Video Generation & Editing

Guide for generating, editing, analyzing, and post-processing videos using AI models and FFmpeg-backed tools exposed through the Hyper MCP.

Requirements

This skill assumes the Hyper MCP is connected to your agent so the tools below are available. The underlying providers (OpenAI Sora, Google Veo, ByteDance Seedance, OpenAI TTS, transcription, etc.) are configured under your Hyper integrations.

Tool surface

Group	Tools
Generation	generate_video, sora_remix_video, sora_delete_video
Analysis	analyze_video, capture_video_frame, transcribe_video
Subtitles & captions	generate_subtitles, burn_subtitles, burn_highlighted_captions
Audio	text_to_speech, add_audio_to_video
Editing	clip_video, stitch_videos, overlay_text

Out of scope

• Image generation, ad creative composition, brand extraction — use image-generation or ad-creative-generation.
• Posting finished videos to social platforms — use tiktok, instagram, or linkedin.
• Running paid video campaigns — use google-ads, meta-ads, tiktok-ads.

Available Tools

Tool	Purpose	Runs in Background
generate_video	Generate video from text / image prompt	Yes
sora_remix_video	Modify existing Sora video	Yes
sora_delete_video	Delete a Sora video	No
capture_video_frame	Extract frame as image	No
analyze_video	Watch and understand video content	No
transcribe_video	Extract audio transcript	No
generate_subtitles	Create SRT / VTT subtitle file	No
burn_subtitles	Burn subtitles onto video	Yes
burn_highlighted_captions	TikTok / karaoke-style word-by-word captions	Yes
text_to_speech	Generate voiceover audio from text	No
add_audio_to_video	Add / replace audio track on video	Yes
clip_video	Extract a time segment from video	Yes
stitch_videos	Concatenate multiple clips	Yes
overlay_text	Add text / titles to video	Yes

Video Understanding

You can watch and analyze any video using analyze_video. This sends the video to a multimodal AI that sees both visual and audio content.

When to use analyze_video

• After generating a video: check if it matches your intent
• Before stitching: verify scene consistency across clips
• Quality review: check for glitches, character drift, lighting issues
• Content understanding: "what happens in this video?"

Analysis Types

analyze_video(file_id="...", analysis_type="general")
analyze_video(file_id="...", analysis_type="quality_review")
analyze_video(file_id="...", analysis_type="scene_breakdown")
analyze_video(file_id="...", question="Does this match: [original prompt]?")

Self-Review Workflow

Always review generated videos before delivering to the user:

result = generate_video(prompt="...", model="veo-3.1-generate-preview")
review = analyze_video(file_id="video_file_id", analysis_type="quality_review")
# If issues found, regenerate with adjustments. If quality is good, proceed to editing.

Script Planning

For longer, cohesive videos, plan the FULL SCRIPT before generating:

1. Scene Breakdown

• Scenes: break story into segments
  • Sora: 4 / 8 / 12 seconds per scene
  • Veo: 4-8 seconds per scene
  • Seedance: 4-15 seconds per scene (native audio with lip-sync)
• Camera: shot type (wide, close-up, tracking), angles, movement
• Transitions: how each scene connects to the next
• Consistency: character descriptions, color palette, visual style

Scene Chaining Technique

To create seamless multi-scene videos:

Scene 1 (text-to-video)

generate_video(prompt="...", model="veo-3.1-generate-preview")

Scene 2+ (image-to-video)

capture_video_frame(video_file_id="scene1_file_id", frame_position="last")
generate_video(prompt="continuation: ...", image_file_id="captured_frame_id")

Repeat: extract last frame → generate next scene.

