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.
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 inclusionAI/AWorld --skill ad_video_create_skillgit clone --depth 1 https://github.com/inclusionAI/AWorldWrote 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/inclusionai/aworld/ad_video_create_skill)<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.
<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>- 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 | $0.00064 | $0.02560 |
| Opus 5 | $0.00032 | $0.01280 |
| Sonnet 5 | $0.00013 | $0.00512 |
| Haiku 4.5 | $0.00006 | $0.00256 |
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 10d 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 — 273 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:
- Asset Discovery: Scan working directory for available assets
- Media Comprehension:
- Activate
media_comprehensionskill - 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)
- Activate
Phase 2: Character Generation (Conditional)
Trigger Condition: No character/subject image provided
Process:
- 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
- Call
image_generatorwith 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:
- Prepare input images:
- Product image (original or compressed if >50KB)
- Character image (from Phase 2 or user-provided)
- Call
image_generatorwith 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" } }
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.
- 10d ago First seen · 273 lines · 64 tokens per session scan A 09d36a7def40
ad_video_create is a skill published in the GitHub repository inclusionAI/AWorld (1,230 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 2,560 once invoked, about $0.0003 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.
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