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: "<short_unique_name>"        # A-Z/0-9/_/- only
   images: [
     { creationIdentifier: "<reference photo>" },
     { creationIdentifier: "<angle 1>" },   # from step 1, optional
     { creationIdentifier: "<angle 2>" }    # optional, max 6 total
   ]
   description: "<concrete facial features: hair, beard, eye color, face shape>"
   

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 <target character>, keeping their exact same
    pose, facial structure, expression and camera framing. <Describe swapped
    elements: clothing, props in the raised hand, new background.> Photorealistic,
    same composition as the original."
  mode: "imagen-nano-banana-2"   # Nano Banana Pro — best for character consistency
  references: [{ type: "character", identifier: "<library id from Step 2>" }]
  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: "<Describe the person as they are now>. They snap their fingers and a
        magical swirling transformation ripples over them: <what morphs into what —
        clothes, hair, background>. Smooth cinematic morph transition, mystical
        particles, seamless transformation."
      duration: 5
      aspectRatio: "16:9"
      resolution: "1080p"
      keyframes:
        start: { type: "image", url: "<reference photo creation id>" }
        end:   { type: "image", url: "<target image creation id from Step 3>" }
    }]

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 (<costume look>). Scene:
        <full scene description with action, camera movement, lighting, mood>."
      duration: 10
      aspectRatio: "16:9"
      resolution: "1080p"
      cameraMotion: "<optional preset>"
      references: [{ type: "image", url: "<target image creation id from Step 3>" }]
    }]

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: "<Character> stands <in the final scene setting>, facing the camera. A
        magical reverse transformation ripples over them: <what morphs back — hair,
        clothes, background returning to the original>. Smooth cinematic reverse
        morph, ending exactly on the reference photo pose."
      duration: 5
      aspectRatio: "16:9"
      resolution: "1080p"
      keyframes:
        start: { type: "image", url: "<target image creation id from Step 3>" }
        end:   { type: "image", url: "<reference photo creation id>" }
    }]

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 <theme-matching
        elements>. The first second is completely silent and empty. Then the title
        text '<TITLE>' 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.