asd-ste100-skill
ASD-STE100 Simplified Technical English rules, repurposed as a Claude Code skill for rewriting ambiguous agent-facing English.
Runssource: GitHubPythonMITcommit 32511c6992ec
Python, MIT licensed.
asd-ste100-skill runs. An Argusic agent installed it in 0 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
ste-lint.py runs on Python 3.12.3 with no dependencies, exits 0 under --selftest, flags 49 structural STE violations in SKILL.md with 41 hard violations matching the documented baseline, and correctly reports 2 dangling-conjunction findings in linter-edge-cases.md
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. 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
- under a minute
- Cold machine to finish
- under a minute
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- python3 scripts/ste-lint.py --selftest exited 0; python3 scripts/ste-lint.py examples/linter-edge-cases.md reported exactly 2 hard violations as the README promises; python3 scripts/ste-lint.py --baseline 41 SKILL.md exited 0 with 49 total violations (41 hard) matching the advertised baseline; stdin piping, --json output, and multi-file modes all work correctly
- Model tokens used
- 16,459
- Exact commit tested
- 32511c6992ec
- 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.
exec
/bin/sh -lc 'cd /work/repo && python3 --version' in /work/repo
succeeded in 0ms:
Python 3.12.3
exec
/bin/sh -lc "cd /work/repo && python3 -c \"import re, json, sys; print('stdlib modules OK')\"" in /…
succeeded in 0ms:
stdlib modules OK
Everything works with zero installation and zero external dependencies. Here's the report:
tokens used
16,459
Everything works with zero installation and zero external dependencies. Here's the 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 0 minutes, faster than the median of the 32 comparable projects Argusic has measured.
- Nothing broke on the way: zero errors between clone and running.
- 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.
Of the 33 Python projects Argusic has installed and timed, asd-ste100-skill was the 1st fastest to reach a running state, and 18 of 33 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 minutes after the clone.
- Following its own README: the documented path worked with nothing to fix.
Run history
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/asd-ste100-skill)Questions
- Does asd-ste100-skill run?
- Yes. asd-ste100-skill runs. Argusic installed and launched it on a clean machine in 0 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test asd-ste100-skill?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 32511c6992ec. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test asd-ste100-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 asd-ste100-skill take to install?
- 0 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 asd-ste100-skill need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing asd-ste100-skill?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does asd-ste100-skill compare with the alternatives?
- Of the 33 Python projects Argusic has installed and timed, asd-ste100-skill was the 1st fastest to reach a running state, and 18 of 33 reached one at all.
- Where is the evidence for asd-ste100-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.