Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add SupercmoHQ/superCMO-skills --skill writing-video-promptsgit clone --depth 1 https://github.com/SupercmoHQ/superCMO-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/supercmohq/supercmo-skills/writing-video-prompts)<a href="https://agentmods.dev/skills/supercmohq/supercmo-skills/writing-video-prompts"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/writing-video-prompts/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/supercmohq/supercmo-skills/writing-video-prompts"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/writing-video-prompts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00102 | $0.03502 |
| Opus 5 | $0.00051 | $0.01751 |
| Sonnet 5 | $0.00020 | $0.00700 |
| Haiku 4.5 | $0.00010 | $0.00350 |
Grade A, and why
writing-video-prompts scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clip prompts
Turn a clip into a prompt its video model will honour. Text only — nothing is generated here.
A video may be one clip or several. Write a prompt for every clip in it, then hand them all back together. Clips in one video share a model, an aspect ratio and a look.
Write only what the clip actually has. No person on camera means no performance direction, no product means no product rules, no speech means no dialogue.
Workflow
Step 1: Read what the clip has
Ask only for what would change the prompt and can't be defaulted.
- The model and the duration. Where no model was named, use
seedance-2.0-fastand say so. The aspect ratio is a call parameter rather than prompt text — carry it if it was given, don't ask. - What happens in the clip — as beats, or as a single action.
- The media, and what each one is for — a storyboard sheet, a start frame, an end frame, reference images, a source video, an audio track. Any of them may be absent, and they differ from clip to clip.
- The dialog segment, where anyone speaks, and whether they are on camera or heard over the picture.
Step 2: Pick the clip's form
With a storyboard sheet, one cut per panel — panel 1 is the first cut, panel 2 the second, hard cut between them.
Without a sheet, cut only where the brief asks for one.
Where a beat's opening frame is fixed — by a panel or by a start frame — the hands, the grip, the product, the props and the positions are already there when it opens. Write what happens next, not how it starts. Write "a hand reaches in" or "she brings it into frame" only when the frame genuinely has no such element. Introduce something that is already there and the model renders a second one — an extra hand, a duplicate arm, a doubled person.
Step 3: Read the chosen model's guide
| Model | Guide |
|---|---|
seedance-2.5 |
references/prompt-seedance.md |
seedance-2.0 |
references/prompt-seedance.md |
veo-3.1 |
references/prompt-veo.md |
gemini-omni |
references/prompt-gemini-omni.md |
kling-3.0-pro |
references/prompt-kling.md |
grok-imagine-video |
references/prompt-grok.md |
wan-2.7 |
references/prompt-wan.md |
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 252 lines · 102 tokens per session scan A 47907cb55b30
writing-video-prompts is a skill published in the GitHub repository SupercmoHQ/superCMO-skills (38 stars, last pushed 15d ago), licensed Apache-2.0. It adds 102 tokens to every session and 3,502 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
best-practices
Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.
llm-application-dev
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
firebase-ai
Use when setting up firebaseai, generating text/chat with Gemini, streaming AI output, building multimodal prompts, or handling AI errors.
coding-agents-prompt-authoring
To author, adapt, review, and validate prompts (skills, agents, workflows, rules, etc.) with brief, contracts, and a validation pack.
fixing-prompt
Prompt: Prompt Refinement and Optimization.
thought-based-reasoning
Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts…