video-use
Edit videos with coding agents
Runssource: GitHubhomepagePythonMITcommit b877063835e6
Python, MIT licensed.
video-use runs. An Argusic agent installed it in 0.5 minutes and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
video-use is installed in a virtualenv, all 26 tests pass, the render pipeline produces valid MP4 from EDL, color grading works with 3 presets, timeline_view generates composite previews, and the skill is registered for Codex.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 96 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- extract_frames fails when end=video duration — ffmpeg produces no frame at the last timeslice0.3 minutes
- Install time
- under a minute
- Cold machine to finish
- 5 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- How the result was proved
- python -m unittest discover tests/ -v → 26 tests, 0 failures, 0 errors (0.011s). render.py --draft with synthetic EDL → valid MP4 (63KB). grade.py --preset warm_cinematic → valid graded MP4 (11KB). timeline_view.py → valid PNG (26KB). Mocked transcribe.py → writes cached transcript JSON. pack_transcripts.py → writes packed MD.
- Model tokens used
- 79,590
- Exact commit tested
- b877063835e6
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
Watch the session
The whole run, from a clean machine, at the speed it actually happened. Nothing is cut and nothing is sped up.
A recording of attempt 1, run . The verdict above comes from a different attempt, so read this as one session and not as the current result. The terminal recording it was made from is the evidence, stored with a sha256 fingerprint; the video is a rendering of it. Nothing loads from the video host until you press play.
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
tokens used 79,590 The result line has been printed. Let me recap everything I did: **Install**: Created a venv (system Python is externally managed), installed all dependencies via p… **Error found & fixed**: `helpers/timeline_view.py` `extract_frames()` — when `end` equals the vide… **Verification**: - All **26 unit tests** pass (0 failures, 0 errors) - `render.py --draft` with a synthetic 2-clip EDL → valid 63KB MP4 - `grade.py --preset warm_cinematic` → valid 11KB graded MP4 - `timeline_view.py` → valid 26KB composite PNG - Mock ElevenLabs Scribe server → `transcribe_one()` writes correct cached transcript JSON - `pack_transcripts.py` → produces packed markdown from mock transcript
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 0.5 minutes, faster than the median of the 46 comparable projects Argusic has measured.
- Recovered from all 1 error without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
- Measured 2 times, so the result is not a one-off.
Of the 47 Python projects Argusic has installed and timed, video-use was the 9th fastest to reach a running state, and 31 of 47 reached one at all.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 0.5 minutes after the clone.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 2/3 | Runs | 100.00 | 0.14 | |
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.04 |
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/video-use)Questions
- Does video-use run?
- Yes. video-use runs. Argusic installed and launched it on a clean machine in 1 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test video-use?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit b877063835e6. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test video-use?
- The run that produced this verdict cost $0.14: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does video-use take to install?
- 0.5 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does video-use need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing video-use?
- 1 thing broke in the recorded run, and 1 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does video-use compare with the alternatives?
- Of the 47 Python projects Argusic has installed and timed, video-use was the 9th fastest to reach a running state, and 31 of 47 reached one at all.
- Where is the evidence for video-use?
- All 2 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.