most popular videos on YouTube. The goal is to extract all subtitle scripts, analyze their style, content structure, and logical progression, produce a style analysis and writing suggestions document, and ultimately write a native English-style video script based on this document.
Before starting, confirm the video theme with the user.
#### Execution Steps
**Step 1: Mining & Extraction of Trending Topics**
- **Objective:** Identify high-traffic areas and acquire raw data.
- **Action**:
- **Smart Search**: Search for trending videos on YouTube related to your topic (sorted by views/interaction rate).
- **Filter Lock**: Select 3 of the most representative viral videos (excluding irrelevant or low-quality content).
- **Script Extraction**: Extract all the complete subtitles (Transcript/Captions) from these videos and save them as analysis material.
**Step 2: In-depth analysis and style analysis**
**Objective:** To crack the "traffic code" behind viral hits.
- **Action**:
- **Expression Analysis**: Analyze competitors' language style (humorous, serious, fast-paced), native expressions, and rhetorical devices.
- **Structural Deconstruction**: Analyzing the narrative rhythm of the video (Hook setup, introduction, development, climax, and logical flow).
- **Techniques of Expression**: Identify its unique camera language or interactive methods.
**Step 3: Strategy Output**
- **Goal:** Establish a "winning strategy" before you even begin writing.
- **Output**:
- **Style Analysis and Writing Suggestions**: A detailed analytical document. It includes: a summary of the success factors of competitors, suggestions for narrative structures suitable for this topic, and suggestions for tone and word choice to emulate in writing. If citations are used, please provide the original video link and corresponding timestamp.
Please have the user confirm the output after completion. Only proceed with the final script creation after user confirmation.
**Step 4: Scripting**
- **Goal**: To create by standing on the shoulders of giants.
- **Action**:
- **Strategy-based writing:** Strictly follow the recommendations in Step 3 when creating your work.
- **Native Polishing**: Ensure the script language is highly authentic (Native Speaker Level), avoid "Chinglish," and incorporate the best expression techniques.
- **Final Output**: Generate a complete YouTube shooting script that includes "screen suggestions + voice-over".
description
Think like a producer of viral videos. By collecting, deconstructing, and reconstructing data, deeply analyze the logic behind YouTube's viral hits, generate native-feeling English scripts, and ensure your videos inherently possess the genes for viral success.
Related Skills
View allAcademic Paper Three-Track Polishing System v2.0
Native-speaker editor-level paper polishing, three-track customized approach directly meeting top journal review standards. Three independent engines precisely align with your target submission model: A. Journal Benchmarking: Locking onto the style guidelines of top journals like Nature and IEEE, utilizing the journal's unique sentence structure preferences (Nature prefers active voice + interdisciplinary readability, IEEE prefers passive voice + high-density technical vocabulary), ensuring your draft directly matches the target journal's standard model. B. Model Article Replication: In-depth analysis of the sentence structure, conjunction preferences, and writing rhythm of your favorite benchmark articles, making your manuscript infinitely close to the top journal's model in language style. C. Pure Refinement: Activating universal top journal standards, performing four-dimensional foundation polishing (grammar correction, word restructuring, intonation unification, extreme clarity), significantly enhancing academic tension and readability. Surgical-level block-by-block refinement, every change is visible and logically enforced. Forced block processing (500-800 words each time) breaks through the AI attention window limitation, polishing sentence by sentence without roughness. Dual-core adversarial engine (Core A language restructuring + ... (Core B Quality Control) Transparent annotation throughout, outputting 3-5 editor-in-chief level revision notes each time, explaining "why this revision aligns with top journal preferences." Absolutely faithful semantics: only changes the expression, not the core content; never tamper with data or invent new viewpoints for you. After opening, directly select the polishing mode (A Journal Benchmark/B Sample Replication/C Pure Refinement), then paste your paper in paragraphs (500-800 words per paragraph is recommended, such as the first half of the introduction). The system will refine each paragraph with editor-in-chief annotations. After confirming there are no errors, proceed to the next paragraph. Fully interactive guidance, no complex configuration required. The accompanying tool, the official polishing module of the "Academic Paper Full-Process Writing System v3.1 (Wuyuan × AFP)," forms a complete closed loop from topic selection to drafting to final polishing. Applicable scenarios: Papers requested by reviewers to be "native-language polished," papers aiming for top journals like Nature/Science, and master's/doctoral students and young scholars wanting to learn the writing style of top journals. Key differences: Several times cheaper than hiring a polishing company, several times more accurate than AI-powered one-shot revisions, and several times more efficient than blind self-editing.
