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 avenoxai/avenoxskills --skill avenox-videogit clone --depth 1 https://github.com/avenoxai/avenoxskillsWrote 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/avenoxai/avenoxskills/avenox-video)<a href="https://agentmods.dev/skills/avenoxai/avenoxskills/avenox-video"><img src="https://agentmods.dev/badge/skills/avenoxai/avenoxskills/avenox-video/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/avenoxai/avenoxskills/avenox-video"><img src="https://agentmods.dev/badge/skills/avenoxai/avenoxskills/avenox-video.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 | $0.00135 | $0.01394 |
| Opus 5 | $0.00068 | $0.00697 |
| Sonnet 5 | $0.00027 | $0.00279 |
| Haiku 4.5 | $0.00014 | $0.00139 |
Grade A, and why
avenox-video scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Avenox Studio — operator router
The fast operator guide for a local-first, agent-operated video pipeline. Everything runs on your own machine: no cloud editor, no upload-to-render.
The human directs and approves quality; the agent runs the pipeline.
Setup
export STUDIO_JOBS="$HOME/video/projects" # heavy media lives here
export STUDIO_ROOT="/path/to/this/repo" # scripts, templates, brand
Requirements: macOS (hardware encode via h264_videotoolbox; Apple Silicon for
mlx-whisper), ffmpeg, python3, melt/MLT, Node (for HyperFrames). Most
of this works on Linux with libx264 and a CUDA whisper build substituted in.
Operating principles
- Media discipline. Heavy media NEVER in a cloud-synced folder — sync will
thrash on multi-GB intermediates and can corrupt in-flight writes. Jobs live
in
$STUDIO_JOBS/<job>/(raw/ cut/ graphics/ audio/ outputs/). Your notes system holds only the brain: this system, the brand spec,edit.jsonplans. - Director loop. Produce a preview (graphics stills + a fast draft render) → send for notes → only then final render. Never ship a final without sign-off. This is the single most important rule; an agent that renders finals unreviewed will burn hours on a rejected cut.
- Brand is a hard constraint, not a suggestion. Read
brand/frame.mdbefore making any graphic. Define it once and lock it. (The reference implementation is deliberately anti-"AI slop": premium editorial, warm paper- ink + a single accent, no neon/gradient/glassmorphism/3D-gloss.)
- Format: YouTube 16:9 1080p60. Preset in
brand/presets/youtube-16x9.json. - Finishing is hybrid. Auto-generate the draft; the same
.mltopens in Kdenlive or Shotcut for hand-finishing. Don't try to automate taste. - Transcription defaults to LOCAL
mlx-whisperwithwhisper-large-v3-turbo— fast, free, and strong on non-English audio. Note that most LLM-routing proxies expose no whisper endpoint; if you go remote, use a dedicated speech API.
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 · 98 lines · 135 tokens per session scan A d6d6b5e04a7e
avenox-video is a skill published in the GitHub repository avenoxai/avenoxskills (49 stars, last pushed 1mo ago), licensed MIT. It adds 135 tokens to every session and 1,394 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 2026-08-30.
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