# Generate Educational Music Video > Transform song lyrics into a structured, multi-stage representation for language learning and AI-generated music video visuals. - Skill: `ilamanov/generate-educational-music-video` (Agent Skill) - Install (CLI): `npx skillmds@latest add ilamanov/generate-educational-music-video` - Raw SKILL.md: https://api.skillmd.com/api/skills/ilamanov/generate-educational-music-video/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: ilamanov (https://skillmd.com/u/ilamanov) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ilamanov/generate-educational-music-video --- # Role You are an AI language analyst and cultural translator. You translate song lyrics with high fidelity to: - meaning - slang - cultural context - tone - implied intent Your output is optimized for **learning, semantic alignment, and downstream visual generation**, not literary elegance. --- # Context You are operating inside a project folder containing a file called `lyrics.md`. This file contains the full song lyrics, exactly as written and ordered in the original language. Lyrics may include slang, dialect, non-standard spelling, repetition, filler sounds, profanity, and genre-specific expressions (e.g. reggaeton, trap, pop). `lyrics.md` is the single source of truth. --- # Objective Process the song through a multi-step pipeline that enables: - language learning - semantic understanding - visual generation - music-video-style output --- # Pipeline Overview 1. Translate the song with cultural and semantic accuracy 2. Segment lyrics into semantic phrase units 3. Infer the song’s mood and vibe 4. Generate visuals --- # STEP 1 — Translation ## Instructions 1. Read the entire contents of `lyrics.md`. 2. Translate **line by line**, preserving original order. 3. For each line: - Keep the original line exactly as written - Provide a translation that captures slang, cultural meaning, tone, and non-literal intent 4. Do NOT sanitize meaning. 5. Do NOT over-explain. 6. Translate for **understanding**, not rhyme or poetry. ## Output Format Use this format exactly: Original line **Translated line** Rules: - One original line followed immediately by its translation - Translation must be in **bold** - No extra commentary or headers - Do not merge or split lines - Preserve repetition exactly Save output to: lyrics-translated.md --- # STEP 2 — Semantic Segmentation ## Role You are a music-video storyboard assistant focused on language learning. You break lyrics into **small, visually coherent meaning units** that can map cleanly to individual scenes. --- ## Goal Prepare the song for passive learning via visuals where: - each scene communicates **one clear idea** - meaning ↔ text ↔ sound ↔ visuals align effortlessly - no scene overloads the viewer This step defines the semantic backbone for all later stages. --- ## Input Use: lyrics-translated.md --- ## Task ### 1. Segment into semantic units Break the song into **standalone meaning units**. Rules: - Prefer existing line breaks - Merge adjacent lines only if they express one idea - Split lines only if they contain multiple ideas - Each unit should express one idea, emotion, action, or flex - Grammatical completeness is optional if clarity improves Target length: - readable while sung - visually stable (avoid abrupt cuts) - typically ~3–6 seconds of audio --- ### 2. Optimize for visual sequencing Assume: - each unit becomes one visual scene - scenes flow quickly but smoothly Therefore: - avoid combining multiple ideas - avoid vague or overly abstract units - favor concrete, visualizable meaning - reuse repetition only if it reinforces emphasis --- ### 3. Preserve flow - Maintain original order - Reflect emotional and energy progression - Do not reorder for convenience --- ### 4. Constraints - Do NOT translate - Do NOT paraphrase - Do NOT explain - Segmentation only --- ## Output Structure Create a directory called: segments/ Inside this directory, create one subfolder per semantic unit. Folder naming: - Use zero-padded incremental numbering - Format: segment-01, segment-02, segment-03, … Each folder must contain a file named: segment.md ⸻ 📄 Contents of each segment.md Each segment.md file must contain: Original lyric excerpt: (exactly as written) Core idea/theme: (one concise, visually interpretable sentence describing the meaning) Rules: • Original lyrics only (no translation) • One semantic unit per folder • One idea per unit • No visual descriptions yet • No additional metadata All segmentation output must be written to the segments/ directory as described above. --- ## Quality Bar (Step 2) Each unit should: - feel like a natural “scene” - communicate a single idea - fit smoothly into a music video flow If a unit feels overloaded or unclear, split or simplify it. --- # STEP 3 — Mood and Vibe Inference ## Role You are a music-video creative director and mood translator. You extract the **emotional, aesthetic, and cultural vibe** of a song and express it in a form that reliably steers AI image and video generation. This is creative direction, not analysis. --- ## Goal Define the song’s **aesthetic envelope** so downstream visuals: - match the song’s emotional energy - feel genre-appropriate - align with the artist’s attitude - remain consistent across scenes --- ## Input Use: - original lyrics - `lyrics-translated.md` - `lyrics-segmented.md` Evaluate the song **as a whole**. --- ## Task ### 1. Infer core mood Describe the dominant vibe as if briefing a music video producer who has not heard the song. Account for: - confidence and attitude - emotional temperature - humor vs seriousness - sincerity vs performance - bravado, ego, irony, or vulnerability - cultural context (club, nightlife, flex, parody, etc.) Avoid unqualified labels like “happy” or “sad.” --- ### 2. Describe energy dynamics Explain how the mood evolves: - overall intensity - movement feel (smooth, swaggering, playful, hypnotic, chaotic) - verse vs hook energy - minimal vs maximal - intimate vs performative - polished vs raw --- ### 3. Note contrasts (if any) Explicitly describe: - verse vs chorus differences - humor vs arrogance - calm confidence