Academic Paper Writing System v3.0 (Wuyuan×AFP)
This academic paper writing system, integrating the Five-Source Model and AFP framework, provides a one-stop solution for the entire academic writing process, from initial observation to completion. ✅ Seven core modules: Topic Selection & Introduction → Literature Review → Research Methods → Discussion → Conclusion → Abstract & Keywords (new in v3.1) → Full Text Integration. Each stage is constrained by the Five-Source Model (structure + materials + style + integration + calibration). ✅ Stage-based diagnosis: The system automatically identifies your current writing stage (starting from scratch/already having a topic/already having a review, etc.) and jumps directly to the corresponding module, eliminating the need to start from the beginning. ✅ Anti-illusion firewall: It mandates the submission of real literature/data, with the B-core having veto power to reject any "illusionary content" without supporting materials, ensuring academic rigor. ✅ Interdisciplinary adaptation: It automatically identifies quantitative, qualitative, and speculative research paradigms and switches to corresponding writing strategies (e.g., quantitative analysis emphasizes "variable conflicts," while qualitative analysis emphasizes "failure of contextual explanatory power"), adapting to all disciplines from humanities and social sciences to STEM fields. After opening, simply tell the system "Which stage am I at?" + "My subject area" to start. The system will guide you through the process of submitting materials (literature/data/research ideas). Each completed module automatically generates usable chapter content, and finally, all are integrated into a complete paper with a single click. User feedback: 40% increase in C-level journal/SCI submission acceptance rate. v3.1 Major upgrade: Added Phase 6 abstract and keyword generation module, achieving a true closed-loop process from "topic selection → manuscript completion → abstract".
Thesis Topic Selection and Introduction Writing System v3.0 (Five-Source Model × AFP)
From vague ideas to complete topic selection, and then to high-quality introductions, the entire process is designed to prevent illusions. ✅ Stage-based diagnostic positioning—Whether you are in the stage of vague observation, research unit refinement, theoretical matching, or introduction writing, the system automatically identifies and starts from the corresponding stage, without having to start from scratch. ✅ Four-core collaborative quality control—A core generates content, B core reviews and rejects (with veto power!), C core evaluates innovativeness, and D core monitors the entire process, ensuring that every output conforms to academic norms. ✅ Anti-illusion firewall—Forced feeding of real literature to generate introductions, rejecting AI-fabricated citations, and all references must be traceable. ✅ Automatic switching of interdisciplinary methodologies—After recognizing your professional background, the system automatically calls the corresponding methodology (humanities and social sciences: Q-method, fantasy topic analysis; science, engineering, agriculture, and medicine: machine learning, multi-omics analysis), without requiring you to understand the details of the methodology. After opening the system and answering 3 questions (professional background/current stage/target journal), the system automatically determines which step to start from. Prepare the prefaces (including references) of 3-5 papers on the same topic as reference materials, and the system will generate a draft introduction that conforms to journal specifications based on real literature. The entire process involves collaboration among four core experts, with a second-tier expert reviewing at key stages. Submissions that fail to meet standards are immediately rejected and require revision. The program covers all disciplines in the humanities, social sciences, science, engineering, agriculture, and medicine, and is compatible with submissions to journals at all levels, including CSSCI, SCI, and Peking University core journals.
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