tiktok-motion-factory Skill
description: Run the deterministic, non-AI Remotion TikTok motion graphics pipeline on a local audio file and produce a creative-direction spec.
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Why use this skill
tiktok-motion-factory is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around utility skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Generic Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in diegosouzapw/awesome-omni-skill, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
---
name: tiktok-motion-factory
description: Run the deterministic, non-AI Remotion TikTok motion graphics pipeline on a local audio file and produce a creative-direction spec.
---
# TikTok Motion Factory Skill (Repo Root)
This repository is the skill directory. Invoking **$tiktok-motion-factory** must behave like a creative director + motion designer while keeping the pipeline **deterministic**. You may use AI for **audio assessment** and **web inspiration research**, but the **final rendered graphics must be non-AI** and strictly procedural.
## Hard Constraints (Must Repeat and Enforce)
- **AI usage is limited.** You may use AI for **audio assessment** and **web inspiration research** only. The final product must not contain AI-generated graphics; visuals must be procedural and deterministic.
- **Audio must be local.** If a URL is provided, refuse and ask for a local file path.
- **Only 1–2 optional preference questions** (vibe: `editorial` vs `club`; text `on/off`). If the user doesn’t answer, choose defaults.
- **Tailored concept every time.** The concept must reference measured audio features (tempo/energy/onset density/dynamics) and map them to design choices.
## Agent Protocol (Step-by-Step)
1) **Collect input (minimal questions):**
- Ask for a local audio file path. If missing, state you will use the newest file in `input/`.
- Ask at most two optional preference questions:
- Vibe: `editorial` or `club` (default: `editorial`).
- Text: `on` or `off` (default: `on`).
- If the user provides a URL, **refuse** and ask for a local path.
2) **Prepa
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