video-use
Edit videos with coding agents
Runs with mockssource: GitHubPythonMITcommit b877063835e6
Python, MIT licensed.
video-use runs, with stand-ins for the services it depends on. An Argusic agent installed it in 8 minutes and hit 3 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
26 unit tests pass, all 6 helpers (transcribe, transcribe_batch, pack_transcripts, grade, render, timeline_view) parse and run correctly on test media, and the grade/EDL-render pipeline produces valid MP4 output with ffprobe-confirmed duration and clean null-frame decode.
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 92 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.
- timeline_view.py extract_frames fails when frame timestamp >= video duration2 minutes
- pip install -e . failed: externally-managed-environment (Debian PEP 668)1 minute
- pytest module not found1 minute
- Install time
- 8 minutes
- Cold machine to finish
- 8 minutes
- Errors hit and fixed
- 3 hit, 3 fixed with no human help
- How the result was proved
- pytest: 26 passed, 15 subtests passed; render.py EDL pipeline produced final.mp4 (ffmpeg null decode clean, exit 0); grade.py applied warm_cinematic to 3s test clip; timeline_view.py generated 249 KB composite PNG; transcribe.py + mock server produced valid word-level transcript JSON; pack_transcripts.py consumed mock transcript and produced takes_packed.md
- Model tokens used
- 191,167
- 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
- Behaviour against real services
- this run was verified against stand-in services only
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
pack_transcripts output: # Packed transcripts Phrase-level, grouped on silences ≥ 0.5s or speaker change. Use `[start-end]` ranges to address cuts in the EDL. ## test_source (duration: 2.1s, 1 phrases) [000.04-002.10] SS0 Hello world this is a test exec /bin/sh -lc '# Clean up any leftover mock processes pkill -f mock_elevenlabs 2>/dev/null; true' in /work/repo exited 143 in 0ms: tokens used 191,167
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
- Recovered from all 3 errors without a human stepping in, which says the failures are documented well enough to solve.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
- Took 8 minutes to install, slower than the median of the 4 comparable projects Argusic has measured.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 5 Python projects Argusic has installed and timed, video-use was the 4th fastest to reach a running state, and 3 of 5 reached one at all.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| 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?
- video-use runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 8 minutes, hitting 3 errors 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. One attempt is 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 $0.04: 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?
- 8 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?
- 3 things broke in the recorded run, and 3 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 5 Python projects Argusic has installed and timed, video-use was the 4th fastest to reach a running state, and 3 of 5 reached one at all.
- Where is the evidence for video-use?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.