Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Teamnpx agentmods add skills/aaaaqwq/agi-super-team/av-sync-workflowWrote 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/aaaaqwq/agi-super-team/av-sync-workflow)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/av-sync-workflow"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/av-sync-workflow.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 | $0.00141 | $0.01611 |
| Opus 5 | $0.00071 | $0.00805 |
| Sonnet 5 | $0.00028 | $0.00322 |
| Haiku 4.5 | $0.00014 | $0.00161 |
Grade A, and why
av-sync-workflow 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 3d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AV-Sync Workflow
Transform audio into a professionally edited video synchronized to beats, mood, and scene.
Workflow Overview
Audio → Analysis → Clip Matching → Beat Sync → Video Assembly → Export
Step 1: Analyze Audio
Use scripts/audio_analysis.py to extract:
- Beats/BPM: Timestamp of each beat, overall tempo (BPM)
- Sections: Verse, chorus, bridge, outro markers
- Emotion/Mood: Energy level, valence (happy/sad), tempo category
- Key moments: High-impact points (drops, climaxes, transitions)
python3 scripts/audio_analysis.py /path/to/song.mp3 --output /tmp/analysis.json
Output structure:
{
"bpm": 120,
"duration": 214,
"beats": [0.0, 0.5, 1.0, ...],
"sections": [
{"type": "intro", "start": 0, "end": 15},
{"type": "verse", "start": 15, "end": 45},
{"type": "chorus", "start": 45, "end": 75}
],
"mood": {"energy": 0.7, "valence": 0.6, "danceability": 0.8},
"key_moments": [
{"time": 45.0, "type": "chorus_drop", "intensity": 1.0}
]
}
Step 2: Gather Video Clips
User provides video clips OR search for stock footage:
Stock footage sources:
- Pexels:
https://www.pexels.com/search/videos/{query}/ - Pixabay:
https://pixabay.com/videos/search/{query}/ - Coverr:
https://coverr.co/search/{query}
Download stock video:
# Via yt-dlp (for pexels/pixabay)
yt-dlp -f "best[height<=1080]" -o "/tmp/clip_%(id)s.%(ext)s" "https://pexels.com/video/12345"
# Via direct URL
ffmpeg -i "https://example.com/video.mp4" -c copy /tmp/clip.mp4
Step 3: Analyze Each Clip
For each clip, extract:
- Scene type (indoor/outdoor, city/nature, close-up/wide)
- Mood/style (energetic/calm, happy/sad)
- Duration and cut points
- Visual elements (faces, motion, colors)
python3 scripts/video_analysis.py /tmp/clip.mp4 --output /tmp/clip_analysis.json
Step 4: Match Clips to Audio Sections
Algorithm: Map clips to audio sections based on:
- Emotion matching: High-energy chorus → energetic clips
- Scene continuity: Smooth transitions between scenes
- Beat alignment: Cut on beats for rhythm
- Length fit: Clip duration matches section duration
What ships with it
3 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.
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.
- 3d ago First seen · 186 lines · 141 tokens per session scan A b5cc563616b9
av-sync-workflow is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed today), licensed MIT. It adds 141 tokens to every session and 1,611 once invoked, about $0.0007 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 2026-09-05.
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