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 video_script_reviewgit 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/video_script_review)<a href="https://agentmods.dev/skills/inclusionai/aworld/video_script_review"><img src="https://agentmods.dev/badge/skills/inclusionai/aworld/video_script_review/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/video_script_review"><img src="https://agentmods.dev/badge/skills/inclusionai/aworld/video_script_review.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 | $0.00106 | $0.01202 |
| Opus 5 | $0.00053 | $0.00601 |
| Sonnet 5 | $0.00021 | $0.00240 |
| Haiku 4.5 | $0.00011 | $0.00120 |
Grade A, and why
video-storytelling-core-principles 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 9d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
1. The “smoke and fire” rule (make it concrete)
- Reject vague grandeur: Do not build visuals around abstract buzzwords like “system,” “architecture,” “underlying logic,” or “empowerment.”
- Use lived-in detail: Map ideas to warm, everyday scenes—e.g. multi-agent teamwork as tiny kitchen sprites dividing cake work; single-model limits as one person juggling chores until everything breaks.
- Stress texture: Prompts must highlight physical material (flour dust, cream sheen, strawberry color, oven glow) for “food appeal” or satisfying, tactile life moments.
2. The “mute” test
- Picture = story: If you mute narration and dialogue, the audience should still follow setup, turn, and payoff. Voiceover annotates the image—images must not become a slide deck for the VO.
- Visual loop and contrast:
- Problem and solution should read through visual contrast, not explanation-only VO.
- Weak: Scene 1 VO “solo is exhausting,” girl sighs; Scene 4 VO “teamwork is easy,” girl smiles—same vague staging.
- Strong: Scene 1—girl whisks with one hand and struggles to pour flour with the other → flour explosion (solo pain). Scene 4—friend takes whisking; girl can sift flour calmly (team gain). Action contrast closes the loop.
3. Physical logic and action breakdown
- Visible failure and success:
- Conflict and outcome cannot be a vague label—they must split into visible physical causes.
- Weak: “She failed at the cake and got flour on her face.” (Why the face?)
- Strong: “Left hand whisks, right hand strains holding the flour bag, recipe in teeth. A sneeze drops the paper; hands slip; the bag tips and flour blasts upward into her face.” Tight chain, self-consistent motion.
- Visual bridge for the “aha”:
- You cannot jump from “stuck” to “solution” without a seen link.
- If inspiration comes from watching something (e.g. ants), show face change (eyes widen, smile) and physical action (grabs a crayon)—then the next beat (drawing a plan) feels earned.
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.
- 9d ago First seen · 68 lines · 106 tokens per session scan A d746e1a8632c
video-storytelling-core-principles is a skill published in the GitHub repository inclusionAI/AWorld (1,229 stars, last pushed yesterday), licensed MIT. It adds 106 tokens to every session and 1,202 once invoked, about $0.0005 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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