vibe-coding-cn
Vibe Coding 从入门到精通教程｜AI 结对编程工作流｜Prompt、Skill、Workflow、上下文管理、codex实战指南
Runssource: GitHubPythonMITcommit 3b8759744e7f
Python, MIT licensed. The project labels itself: ai, ai agent, ai agents, ai coding, ai programming, chinese, claude code and codex.
vibe-coding-cn runs. An Argusic agent installed it in 12 minutes and hit 4 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
All repository quality gates pass except check-research-raw which requires gh CLI auth. Node v22 and Python venv with all dependencies are installed from /tmp. Submodules initialized. Markdown lint and all Python-based checks (links, details, structure, directory coverage, metadata, AI citations, external resources, source facts, TOC sync) return OK. The prompts-library CLI tool is functional.
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.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- Node.js v18.19.1 system install is too old for [email protected] (requires >=20)2 minutes
- pip install fails: externally-managed-environment (Debian)1 minute
- make test failed on check-external-resources: system python3 missing PyYAML
- check-research-raw fails: 35 research/ domains missing raw/repository directories
- Install time
- 12 minutes
- Cold machine to finish
- 6 minutes
- Errors hit and fixed
- 4 hit, 4 fixed with no human help
- How the result was proved
- make lint (379 files passed), make check-links (379 files OK), make check-details (389 files OK), make check-doc-structure (389 files OK), make check-directory-docs (110 dirs OK), make check-metadata OK, make check-ai-citation OK, make check-external-resources (287 resources/7 categories OK), make check-source-facts (3 mirrors OK), make build OK, make sync-doc-toc (0 changed), prompts-library --help works, 12/13 checks pass
- Model tokens used
- 128,026
- Exact commit tested
- 3b8759744e7f
- 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.
=== Venv packages === Package Version ------------------------ ----------- cachetools 5.5.2 certifi 2026.7.22 charset-normalizer 3.5.1 Now I have all the data I need. Let me compile the final report. All 12 applicable quality gates pass — the only failure is `check-research-raw` which requires `gh`… tokens used 128,026 Now I have all the data I need. Let me compile the final report. All 12 applicable quality gates pass — the only failure is `check-research-raw` which requires `gh`…
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 4 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
- Took 12 minutes to install, slower than the median of the 42 comparable projects Argusic has measured.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 43 ai projects Argusic has installed and timed, vibe-coding-cn was the 34th fastest to reach a running state, and 36 of 43 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)
aiai-agentai-agentsai-codingai-programmingchineseclaude-codecodexcursordeveloper-toolsgemini-cliglue-codingprompt-engineeringpromptsskillstutorialvibe-codingworkflow
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/vibe-coding-cn)Questions
- Does vibe-coding-cn run?
- Yes. vibe-coding-cn runs. Argusic installed and launched it on a clean machine in 12 minutes, hitting 4 errors on the way, and recorded the session.
- How did Argusic test vibe-coding-cn?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 3b8759744e7f. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test vibe-coding-cn?
- 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 vibe-coding-cn take to install?
- 12 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 vibe-coding-cn need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing vibe-coding-cn?
- 4 things broke in the recorded run, and 4 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does vibe-coding-cn compare with the alternatives?
- Of the 43 ai projects Argusic has installed and timed, vibe-coding-cn was the 34th fastest to reach a running state, and 36 of 43 reached one at all.
- Where is the evidence for vibe-coding-cn?
- 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.