kk-mix
Windows 批量视频混剪工具，15 项批处理功能 | A Windows batch video editing toolkit, 15 batch operations.
Runssource: GitHubPythonMITcommit 01077fabc17e
Python, MIT licensed. The project labels itself: batch processing, desktop app, ffmpeg, fluent design, pyside6, python, qt and video editing.
kk-mix runs. An Argusic agent installed it in 2 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
kk-mix v10.3.0 installed with Python 3.12 in uv virtualenv, GUI launches with offscreen/vnc platform, i18n check passes, 5 worker types (split/compress/rotate/volume/concat) produce valid FFmpeg output verified via 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 100 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.
- libxcb-cursor0 system library not found — Qt xcb platform plugin cannot load (xcb-cursor0 missing)
- Cython compile of _license_core.pyx fails — Python.h not found (python3-dev headers missing, no root)
- Install time
- 2 minutes
- Cold machine to finish
- 6 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- uv sync installed 27 deps; uv run python kk_qt.py with QT_QPA_PLATFORM=vnc stayed up 8s (QVncServer created on port 5900, exit 124 = timeout). uv run python tools/check_i18n.py: 369 keys OK, no untranslated strings. 5 worker types exercised with real FFmpeg, split (4 segments), compress (2 videos at 1M), rotate (2 clockwise), volume (2 at 0.5x), concat (2-folder fade), all 12 output .mp4 files verified valid via 'ffmpeg -v error -i FILE -f null -' returning zero errors.
- Model tokens used
- 191,300
- Exact commit tested
- 01077fabc17e
- 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
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
/tmp/output_volume/volume_001_0.5x_clip1.mp4: VALID /tmp/output_volume/volume_002_0.5x_clip2.mp4: VALID === Worker test results === 4 2 2 2 1 All done. Here is my report: tokens used 191,300 All done. Here is my report:
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 2 minutes.
- Recovered from all 2 errors 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.
What did not, or is not known
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
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 2 minutes after the clone.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
Topics (from GitHub)
batch-processingdesktop-appffmpegfluent-designpyside6pythonqtvideo-editingvideo-processingwindows
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/kk-mix)Questions
- Does kk-mix run?
- Yes. kk-mix runs. Argusic installed and launched it on a clean machine in 2 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test kk-mix?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 01077fabc17e. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test kk-mix?
- The run that produced this verdict cost $0.03: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does kk-mix take to install?
- 2 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 kk-mix need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing kk-mix?
- 2 things broke in the recorded run, and 2 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for kk-mix?
- 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.