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 (Google Veo, ByteDance Seedance, OpenAI TTS, transcription, etc.) are configured under your Hyper integrations.
How to run the tools in this skill
Every tool in this skill is named by its canonical tool name. Run it with the call your surface gives you:
| Surface | Find a tool | Run it |
|---|---|---|
| Hyper chat | Call the tool by its name; search_tools(queries=["<provider> <action>"]) shows its parameters | <name>(...) |
| MCP client (Claude, Cursor, Codex, ChatGPT) | search("<what you want to do>"), then describe("<name>") | call_read("<name>", {...}), call_write or call_destructive: the one that describe names in call_with |
| Hyper CLI | hyperai search "<what you want to do>", then hyperai describe <name> | hyperai call <name> --json '{...}' |
If a tool is not found: in Hyper chat, find its toolkit id with discover_toolkits and pass it to enable_toolkit. On an MCP client or the CLI, tell the user which integration to connect in Hyper.
Tool surface
| Group | Tools |
|---|---|
| Generation | videos_generate |
| Analysis | videos_analyze, videos_frames_capture, videos_transcribe |
| Subtitles & captions | videos_subtitles_generate, videos_subtitles_burn, videos_captions_burn_highlighted |
| Audio | audio_speech_generate, videos_audio_add |
| Editing | videos_clips_extract, videos_stitch, videos_text_overlays_add |
Out of scope
- Image generation, ad creative composition, brand extraction — use
image-generationorad-creative-generation. - Posting finished videos to social platforms — use
tiktok,instagram, orlinkedin. - Running paid video campaigns — use
google-ads,meta-ads,tiktok-ads.
Available Tools
| Tool | Purpose | Runs in Background |
|---|---|---|
videos_generate | Generate video from text / image prompt | Yes |
videos_frames_capture | Extract frame as image | No |
videos_analyze | Watch and understand video content | No |
videos_transcribe | Extract audio transcript | No |
videos_subtitles_generate | Create SRT / VTT subtitle file | No |
videos_subtitles_burn | Burn subtitles onto video | Yes |
videos_captions_burn_highlighted | TikTok / karaoke-style word-by-word captions | Yes |
audio_speech_generate | Generate voiceover audio from text | No |
videos_audio_add | Add / replace audio track on video | Yes |
videos_clips_extract | Extract a time segment from video | Yes |
videos_stitch | Concatenate multiple clips | Yes |
videos_text_overlays_add | Add text / titles to video | Yes |
Video Understanding
You can watch and analyze any video using videos_analyze. This sends the video to a multimodal AI that sees both visual and audio content.
When to use videos_analyze
- 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
pythonvideos_analyze(file_id="...", analysis_type="general") videos_analyze(file_id="...", analysis_type="quality_review") videos_analyze(file_id="...", analysis_type="scene_breakdown") videos_analyze(file_id="...", question="Does this match: [original prompt]?")
Self-Review Workflow
Always review generated videos before delivering to the user:
pythonresult = videos_generate(prompt="...", model="veo-3.1-generate-preview") review = videos_analyze(file_id="video_file_id", analysis_type="quality_review") # If issues found, regenerate with adjustments. If quality is good, proceed to editing.
Routing table
All reference files live in
references/. Read them atreferences/<file>(e.g.references/generation.md).
| The user wants to… | Read these files first |
|---|---|
| Generate a video (any model) | references/generation.md — model selection, parameter matrix, prompt templates |
| Build a longer multi-scene video | references/generation.md — script planning + scene chaining |
| Add subtitles / captions / voiceover / overlays, or clip a video | references/post-production.md |
| Produce UGC / TikTok content end-to-end | references/ugc-video.md (ugc_videos_create modes) → references/workflows.md |
| Shape a prompt for a specific model (Veo / Seedance / Kling) | references/video-prompting.md |
| Turn a podcast / long video into short clips | references/workflows.md → references/post-production.md |
| Understand or QA an existing video | Use videos_analyze (see Video Understanding above) |
Best Practices
- Review before delivering: always use
videos_analyzeto check your output. - Maintain visual consistency: use the same character descriptions, lighting, and style across all scenes.
- Plan transitions: design the end of each scene to flow into the next.
- Batch similar scenes: generate scenes with similar settings together.
- Review before chaining: check each scene before using its last frame for the next.
- Use single-variable iteration: remix / regenerate by changing one variable at a time.
- Add captions for accessibility: use the subtitle pipeline for all UGC content.
