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 embedded_videogit 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/embedded_video)<a href="https://agentmods.dev/skills/inclusionai/aworld/embedded_video"><img src="https://agentmods.dev/badge/skills/inclusionai/aworld/embedded_video.svg" alt="Measured on agentmods" 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 | $0.00113 | $0.00840 |
| Opus 5 | $0.00056 | $0.00420 |
| Sonnet 5 | $0.00023 | $0.00168 |
| Haiku 4.5 | $0.00011 | $0.00084 |
Grade A, and why
embedded-video-pip-smooth-playback 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
1. Problem scenario
When you build videos with code-driven renderers (e.g. Remotion, AE scripts, complex FFmpeg filter graphs), you often need picture-in-picture: one main composition with another video embedded inside it.
Typical symptom: In the exported file, motion on the main layer (translation, scale, etc.) looks smooth, but the embedded clip stutters badly, drops frames, or even freezes for long stretches.
2. Root cause: sparse keyframes
Modern codecs (H.264/H.265) save space by storing full pictures only at scene cuts or every few seconds (keyframes / I-frames). Frames in between (P-frames / B-frames) only store differences from neighbors.
Engines like Remotion export frame by frame. To render frame N, the embedded clip must seek to the matching timestamp.
If the embedded file has almost no keyframes (e.g. one I-frame at the start of a 10 s clip), the decoder often has to decode from frame 0 forward to reach frame N. That leads to:
- Very slow seeks: Decoding takes so long that the renderer times out and grabs a frame before the decode finishes.
- Repeated frames: The decoder cannot keep up, so several consecutive captures show the same old image—stutter in the final output.
3. Fix: all-intra encoding (every frame a keyframe)
Idea: Re-encode the embedded asset so every frame is a keyframe. Then any seek returns a full picture immediately, with no long chains of dependent frames.
Steps
Step 1: Re-encode with FFmpeg
Run:
ffmpeg -i input.mp4 -c:v libx264 -g 1 -pix_fmt yuv420p output_keyframes.mp4
Parameters:
| Flag | Meaning |
|---|---|
-i input.mp4 |
Source clip you embed. |
-c:v libx264 |
H.264 for broad compatibility with web and renderers. |
-g 1 |
Critical: GOP size 1 → one keyframe per frame. |
-pix_fmt yuv420p |
Common 8-bit 4:2:0 layout for players and pipelines. |
output_keyframes.mp4 |
Output used as the fixed asset. |
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 · 70 lines · 113 tokens per session scan A 6b0d947d45d6
embedded-video-pip-smooth-playback is a skill published in the GitHub repository inclusionAI/AWorld (1,229 stars, last pushed yesterday), licensed MIT. It adds 113 tokens to every session and 840 once invoked, about $0.0006 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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