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/WhiteTowerAI/cut-as-codenpx agentmods add skills/whitetowerai/cut-as-code/video-to-shortsWrote 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/whitetowerai/cut-as-code/video-to-shorts)<a href="https://agentmods.dev/skills/whitetowerai/cut-as-code/video-to-shorts"><img src="https://agentmods.dev/badge/skills/whitetowerai/cut-as-code/video-to-shorts/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/whitetowerai/cut-as-code/video-to-shorts"><img src="https://agentmods.dev/badge/skills/whitetowerai/cut-as-code/video-to-shorts.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 | $0.00037 | $0.04919 |
| Opus 5 | $0.00018 | $0.02459 |
| Sonnet 5 | $0.00007 | $0.00984 |
| Haiku 4.5 | $0.00004 | $0.00492 |
Grade A, and why
video-to-shorts 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 — 543 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video To Shorts
Dependencies
/video-understand is a prerequisite. Run it first so shorts use the validated
source transcript and canonical timeline.
Before starting, verify that it is installed. If it is not, warn the user that
this prerequisite is missing and stop before processing media.
Require ffmpeg/ffprobe on PATH and Python with Pillow. Check them before processing media.
Scope
This skill has two project phases:
- Plan before render: map the understood transcript to program time, author and
approve candidates, and write
work/shorts/shorts-plan.json. This phase writes no final video. - Finalize after render: after every selected main-sequence edit is approved and
the shared main delivery is verified, rerun
plan.pyto bind the same approved ranges to that delivery, then extract horizontal and optional vertical outputs.
It owns short candidate judgment, deterministic qualification, horizontal extraction, short-relative transcripts, and optional deterministic vertical delivery. It does not transcribe in project mode, change the main sequence, add captions/graphics, grade, publish, or perform continuous subject tracking.
Delivered Shorts are derivatives of final/final-video.mp4, but candidate planning
precedes that file. Record the operation in the project DAG, but never add it to
sequences.main.operations; the main delivery and its original-vs-final comparison
remain unchanged.
Project Inputs
work/project.json- the project source recorded by
project.source.path work/understand/transcript.jsonin source timework/timeline.json- active main-operation revisions at each phase
Planning does not require final/final-video.mp4. Finalization and extraction require
that exact project render with render.status: verified.
prepare_transcript.py --timeline calls the shared
projectlib.map_transcript_to_timeline. Candidate/extraction times are therefore
program seconds on the actual final video. Words removed by the main edit are not
available to shorts. Each retained word keeps its source_range, program_range,
and clip_id.
What ships with it
21 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.
- assets/shorts-candidates-review.html 11 KB
- assets/shorts-vertical-review.html 13 KB
- examples/example_shorts_candidates_text_visual.json 1.5 KB
- examples/example_shorts_plan.json 2.6 KB
- examples/example_transcript.json 1.7 KB
- README.md 3.0 KB
- scripts/boundary_refine.py 18 KB runs code
- scripts/build_candidate_review.py 11 KB runs code
- scripts/candidates.py 14 KB runs code
- scripts/check_project_protocol.py 34 KB runs code
- scripts/check_review_ui.py 60 KB runs code
- scripts/extract_shorts.py 21 KB runs code
- scripts/interaction.py 5.0 KB runs code
- scripts/plan.py 24 KB runs code
- scripts/prepare_transcript.py 14 KB runs code
- scripts/prepare_visual_context.py 5.9 KB runs code
- scripts/preview.py 13 KB runs code
- scripts/render_vertical.py 26 KB runs code
- scripts/review_gate.py 63 KB runs code
- scripts/transcript_utils.py 2.5 KB runs code
- scripts/vertical_plan.py 25 KB runs code
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 · 543 lines · 37 tokens per session scan A dfddcce3a77e
video-to-shorts is a skill published in the GitHub repository WhiteTowerAI/cut-as-code (11 stars, last pushed 17d ago), licensed MIT. It adds 37 tokens to every session and 4,919 once invoked, about $0.0002 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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