ppt-master
AI turns documents or topics into real, native PowerPoint decks, with native shapes, transitions and animations, data-backed charts and tables on demand, audio narration from speaker notes, and support for your own .pptx templates. · by Hugo He
Runssource: GitHubhomepagePythonMITcommit d3d81fe3cf4c
Python, MIT licensed. The project labels itself: ai agent, aippt, office, powerpoint, powerpoint generation, ppt, pptx and presentation.
ppt-master runs. An Argusic agent installed it in 14 minutes and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Python 3.12 venv installed 78 packages; project_manager.py init, validate, scaffold-lock, scaffold-spec work; svg_quality_checker.py passed 3 SVGs with 0 blocking errors; svg_to_pptx.py produces valid 3-slide 16:9 PPTX with native shapes; pptx_to_svg.py roundtrips PPTX to editable SVG; source_to_md.py converts text files; animation_config.py CLI responds;
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 73.3 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.
- test_native_export_guards.py collection fails under full 'pytest tests/' suite due to a relative-import in svg_to_pptx/animation_config.py that depends on package context — 37 tests pass when the file is run individually
- Install time
- 14 minutes
- Cold machine to finish
- 15 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- How the result was proved
- python3 -c 'from pptx import Presentation; prs = Presentation("/tmp/test_output.pptx"); assert len(prs.slides)==3; assert prs.slide_width==12192000; assert prs.slide_height==6858000' returned no error; pytest tests/ (excluding 1 pre-existing collection issue) ran 175 passed with 350 subtests passed in 16s; svg_to_pptx.py exported a 3-slide PPTX with native DrawingML shapes; pptx_to_svg.py roundtrip produced editable SVG with 3 pages; quick-generate export succeeded; attribution_guard.py passed
- Model tokens used
- 165,932
- Exact commit tested
- d3d81fe3cf4c
- 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.
drwxrwxr-x 6 runner runner 115 Sep 14 07:30 . drwxrwxrwt 1 root root 4096 Sep 14 07:16 .. drwxrwxr-x 3 runner runner 4096 Sep 14 07:21 bin drwxrwxr-x 3 runner runner 32 Sep 14 07:16 include drwxrwxr-x 3 runner runner 32 Sep 14 07:16 lib lrwxrwxrwx 1 runner runner 3 Sep 14 07:16 lib64 -> lib -rw-rw-r-- 1 runner runner 159 Sep 14 07:16 pyvenv.cfg drwxrwxr-x 3 runner runner 29 Sep 14 07:17 share /usr/bin/date 2026-09-14T07:30:38Z tokens used 165,932
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.
- 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 3 times, so the result is not a one-off.
What did not, or is not known
- Took 14 minutes to install, slower than the median of the 11 comparable projects Argusic has measured.
Of the 12 ai-agent projects Argusic has installed and timed, ppt-master was the 8th fastest to reach a running state, and 9 of 12 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.
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)
ai-agentaipptofficepowerpointpowerpoint-generationpptpptxpresentationslideslides
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/ppt-master)Questions
- Does ppt-master run?
- Yes. ppt-master runs. Argusic installed and launched it on a clean machine in 14 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test ppt-master?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit d3d81fe3cf4c. 3 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test ppt-master?
- The run that produced this verdict cost $0.10: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does ppt-master take to install?
- 14 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 ppt-master need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing ppt-master?
- 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 ppt-master compare with the alternatives?
- Of the 12 ai-agent projects Argusic has installed and timed, ppt-master was the 8th fastest to reach a running state, and 9 of 12 reached one at all.
- Where is the evidence for ppt-master?
- All 3 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.