Stitching Scenes Together

After generating all scenes, combine them:

stitch_videos(video_file_ids=["scene1_id", "scene2_id", "scene3_id"])

stitch_videos(
    video_file_ids=["scene1_id", "scene2_id", "scene3_id"],
    transition="crossfade",
    crossfade_duration=0.5,
)

Subtitle / Caption Workflow

Full pipeline: Video → Transcript → Subtitles → Burned Video

transcript = transcribe_video(file_id="video_file_id")

subs = generate_subtitles(file_id="video_file_id", transcript=transcript, format="srt")

burn_subtitles(
    video_file_id="video_file_id",
    subtitle_file_id=subs.file_id,
    style="bold_outline",
    position="bottom",
)

Subtitle Styles

Style	Effect
default	Plain white text
bold_outline	Bold white with black outline (recommended)
shadow	White text with drop shadow
box	White text on semi-transparent black box

Text Overlays

Add titles, lower-thirds, CTAs, and other graphics:

overlay_text(
    video_file_id="video_file_id",
    overlays=[
        {
            "text": "Episode 1: The Beginning",
            "start_time": 0.0,
            "end_time": 3.0,
            "position": "center",
            "font_size": 48,
            "color": "white",
            "background": "black@0.5",
        },
        {
            "text": "Subscribe for more!",
            "start_time": 10.0,
            "end_time": 14.0,
            "position": "bottom-right",
            "font_size": 28,
        },
    ],
)

Overlay Positions

top, bottom, center, top-left, top-right, bottom-left, bottom-right

Voiceover / Narration

Generate natural-sounding voiceover with TTS and add it to any video:

audio = text_to_speech(
    text="Welcome to our product. Here's how it works...",
    voice="nova",
    model="tts-1",
)

add_audio_to_video(
    video_file_id="video_id",
    audio_file_id=audio.file_id,
    mode="replace",
)

add_audio_to_video(
    video_file_id="video_id",
    audio_file_id=audio.file_id,
    mode="mix",
    audio_volume=0.8,
)

Available Voices

alloy, ash, coral, echo, fable, nova (recommended), onyx, sage, shimmer

Highlighted Captions (TikTok / Reels Style)

Add word-by-word highlighted captions that light up as spoken:

burn_highlighted_captions(
    video_file_id="video_id",
    style="tiktok",
    highlight_color="#3B82F6",
    base_color="white",
    words_per_group=3,
    position="center",
)

burn_highlighted_captions(
    video_file_id="video_id",
    style="karaoke",
    highlight_color="yellow",
    base_color="white",
    background="black@0.6",
    words_per_group=4,
    position="bottom",
)

Video Clipping

Extract segments from longer videos:

clip_video(
    video_file_id="long_video_id",
    start_time=45.0,
    end_time=60.0,
)

UGC / TikTok Production Workflow

Complete workflow for producing UGC-style content:

1. Script: plan scenes, dialogue, and visual style
2. Generate: create each scene with generate_video
3. Review: use analyze_video to check each scene for quality
4. Chain: extract last frames with capture_video_frame, generate next scenes
5. Stitch: combine all scenes with stitch_videos
6. Narrate: generate voiceover with text_to_speech + add_audio_to_video
7. Caption: add TikTok-style captions with burn_highlighted_captions
8. Overlay: add titles / CTAs with overlay_text
9. Final review: use analyze_video on the final video for quality check

Example: Narrated UGC Video

generate_video(prompt="...", model="veo-3.1-generate-preview")

audio = text_to_speech(text="Your narration script here...", voice="nova")

add_audio_to_video(video_file_id="generated_video_id", audio_file_id=audio.file_id)

burn_highlighted_captions(video_file_id="narrated_video_id", style="tiktok")

Example: Podcast to Short-Form Clips

transcript = transcribe_video(file_id="podcast_video_id")

analysis = analyze_video(
    file_id="podcast_video_id",
    question="Identify the 3 most memorable / quotable moments with timestamps",
    analysis_type="scene_breakdown",
)

clip_video(video_file_id="podcast_video_id", start_time=120.0, end_time=150.0)
clip_video(video_file_id="podcast_video_id", start_time=340.0, end_time=365.0)

stitch_videos(video_file_ids=["clip1_id", "clip2_id"])

burn_highlighted_captions(video_file_id="stitched_id", style="tiktok")

Prompt Structure

Each scene prompt should include:

• "Continuation of previous scene" (for scenes 2+)
• Consistent character / setting descriptions
• Specific action for this segment
• Camera movement direction

Control Principles (most important)

• Treat API params as the container and prompt text as the content:
  • model, size / aspect_ratio, and duration_seconds must be set explicitly in the tool call.
  • Do not expect prose like "make it longer" or "make it vertical" to override API parameters.
• Use detail for control, brevity for exploration:
  • Short prompts give more creative variation.
  • Detailed prompts improve consistency and shot control.
• Iterate in small steps:
  • Change one major variable at a time (camera, lighting, action, or palette).
  • Keep what works fixed and only modify the target dimension.

