unreal-automotive-rain-film

unreal-automotive-rain-film is a skill for Claude Code, Codex from dcc-mcp/dcc-mcp-unreal. It costs 85 tokens per session (462 once invoked), scanned A, original, no licence file.

A workflow for creating, checking, and rendering a rain-soaked Audi A5 film in Unreal Engine 5.8 using Epic's Automotive Configurator sample. It covers the specified car asset, wet-material look development, rain effects, lighting, camera focus, and 4K rendering.

In plain words
What is it for?
Use it to build or audit the official BPAudiA5 promotional scene, including wet surfaces, Niagara rain and impact effects, HDRI lighting, cinematic depth of field, and Movie Render Queue output.
Why use it?
It gives the agent a defined process for producing this particular cinematic car scene with the required visual and rendering setup.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to build or audit the official BPAudiA5 promotional scene, including wet surfaces, Niagara rain and impact effects, HDRI lighting, cinematic depth of field, and Movie Render Queue output.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dcc-mcp/dcc-mcp-unreal/unreal-automotive-rain-film
Install

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.

Any agent
npx skills add dcc-mcp/dcc-mcp-unreal --skill unreal-automotive-rain-film
Clone the repo
git clone --depth 1 https://github.com/dcc-mcp/dcc-mcp-unreal

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for unreal-automotive-rain-film

README.md
[![agentmods](https://agentmods.dev/badge/skills/dcc-mcp/dcc-mcp-unreal/unreal-automotive-rain-film/github.svg)](https://agentmods.dev/skills/dcc-mcp/dcc-mcp-unreal/unreal-automotive-rain-film)
Your own site
<a href="https://agentmods.dev/skills/dcc-mcp/dcc-mcp-unreal/unreal-automotive-rain-film"><img src="https://agentmods.dev/badge/skills/dcc-mcp/dcc-mcp-unreal/unreal-automotive-rain-film/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.

agentmods 80×15 button for unreal-automotive-rain-film

Your own site · 80×15
<a href="https://agentmods.dev/skills/dcc-mcp/dcc-mcp-unreal/unreal-automotive-rain-film"><img src="https://agentmods.dev/badge/skills/dcc-mcp/dcc-mcp-unreal/unreal-automotive-rain-film.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 85 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 462 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00085 $0.00462
Opus 5 $0.00043 $0.00231
Sonnet 5 $0.00017 $0.00092
Haiku 4.5 $0.00009 $0.00046

Measured 12d ago against content hash ebfa4e01cb1d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

unreal-automotive-rain-film 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.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/_automotive_common.py, scripts/_movie_render.py, scripts/audit_audi_asset.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

src/dcc_mcp_unreal/skills/unreal-automotive-rain-film/SKILL.md · 41 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

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.

Changes

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.

  1. 12d ago First seen · 41 lines · 85 tokens per session scan A ebfa4e01cb1d

Subscribe to this mod's changes

unreal-automotive-rain-film is a skill published in the GitHub repository dcc-mcp/dcc-mcp-unreal (4 stars, last pushed yesterday), with no licence file. It adds 85 tokens to every session and 462 once invoked, about $0.0004 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 2026-08-31.

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