ad_video_create

ad_video_create is a skill for Claude Code, Codex from inclusionAI/AWorld. It costs 64 tokens per session (2,565 once invoked), scanned A, original, MIT.

A workflow for creating advertising videos from product images, optionally including a person, pet, or other subject and background music.

In plain words
What is it for?
Use it to make promotional videos for products such as pet goods, furniture, or technology, using an existing subject image or generating a suitable subject when none is provided.
Why use it?
It helps turn still product photos and supporting assets into a planned commercial video with product, lifestyle, detail, and usage shots.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to make promotional videos for products such as pet goods, furniture, or technology, using an existing subject image or generating a suitable subject when none is provided.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/inclusionai/aworld/ad_video_create_skill
About the project

AWorld is an agent harness, meaning a framework that coordinates an AI agent’s tools, memory, context, and execution so expert knowledge can be turned into reusable skills and autonomous agents. It is for building domain-specific agent applications and workflows, with the catalogue entries representing skills, agents, and commands that operate within the AWorld ecosystem.

inclusionAI/AWorld · 1,246 stars · on GitHub · aworldagents.com

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 inclusionAI/AWorld --skill ad_video_create_skill
Clone the repo
git clone --depth 1 https://github.com/inclusionAI/AWorld

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 ad_video_create

README.md
[![agentmods](https://agentmods.dev/badge/skills/inclusionai/aworld/ad_video_create_skill/github.svg)](https://agentmods.dev/skills/inclusionai/aworld/ad_video_create_skill)
Your own site
<a href="https://agentmods.dev/skills/inclusionai/aworld/ad_video_create_skill"><img src="https://agentmods.dev/badge/skills/inclusionai/aworld/ad_video_create_skill/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 ad_video_create

Your own site · 80×15
<a href="https://agentmods.dev/skills/inclusionai/aworld/ad_video_create_skill"><img src="https://agentmods.dev/badge/skills/inclusionai/aworld/ad_video_create_skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,565 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original 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 $0.00064 $0.02565
Opus 5.5 $0.00026 $0.01026
Sonnet 5.5 $0.00013 $0.00513
Haiku 4.5 $0.00006 $0.00257

Measured 1mo ago against content hash e59317051057, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ad_video_create 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 1mo 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.

aworld-skills/ad_video_create_skill/SKILL.md · 274 lines

How it starts

The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Workflow Architecture

Phase 1: Asset Preparation & Analysis

Input Requirements:

  • Primary Asset (Required): Product image (e.g., cat tower, furniture, gadget)
  • Character/Subject Asset (Optional): Supporting character image (e.g., pet, person, lifestyle element)
  • Audio Asset (Optional): Background music file (MP3 format)

Process:

  1. Asset Discovery: Scan working directory for available assets
  2. Media Comprehension:
    • Activate media_comprehension skill
    • Analyze product image to understand:
      • Product features and characteristics
      • Color palette and material textures
      • Suitable environment context
    • If character image exists, analyze its attributes (appearance, pose, mood)

Phase 2: Character Generation (Conditional)

Trigger Condition: No character/subject image provided

Process:

  1. Based on product analysis from Phase 1, determine appropriate character type:
    • For pet products → Generate pet character (matching product target audience)
    • For home goods → Generate lifestyle character or scene element
    • For tech products → Generate user persona or usage scenario
  2. Call image_generator with detailed prompt:
    • Character attributes aligned with product positioning
    • Pose and expression suitable for composition
    • Style consistency with product aesthetic

Output: Character image ready for composition


Phase 3: Image Composition with Environment

Objective: Create a realistic advertisement scene combining product + character + environment

Key Requirements:

  • Single Character Constraint: Ensure only ONE character appears in final composition
  • Environment Background: Must include realistic home/lifestyle setting, not plain white background
  • Natural Integration: Character should interact naturally with product

Process:

  1. Prepare input images:
    • Product image (original or compressed if >50KB)
    • Character image (from Phase 2 or user-provided)
  2. Call image_generator with composition directive:
    {
      "content": "Compose [character description] with [product description] in [environment setting]. 
                  Requirements:
                  - Only ONE character in the scene
                  - Realistic home environment (floor, walls, natural lighting, plants, furniture)
                  - Natural interaction between character and product
                  - Professional product photography style",
      "info": {
        "image_urls": ["product.jpg", "character.jpg"],
        "size": "1328x1328",
        "guidance_scale": 4.5-5.0,
        "num_inference_steps": 30-35,
        "watermark": false,
        "output_path": "./composed_ad_image.png"
      }
    }
    

Read the full file on GitHub · 274 lines

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. 1mo ago Changed · +1 lines e59317051057
  2. 1mo ago First seen · 273 lines · 64 tokens per session scan A 09d36a7def40

Subscribe to this mod's changes

ad_video_create is a skill published in the GitHub repository inclusionAI/AWorld (1,246 stars, last pushed 3d ago), licensed MIT. It adds 64 tokens to every session and 2,565 once invoked, about $0.0003 per session on Opus 5.5. 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.

Related

Other skills, from other repositories

flowcraft-config

Author, validate, and troubleshoot complete FlowCraft configuration: deployment documents (deploy.yaml with the runtime section, inference/workspace/sandbox/tool sub-documents, core/memory contracts, and graph JSON node wiring), craft.yaml application definitions, and plugin.json manifests. Use when writing or…

GizClaw/flowcraft · 121 tokens

muse-governance

Governance behavior for Muse agents governed by DashClaw. Teaches the governance protocol over REST: when to call guard, how to interpret allow/warn/block/requireapproval, recording actions and outcomes, plan-first execution with preflight approval, and waiting for human review. Trigger on: governed agent, dashclaw…

ucsandman/DashClaw · 88 tokens

hello

What the hello plugin contributes to anvil; handed to agents by the host.

GizClaw/flowcraft · 18 tokens

expense

Read a receipt or invoice — pasted text, or a photo/scan — and turn it into one clean, structured expense record: merchant, date, currency, total, tax id, and itemised amounts, with those amounts checked to actually sum to the stated total. OCR text is dirty (O↔0, l↔1, misplaced decimals); this skill calibrates that…

Caprista/KarvyLoop · 0 tokens

draft-digital-nomad-debunk

Ask the writing assistant to draft an 800-word opening for "Disenchanting the Digital Nomad" following the outline agreed this morning — no fence-sitting, bring the bite I asked for.

Caprista/KarvyLoop · 51 tokens

project-summary

Summarize a Python project for SDK users.

wxhcore/bumblehive · 13 tokens