codex-ppt-skill
GPT-Image-2 PPT Generator Skill for Creating Image-Based PowerPoint Presentations in Codex and Other Skill-Compatible Agents
Runs with mockssource: GitHubhomepagePythonMITcommit 445b1780c85c
Python, MIT licensed. The project labels itself: agent skills, ai ppt, claude code, codex, gpt image 2, powerpoint, ppt and ppt skill.
codex-ppt-skill runs, with stand-ins for the services it depends on. An Argusic agent installed it in 1.2 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
Dependencies install cleanly, all 9 image-model unit tests pass, the PPT assembly script produces a valid .pptx with correctly numbered slides and speaker notes, the image generation CLI dry-runs produce valid request payloads, and the shared runtime bootstrap completes without error.
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.
- Install time
- 1 minutes
- Cold machine to finish
- 2 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- .venv/bin/python -m unittest tests/test_image_gen_models.py -v exited 0, 9 passed; .venv/bin/python -c ... assemble_ppt create_presentation returned True, 3 slides in output PPTX; image_gen.py --dry-run output correct JSON payload
- Model tokens used
- 43,510
- Exact commit tested
- 445b1780c85c
- 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.
- **Verified provider loader**: `image_providers.create_image_provider` loaded without import error… No errors were hit. The project's core scripts (image model validation, PPT assembly, runtime manag… tokens used 43,510 All tests pass and the scripts work correctly. Let me assemble the final report. Work done: - **Installed dependencies**: Created a virtual environment at `/work/repo/.venv` and installed `py… - **Ran tests**: `unittest tests/test_image_gen_models.py` — all 9 tests passed. - **Verified PPT assembly**: Created a temp directory with 3 dummy PNG slides and a `speech.md`, ra… - **Verified image gen CLI**: `image_gen.py generate --prompt "..." --dry-run` printed correct defa… - **Verified provider loader**: `image_providers.create_image_provider` loaded without import error… No errors were hit. The project's core scripts (image model validation, PPT assembly, runtime manag…
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
- Installed in 1.2 minutes, faster than the median of the 18 comparable projects Argusic has measured.
- Nothing broke on the way: zero errors between clone and running.
- 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.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 19 agent-skills projects Argusic has installed and timed, codex-ppt-skill was the 6th fastest to reach a running state, and 14 of 19 reached one at all.
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
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.01 |
Topics (from GitHub)
agent-skillsai-pptclaude-codecodexgpt-image-2powerpointpptppt-skillpptxpresentationskill
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/codex-ppt-skill)Questions
- Does codex-ppt-skill run?
- codex-ppt-skill runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 1 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test codex-ppt-skill?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 445b1780c85c. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test codex-ppt-skill?
- The run that produced this verdict cost $0.01: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does codex-ppt-skill take to install?
- 1.2 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 codex-ppt-skill need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing codex-ppt-skill?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does codex-ppt-skill compare with the alternatives?
- Of the 19 agent-skills projects Argusic has installed and timed, codex-ppt-skill was the 6th fastest to reach a running state, and 14 of 19 reached one at all.
- Where is the evidence for codex-ppt-skill?
- 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.