rediumvex/ai-video-generator-claude: 10 Claude skills that write Seedance 2.0 prompts

What ai-video-generator-claude actually solves

The problem the repository names is prompt quality, not video generation. Its README argues that most people write two-line prompts and get generic output, because Seedance 2.0 responds to specific camera language, lighting terminology and timing breakdowns. The ten skills exist to convert a short concept into a 15 to 25 line prompt with timestamps, camera moves, lighting presets, sound layers and platform settings.

The audience is narrow and specific: someone who already uses Claude Code, already has access to Seedance 2.0 through Higgsfield, and publishes short-form video on a schedule. The README frames the skills as the pipeline behind an Instagram account with 300K+ followers, and lists the styles by intent: views (Viral Hook, Faceless Channel, Podcast Visual), business (SaaS Launch, Course Promo, Testimonial Story) and brand (Personal Brand, Luxury Aesthetic, Before-After, AI Avatar).

If you do not have the Higgsfield side, none of this produces a frame. The repository contains no model weights, no API client and no rendering code. Its output is text you paste into another product.

How the skills are structured and what Claude does with them

The repository layout is flat and readable: LICENSE, README.md, install.py and skills/. Each skill directory is described in the README as containing a 2-second hook framework with 10+ attention patterns, a beat-by-beat timeline for a 4 to 15 second clip, a camera movement list with 15+ techniques and the exact phrasing Seedance 2.0 understands, lighting and mood presets, a four-layer sound design stack, a material reference system for @image1, @video1 and @audio1, platform tuning for TikTok, Reels, Shorts and YouTube, and 5+ example prompts.

So the mechanism is retrieval by convention rather than a running program. Claude Code discovers skill directories under ~/.claude/skills, the user's concept selects one or more of them, and the skill's written framework shapes the model's response. The Python file is an installer, not an inference path. The README does not document how skill selection is arbitrated when a request spans two skills, though it does give an example of combining Viral Hook with Faceless Channel.

That design has a real consequence. The output quality depends on the model reading the framework and on the user pasting the result into Higgsfield correctly. Nothing in the repository validates the prompt before you spend a generation on it.

Installing ai-video-generator-claude and generating a first prompt

The README gives two manual routes and one installer. The manual route clones the repository directly into the Claude skills folder, renaming it to ai-video-generator.

cd ~/.claude/skills
git clone https://github.com/rediumvex/ai-video-generator-claude.git ai-video-generator

A second variant targets users of gstack, cloning into ~/.claude/skills/gstack instead. After cloning, the README says to restart Claude Code, at which point each skill inside skills/ becomes available.

The interactive installer offers more control. Running it without flags prompts you to pick which skills to install, --all installs everything and --list previews what is available before you commit.

cd ai-video-generator
python install.py          # Pick which skills to install
python install.py --all    # Install everything
python install.py --list   # Preview available skills

For a first real use, the README's own example is the clearest test: ask for a 10 second product launch video for a SaaS app with dark mode UI and a minimal aesthetic, and the SaaS Launch skill is meant to return a complete prompt with camera, lighting, sound and timing. Expect a multi-line text block, not a file. The README does not state where the generated prompt is written, so treat the chat output as the artifact and copy it into Higgsfield yourself.

Where the repository stops: no rendering, no validation, no rollback

The most important limitation is stated by the README itself: the skills produce prompts, and you paste them into Higgsfield to get video. There is no Higgsfield integration in the repository, no API key handling, no upload step and no queue. The related search phrase about a Higgsfield MCP connection is not answered anywhere in the README, which documents only clipboard-style handoff.

A second gap is verification. The README asserts that Seedance 2.0 responds better to phrases like "dolly forward 3 feet, f/1.4, rack focus from foreground to background" than to "make it look cinematic," but it publishes no comparison, no sample outputs and no failure cases. You cannot tell from the repository which of the ten skills is strongest, or how often a generated prompt needs manual editing before it produces usable footage.

Third, the installer's behaviour on an existing installation is undocumented. The README does not describe what happens if you run python install.py --all over a directory you have already edited, and there is no rollback path described. If you plan to modify the skill files, keep your own copy before reinstalling.

Comparing it with a general-purpose prompt library

The obvious alternative is a plain prompt library: a text file or Notion page of Seedance 2.0 prompts you copy by hand. The difference is in how the prompt is assembled. A library gives you a fixed prompt to adapt; these skills give Claude a framework (hook patterns, timeline architecture, camera vocabulary, lighting presets, sound layers) and let the model build a prompt for the concept you describe. That matters when your input changes shape every time, which is normal for social video.

The trade-off runs the other way too. A library is inspectable and predictable: you know exactly what you are pasting. A skill-driven prompt varies with the model's reading of the framework and with how you phrase the request, so two people asking for the same 10 second SaaS clip may get materially different prompts. The README does not discuss this variance or offer a way to pin output style.

A second alternative is writing prompts directly against Seedance 2.0's own documentation. That is cheaper in setup and gives you full control, but it puts the burden of remembering camera phrasing and sound layering on you every time. The repository's value is that this knowledge is written down once and reused.

Licence, maintenance and the cost of keeping it current

The repository is MIT licensed, Copyright (c) 2026 Roman Knox. MIT permits commercial use, modification and redistribution provided the copyright notice and permission notice are retained. That matters here because the skills are text you may want to fork and adapt to your own brand voice. This is a description of the licence terms, not legal advice; check the LICENSE file for the exact wording.

The last push to the default branch was on 2026-08-11, and the repository is not archived. There are no retrieved releases, so updates arrive as commits to main rather than versioned tags. The upgrade cost is therefore low in mechanics (a git pull in ~/.claude/skills/ai-video-generator) but carries the usual risk of a moving branch: if you have edited skill files locally, a pull can conflict.

The bigger maintenance question is external. The skills encode phrasing the README attributes to Seedance 2.0. If that model's prompt handling changes, the camera and lighting vocabulary in skills/ may need revision, and nothing in the repository signals when that happens. Budget for periodically re-reading the skill files against whatever Seedance 2.0 currently accepts.

Editorial conclusion

Adopt it if you already run Claude Code and Higgsfield and want a repeatable structure for short-form video prompts, particularly for TikTok, Reels and Shorts formats. Skip it if you expect the repository to render video: it produces text prompts only, and you still need a Higgsfield account and Seedance 2.0 access to see any footage. Before relying on it, open skills/ and confirm which of the ten skill directories are actually present, since the README's table and the installer's --list output are the only inventory the repository publishes.

Frequently asked questions

Official sources

1. Issues
2. License: MIT
3. README
4. rediumvex/ai-video-generator-claude on GitHub