AI-Engineering-Coach
better agentic engineering
Runssource: GitHubTypeScriptMITcommit 74eb09ca7107
TypeScript, MIT licensed.
AI-Engineering-Coach runs. An Argusic agent installed it in 8 minutes and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Full CI gate passes: typecheck, lint (0 errors), spellcheck, knip, lockfile-lint, all 1348 tests in 70 files pass, bundle within 2MB budget, and the extension packages to a 3.49 MB .vsix
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.
- npm test fails: 7 tests in github-app-analytics.test.ts fail with 'status' expected 'ready' got 'unavailable' because the production default query() calls the missing sqlite3 CLI binary, but the test creates databases via Node's built-in DatabaseSync8 minutes
- Install time
- 8 minutes
- Cold machine to finish
- 8 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- How the result was proved
- npm run check (typecheck + lint + spellcheck + knip + lockfile-lint + test) exited 0; 70 files / 1348 tests passed; npm run check-size passed; npm run package built .vsix successfully
- Model tokens used
- 53,346
- Exact commit tested
- 74eb09ca7107
- 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.
/bin/sh -lc 'echo "Start: $(date -d @$(stat -c '"'%Y' /work/repo/package.json) -u +%s)\"; echo \"No… succeeded in 0ms: Start: 1790936140 Now: 1790936622 exec /bin/sh -lc 'echo $(( $(date -u +%s) - 1790936140 ))' in /work/repo succeeded in 0ms: 486 All done. Here's the result: tokens used 53,346 All done. Here's the result:
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.
What did not, or is not known
- Took 8 minutes to install, slower than the median of the 22 comparable projects Argusic has measured.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 23 TypeScript projects Argusic has installed and timed, AI-Engineering-Coach was the 13th fastest to reach a running state, and 18 of 23 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 8 minutes after the clone.
Run history
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/ai-engineering-coach)Questions
- Does AI-Engineering-Coach run?
- Yes. AI-Engineering-Coach runs. Argusic installed and launched it on a clean machine in 8 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test AI-Engineering-Coach?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 74eb09ca7107. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test AI-Engineering-Coach?
- 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 AI-Engineering-Coach 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 AI-Engineering-Coach need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing AI-Engineering-Coach?
- 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 AI-Engineering-Coach compare with the alternatives?
- Of the 23 TypeScript projects Argusic has installed and timed, AI-Engineering-Coach was the 13th fastest to reach a running state, and 18 of 23 reached one at all.
- Where is the evidence for AI-Engineering-Coach?
- 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.