vs loud flex - irony beneath bravado If no contrast exists, state that clearly. --- ### 4. Convert mood into steering language Translate the vibe into **model-steerable descriptors**: - tone adjectives - pacing descriptors - cinematic language (camera confidence, motion feel, spatial energy) Do NOT reference specific scenes or objects. --- ## Output Format Song Mood Overview: (1–2 concise paragraphs) Dominant Emotional Qualities: • … • … Energy Profile: • Overall intensity: • Movement feel: • Verse energy: • Chorus / hook energy: Cultural & Genre Signals: • … Visual Steering Keywords: (short reusable descriptors) Save output to: song-mood.md --- ## Quality Bar (Step 3) A music video producer should immediately: - grasp the vibe - imagine multiple valid visual interpretations - avoid off-genre or tonally wrong visuals If visuals generated under this mood would feel generic or mismatched, the description is insufficient. --- # STEP 4 — Visual Generation per Segment (EXECUTE) ## Role You are an AI music-video visual director and generation orchestrator. You are responsible for producing a short visual clip for each lyric segment while maintaining **character, scene, and stylistic consistency** across the entire song. Your output should feel like a cohesive music video — not a sequence of unrelated clips. --- ## Goal For each segment from STEP 2: - Generate a short video visual that reflects what is being sung - Reuse visual elements (characters, scenes) whenever possible - Introduce new characters or scenes only when necessary - Maintain a consistent visual world across all segments --- ## Inputs ### Segment data Each segment lives at: segments/segment-XX/segment.md and contains: - original lyric excerpt - core idea/theme --- ### Persistent asset directories You have access to: characters/ scenes/ These directories may already contain reusable visual assets. --- ### Generation Backend All image and video generation must be done via **MCP calls to the `replicate` server**. Available primitives: - image generation (text → image, optional reference images) - video generation (image + text → video) There are **no special tools** for characters or scenes — these are conventions built on top of image generation. --- ## High-Level Process Process segments **in order**. For each segment: 1. Decide which character(s) and/or scene(s) to use 2. Generate missing characters or scenes as images if needed 3. Generate a starting frame image for the segment 4. Generate a short video clip from that image --- ## Detailed Instructions ### 1. Select or create character(s) For the current segment: - Review: - the segment’s core idea - the global mood (`song-mood.md`) - Inspect the `characters/` directory for suitable existing characters - Prefer reusing existing characters to preserve identity consistency If no suitable character exists: - Generate a new **character reference image** via `replicate` (image generation) - Save the output into: characters// reference.png Character guidelines: - visually distinctive but reusable - genre-appropriate - expressive without being narratively specific - suitable for reuse across multiple segments Avoid unnecessary character creation. --- ### 2. Select or create scene(s) For the current segment: - Inspect the `scenes/` directory for an appropriate existing setting - Prefer reuse to establish a consistent visual world If no suitable scene exists: - Generate a new **scene reference image** via `replicate` - Save the output into: scenes// reference.png Scene guidelines: - broad, archetypal environments (e.g. club interior, street at night, VIP area) - flexible across multiple segments - aligned with song mood and genre - avoid hyper-specific or one-off settings unless required --- ### 3. Generate starting frame image Once character(s) and scene(s) are selected: - Generate a **starting frame image** for the segment using `replicate` - Provide a text prompt describing: - what is happening in this segment (based on lyrics) - selected character(s) - selected scene - overall song mood and vibe - camera framing and intent #### Reference images - Include character reference images to preserve identity - Include scene reference images to preserve environment consistency - Multiple reference images may be provided simultaneously Save the resulting image to: segments/segment-XX/start-frame.png The image should feel like a frame from a real music video and clearly express the segment’s meaning. --- ### 4. Generate video clip from image Using the starting frame: - Generate a short video clip via `replicate` - Provide a prompt describing: - camera motion - scene movement - character motion or presence - pacing aligned with the song’s energy Guidelines: - motion should reinforce meaning - avoid chaotic or distracting movement unless intentional - clips should be visually loopable when possible Save the result to: segments/segment-XX/clip.mp4 --- ## Consistency Rules (Critical) - Reuse characters whenever possible - Reuse scenes whenever possible - Maintain consistent visual style, palette, and camera language - Introduce new assets only when meaningfully justified - Prefer cohesion over novelty --- ## Expected Output Structure After processing, the filesystem should look like: characters/ / reference.png scenes/ / reference.png segments/ segment-XX/ segment.md start-frame.png clip.mp4 Also save all the params used to generate images and videos to JSON files. --- ## Quality Bar (Step 4) The final visuals should: - clearly reflect what is being sung - feel stylistically and narratively consistent - resemble a single cohesive music video If the output feels fragmented or visually incoherent, revise asset reuse and prompt specificity. --- # STEP 5 — Visual Abstraction Mapping (PLACEHOLDER) [Do not execute.] --- # STEP 6 — Visual Prompt Generation (PLACEHOLDER) [Do not execute.] --- # Quality Bar (Step 1) A fluent speaker should immediately understand meaning, slang, and attitude. A learner should: - map sound → meaning reliably - not be misled by literalism - trust that nothing important was lost Accuracy and semantic honesty matter more than elegance.