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Business judgment on AI products

cinematic-video-prompt-skill

People shooting films or ads open this while writing AI video prompts: instead of describing camera angle, movement, lighting, composition and grading from experience, they use a 700+ term reference and a Claude Skill, then paste the resulting prompt text into Veo 3, Kling, Sora, Runway or Midjourney; the final clip still needs human review.

Not a business yet Early Open-source projectAI + CreativeFilm and video productionAdvertising and marketingShort-video creatorCommercial directorVietnamGlobalCross-market opportunityOpen-source traction 84
Team / maker
Rylaispirit
First tracked here
2026-09-21
Last updated here
2026-09-25
Product site
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01

Why this would be needed

Start inside the user's day · Public facts + observable behavior · 2026-09-23

Use case

Short-video creators and commercial directors writing AI video prompts must translate shooting requirements such as camera angle, movement, lighting, composition and grading into prompt text that Veo 3, Kling, Sora, Runway and Midjourney can act on, ending with usable video clips.

Today they rely on English cinematography tutorials, scattered blog glossaries, asking in communities, or simply rewriting prompts over and over.

Non-English creators lack camera-language vocabulary, so they write vague descriptions or guess repeatedly, and outputs miss the intended framing or movement, burning compute and time on retries; public material only shows the glossary and Skill form, with no user complaints or adoption data.

xOcto's call

Demand is evidenced

The trend is that video models make 'writing correct camera language' the new bottleneck, so terminology is migrating from cinematography textbooks into the prompt layer. The opening is localized camera vocabularies plus industry templates for non-English markets, such as Vietnamese or Indonesian e-commerce ad and short-drama shot packs, sold per template or per finished cut rather than as a generic glossary.

Reason to use it

Why users would choose it

Compared with reading tutorials and trial-and-error, it groups 700+ shot terms by angle, movement, lighting, composition and grading with Vietnamese explanations so creators can look up and substitute terms directly, cutting blind retries; this is inference from product capability, with no retention or repeat-use evidence yet.

Where the easy answer breaks down

The tension worth following

An English validation note will follow from the public evidence.

If this is your job

Worth trying. Compared with reading tutorials and trial-and-error, it groups 700+ shot terms by angle, movement, lighting, composition and grading with Vietnamese explanations so creators can look up and substitute terms directly, cutting blind retries; this is inference from product capability, with no retention or repeat-use evidence yet.

Entry and what to borrow

The trend is that video models make 'writing correct camera language' the new bottleneck, so terminology is migrating from cinematography textbooks into the prompt layer. The opening is localized camera vocabularies plus industry templates for non-English markets, such as Vietnamese or Indonesian e-commerce ad and short-drama shot packs, sold per template or per finished cut rather than as a generic glossary.

What this judgment rests on
Public fact

People shooting films or ads open this while writing AI video prompts: instead of describing camera angle, movement, lighting, composition and grading from experience, they use a 700+ term reference and a Claude Skill, then paste the resulting prompt text into Veo 3, Kling, Sora, Runway or Midjourney; the final clip still needs human review.

Workflow reasoning

Compared with reading tutorials and trial-and-error, it groups 700+ shot terms by angle, movement, lighting, composition and grading with Vietnamese explanations so creators can look up and substitute terms directly, cutting blind retries; this is inference from product capability, with no retention or repeat-use evidence yet.

The unknown that could change the call

An English validation note will follow from the public evidence.

01 · Value Supported

The assessment is recorded; an English explanation is pending.

02 · Consensus Insufficient evidence

The assessment is recorded; an English explanation is pending.

03 · Model Insufficient evidence

The assessment is recorded; an English explanation is pending.

04 · Truth Supported

The assessment is recorded; an English explanation is pending.

02

Chinese and English ecosystems

Market comparison · Cross-market opportunity

English ecosystem · English-language market

Local supply: Emerging
Demand evidence: Not yet verified

Public coverage has been recorded for this market. · 2026-09-25

Chinese ecosystem · CN

Local supply: Not found in covered sources
Demand evidence: Not yet verified

Public coverage has been recorded for this market. · 2026-09-25

There is no full analysis yet. Start with the direction above.

Public information is limited; this view will update as more evidence appears. It was recently added and does not yet have verifiable usage data.

Full analyses of similar products: shuohao-skills, open-ai-canvas

04

Verifiable public evidence

Evidence trail

05

Go from the product name to primary material

Use these searches when the official site is missing or the current link is only a lead.