Key Parameters

Parameter	Description
image_file_id	Use for image-to-video (scene continuity)
capture_video_frame	Extract frames with position="last" | "first" | "middle"
size	For Sora only. One of: "720x1280", "1280x720", "1024x1792", "1792x1024"
aspect_ratio	For Veo only. One of: "16:9" or "9:16"
duration_seconds	Sora: 4, 8, or 12 seconds only. Veo: 4-8 seconds

Important Input Rules

• Use exact values accepted by the tool schema. Do not send aliases like landscape, portrait, 720p, or 1080p.
• For Sora, prefer size and do not send aspect_ratio.
• For Veo, prefer aspect_ratio and do not send size.
• For Seedance, use aspect_ratio and optionally resolution. Do not pass size.
• Keep the same size / aspect_ratio across chained scenes for continuity.

Model Selection Guide

When the user mentions a specific model name, always use that model. Map user requests to the correct model parameter:

User says	model parameter
"use seedance", "seedance video"	"seedance-2"
"fast seedance"	"seedance-2-fast"
"use sora", "sora video"	"sora-2"
"sora pro"	"sora-2-pro"
"use veo", "veo video"	"veo-3.1-generate-preview"
"fast veo"	"veo-3.1-fast-generate-preview"

Model-Specific Parameter Matrix

• Sora models (sora-2, sora-2-pro)
  • Allowed sizing parameter: size
  • Allowed size values: "720x1280", "1280x720", "1024x1792", "1792x1024"
  • Do not pass aspect_ratio
  • Practical default pair: "1280x720" or "720x1280"
• Veo models (veo-3.1-generate-preview, veo-3.1-fast-generate-preview)
  • Allowed sizing parameter: aspect_ratio
  • Allowed aspect_ratio values: "16:9", "9:16"
  • Do not pass size
• Seedance models (seedance-2, seedance-2-fast)
  • Allowed sizing parameters: aspect_ratio and resolution
  • Allowed aspect_ratio values: "16:9", "9:16", "1:1", "4:3", "3:4"
  • Allowed resolution values: "480p", "720p"
  • Supports generate_audio=true for native audio with lip-sync
  • Do not pass size

Duration Limits

• Sora: 4, 8, or 12 seconds per generation. Use scene chaining + stitch_videos for longer videos.
• Veo: 4, 5, 6, 7, or 8 seconds per generation.
• Seedance: 4-15 seconds per generation (most flexible). Supports generate_audio=true for native audio with lip-sync.

High-Control Prompt Template

Use this structure when you want predictable output:

Style/Tone: [realistic, cinematic, animation, documentary, etc.]
Subject/World: [who/what is in frame, key visual anchors]
Camera: [shot size + angle + movement]
Lighting/Palette: [light direction + 3-5 color anchors]
Action Beats:
- [beat 1 with timing/count]
- [beat 2 with timing/count]
- [beat 3 with timing/count]
Audio/Dialogue: [short lines or ambient cues]
Constraints: [no logos/brands, no text overlays, etc.]

Best Practices

1. Review before delivering: always use analyze_video to check your output.
2. Maintain visual consistency: use the same character descriptions, lighting, and style across all scenes.
3. Plan transitions: design the end of each scene to flow into the next.
4. Batch similar scenes: generate scenes with similar settings together.
5. Review before chaining: check each scene before using its last frame for the next.
6. Use single-variable iteration: remix / regenerate by changing one variable at a time.
7. Add captions for accessibility: use the subtitle pipeline for all UGC content.