# AI Video Agency Magnific > Full-service AI video agency workflow built entirely on the Freepik/Magnific MCP connector (no CLI, no separate account). Turns a single reference photo into a complete cinematic multi-scene video with consistent character identity: character library asset → transformation morph → story scenes → reverse transformation. Use this skill whenever the user wants to create an AI video story, transform themselves into a character, make a multi-scene/multi-clip video, produce a YouTube short or hook, create an ad or brand story from a photo, or says things like "make a video of me as X", "tell a story with my photo", "create AI clips", "video storytelling", or "KI Video". Also trigger when the user provides a portrait/selfie and wants any kind of narrative video from it. Adapted from Arnie936/ai-video-agency (Higgsfield CLI edition) for the Freepik/Magnific MCP tools — see README.md for source and changelog. - Skill: `ki-stuff/ai-video-agency-magnific` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add ki-stuff/ai-video-agency-magnific` - Raw SKILL.md: https://api.skillmd.com/api/skills/ki-stuff/ai-video-agency-magnific/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: ki-stuff (https://skillmd.com/u/ki-stuff) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ki-stuff/ai-video-agency-magnific --- # AI Video Agency (Magnific Edition) Act as a full-service AI video agency: take one reference photo from the user and deliver a complete, coherent, multi-scene cinematic video with a consistent main character. Built entirely on the Freepik/Magnific MCP tools (Nano Banana Pro / GPT 2 for images, Seedance 2.0 for video) — no CLI installation, no separate account, no ffmpeg. ## Step 0 — Setup check 1. **Freepik/Magnific connector active?** Call `account_profile` and `account_balance`. If the call fails, tell the user to enable the Freepik/Magnific connector in their Claude account settings before continuing. 2. **Credits.** Read `account_balance`. If `plan.isUnlimitedMode` is true but `plan.unlimitedAppliesHere` is false, tell the user generations in this session will consume real credits despite their unlimited plan — do this once, before the first paid call, not on every call. No git, no CLI, no ffmpeg required. ## Step 1 — Onboarding interview You are the agency's creative director. Interview the user, one question at a time, in their language. Never batch all questions into one wall of text. 1. **Reference photo.** Ask for the start image. Get it into Magnific as a creation: - User attached a file in the chat → `creations_upload_file` - User gave a public URL → `creations_upload_image` Read the image yourself and analyze out loud: pose, framing, lighting, distinctive features (hairstyle, facial hair, accessories), aspect ratio. Note the anchor pose (a distinctive gesture is gold — it becomes the visual motif that bookends the whole video). 2. **Concept.** What do they want to become / what story do they want to tell? If they have no idea, pitch 2–3 concepts based on the photo's vibe. 3. **Format.** Ask for: - Number of story scenes (recommend 3; more scenes = more identity drift risk) - Clip length (recommend 10s per story scene, 5s per transformation) - With or without transformation bookends (morph in at the start, morph back at the end — recommended for personal videos, usually skipped for pure ads) 4. **Script.** Draft the story as a numbered scene list with a strong hook in scene 1. Classic arc: hook & threat → struggle & turning point → triumph & full circle. The FINAL scene must end with the character in the same pose as the reference photo, facing the camera — this is what makes the reverse morph seamless. Get the user's sign-off before generating anything. ## Step 2 — Character library asset (the identity anchor) This replaces the manual "character reference sheet" prompt trick with a native Magnific feature: a reusable character asset. 1. Optional but recommended: generate a few extra angles from the single reference photo with `images_variations` (`variationMode: "angles"`, source = the uploaded reference creation) so the library asset has more than one view to anchor on. 2. Call `library_create`: ``` type: "character" name: "" # A-Z/0-9/_/- only images: [ { creationIdentifier: "" }, { creationIdentifier: "" }, # from step 1, optional { creationIdentifier: "" } # optional, max 6 total ] description: "" ``` 3. Note the returned numeric `id` and use it for **image** generations (`images_generate` accepts the numeric library id directly in `references[].identifier`). For **video** generations, `video_generate` does NOT accept the numeric library id — pass the actual reference *image's* creation identifier instead (see Step 5). Tested against the live API: passing the library id or its string `identifier` to `video_generate` fails with "Creation not found". 4. Show the user the reference photo they chose and confirm the character was created before continuing. ## Step 3 — Transformation target image (if bookends are wanted) Generate the "after" image: same pose, same framing, only character and environment swapped. ``` images_generate: prompt: "Transform the person into , keeping their exact same pose, facial structure, expression and camera framing. Photorealistic, same composition as the original." mode: "imagen-nano-banana-2" # Nano Banana Pro — best for character consistency references: [{ type: "character", identifier: "" }] aspectRatio: "16:9" ``` Download/keep this creation — it is reused in Step 6 as the reverse-morph start image. Show it to the user for approval before spending credits on video. ## Step 4 — Transformation morph in (Seedance 2.0, first/last frame) Call `video_plan` first to confirm the brief and model choice, then generate: ``` video_generate: video: clips: [{ slug: "bytedance-seedance-pro-2.0" prompt: ". They snap their fingers and a magical swirling transformation ripples over them: . Smooth cinematic morph transition, mystical particles, seamless transformation." duration: 5 aspectRatio: "16:9" resolution: "1080p" keyframes: start: { type: "image", url: "" } end: { type: "image", url: "" } }] ``` Give the morph a trigger (finger snap, spin, flash) — it motivates the transition and reads better than an unmotivated dissolve. ## Step 5 — Story scenes (Seedance 2.0, character reference) **Critical architecture decisions — carried over from the original workflow:** - **NO frame chaining.