Agent skill · Media & Video

shortfilm-prompt

Generate cinematic AI shortfilm prompts (works with Seedance 2.0, Xiaoyunque, Sora, Kling, Jimeng, Veo) using the 5-stage structure from Mx-Shell's Zombie Scavenger. Trigger when the user wants transformation sequences, multi-shot narrative shorts, weapon-charge/combat segments, emotional family/pet/farewell narratives (催泪/亲情/萌宠/离别), or any cinematic video prompt.

jnMetaCodegithub.com/jnMetaCodeGitHub ↗
claude-codeMIT
Install
npx skills add jnMetaCode/ai-shortfilm-prompts --skill shortfilm-prompt --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 10
SKILL.md size: 19 KB
Bundled scripts: none
Path: skills/shortfilm-prompt/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 318
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# shortfilm-prompt — Cinematic AI Video Prompt Generator You play the role of a director's assistant fluent in the 5-stage AI shortfilm prompt structure (first proven by Mx-Shell in *Zombie Scavenger*). When the user invokes this skill they want a prompt they can paste directly into a video model: Seedance 2.0 / Xiaoyunque / Sora / Kling / Jimeng / Veo. **Model-agnostic core**: the 5-stage structure itself is the same across all models. At the end of your output, give one line of model-specific advice (Sora prefers concise; Kling is more permissive on IP names; Seedance blocks IP names; etc.). ## Workflow (execute in order) ### Step 1 — Did the user already specify enough? If their initial request already includes **all** of the following, skip Step 2 and go straight to Step 3: - Video type (transformation / multi-shot narrative / **emotional narrative (family · pet · farewell)** / atmospheric single shot / weapon-charge / combat / static character poster) - Duration (5s / 10s / 15s / 20s / multi-shot edited) - Subject base setup (person / robot / mech) - Scene (location + time + atmosphere) - Visual style preference (reference film or aesthetic) ### Step 2 — If info is incomplete,

What's inside
Steps it walks through
  1. Workflow (execute in order)
  2. Step 1 — Did the user already specify enough?
  3. Step 2 — If info is incomplete, ask at most 2–3 key questions
  4. Step 3 — Output a prompt in the 5-stage structure
  5. Step 4 — Briefly explain 2–3 of your writing choices
  6. Template library (load the matching one)
  7. Methodology core (must follow)
  8. Emotional narrative adaptation (family · pet · farewell)
  9. Stage 1 · Core theme
  10. Stage 2 · Character & scene
  11. Stage 3 · Atmosphere & quality (the key trick)
  12. Stage 4 · Camera rules
  13. Stage 5 · Storyboard
  14. Negative prompts (model-dependent)
Ships with 9 files
  • SKILL.zh.md
  • TESTING.md
  • examples/01-mecha-energy-shield.md
  • examples/02-skill-output-sample.md
  • examples/03-multi-shot-cat-encounter.md
  • examples/04-weapon-charge-combat.md
  • examples/05-ip-name-forced.md
  • examples/06-emotional-pet-farewell.md
  • examples/README.md
About this skill
What does the shortfilm-prompt skill do?

Generate cinematic AI shortfilm prompts (works with Seedance 2.0, Xiaoyunque, Sora, Kling, Jimeng, Veo) using the 5-stage structure from Mx-Shell's Zombie Scavenger. Trigger when the user wants transformation sequences, multi-shot narrative shorts, weapon-charge/combat segments, emotional family/pet/farewell narratives (催泪/亲情/萌宠/离别), or any cinematic video prompt.

How do I install it?

Run `npx skills add jnMetaCode/ai-shortfilm-prompts --skill shortfilm-prompt --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From jnMetaCode/ai-shortfilm-prompts, a repository with 318 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going