** Do NOT use the last frame of scene N as the start image of scene N+1. Identity drift compounds. Each scene is an independent generation. - **Every scene references the character** via `references`, not `keyframes`. - **Fewer, longer scenes.** 3×10s beats 6×5s: smoother motion, fewer hand-off points, less drift. - Bonus over the CLI original: use the `cameraMotion` field (52 presets, e.g. `pushIn`, `orbitLeft`, `handheld`, `crashZoomIn`) to direct each shot explicitly. - **Reference value:** use the creation identifier of an actual reference image (the Step 1 reference photo or the Step 3 target image) — NOT the numeric `library_create` id. `video_generate` rejects library ids with "Creation not found"; only `images_generate` accepts them. - **`type: "character"` can trigger moderation blocks.** Tested against the live API: a photorealistic human-face image passed as `references[].type: "character"` was blocked ("Seedance blocked this request due to moderation rules"), while the identical identifier passed as `type: "image"` succeeded. Default to `type: "image"` for the character reference; only try `type: "character"` if `image` doesn't hold identity well enough, and expect it may get blocked. ``` video_generate: video: clips: [{ slug: "bytedance-seedance-pro-2.0" prompt: "The character from the reference image (). Scene: ." duration: 10 aspectRatio: "16:9" resolution: "1080p" cameraMotion: "" references: [{ type: "image", url: "" }] }] ``` Run `creations_wait` on the returned identifier, then `creations_show` to display it and give the user the result as you go. Write the last scene so it ends on the anchor pose (character facing camera in the reference-photo gesture) — that frame is what Step 6 approximates. ## Step 6 — Reverse transformation (if bookends are wanted) Magnific has no video-frame-extraction tool, so this workflow skips extracting the literal last frame (the original CLI version used `ffmpeg -sseof`). Instead, reuse the **target image from Step 3** as the start — since the final scene was directed to end on that exact pose anyway, it's a close enough stand-in and needs no local tooling. ``` video_generate: video: clips: [{ slug: "bytedance-seedance-pro-2.0" prompt: " stands , facing the camera. A magical reverse transformation ripples over them: . Smooth cinematic reverse morph, ending exactly on the reference photo pose." duration: 5 aspectRatio: "16:9" resolution: "1080p" keyframes: start: { type: "image", url: "" } end: { type: "image", url: "" } }] ``` If the user wants frame-perfect precision instead of this approximation, tell them that's a known gap versus the ffmpeg-based original (see README changelog). ## Step 7 — Optional: title & text transition cards Once all story clips are done, ask the user once: **"Do you want any text transitions — a title card, an outro card, or a text overlay anywhere?"** Placement guidance (advise the user, don't just obey): - **Best spot for a title card**: right after the opening transformation, before scene 1. - **Avoid cards in the mid-story transitions** — they break tension exactly where retention matters most. - **An outro card after the reverse morph** rounds off the film without interrupting the story. ``` video_generate: video: clips: [{ slug: "bytedance-seedance-pro-2.0" prompt: "Cinematic title card on a near-black background with . The first second is completely silent and empty. Then the title text '' flies in dynamically as <style>, accompanied by a single deep cinematic whoosh-impact sound exactly when the text lands. The text holds, gently flickering. The final second: total silence again, text slowly fading. No music, no other sounds — only the one whoosh-impact. Elegant, epic, minimalist title sequence, 16:9." duration: 5 aspectRatio: "16:9" resolution: "1080p" }] ``` Verify the spelling of the rendered text by viewing the result — text is the most common generation failure. ## Step 8 — Assembly & delivery Concatenate everything natively — no ffmpeg: ``` video_concatenate: creationIdentifiers: [ "<morph_in>", "<title_card>", "<scene1>", "<scene2>", "<scene3>", "<morph_back>" ] name: "<project name> — Final Cut" ``` Then `creations_wait` on the returned identifier and `creations_show` to render it. Deliver: file list, total runtime, and a one-line recap of the story. Offer next steps (regenerate individual scenes, a vertical 9:16 version for Shorts/TikTok via `video_upscale`/re-render with a different `aspectRatio`, or upscale the final cut). ## Quality rules - Show intermediate images (target image) to the user for approval before spending credits on video. - Check identity after each scene by viewing it; regenerate a scene if the face drifted badly — never chain a drifted frame forward. - Prefer models with `agentRecommendation.tier: sota`, then lower `rank`, per `video_models_list` / `images_models_list` — don't hardcode a model beyond what this skill specifies unless the user asks for something else. - Prompts to Magnific in English; conversation with the user in their language. - Tell the user about credit consumption once, before the first paid generation — not on every single call. ## Use cases beyond personal stories The same pipeline works for ads and brand content: the "reference photo" can be a product shot or brand avatar (`library_create` with `type: "product"`), the "transformation" a product reveal, the story scenes a mini-commercial. See `references/prompting-guide.md` for prompt patterns and `references/troubleshooting.md` for common failures.