ai-goofish-monitor
基于 Playwright 和AI实现的闲鱼多任务实时/定时监控与智能分析系统，配备了功能完善的后台管理UI。帮助用户从闲鱼海量商品中，找到心仪产品。
Runs with mockssource: GitHubPythonMITcommit f85d140b6b45
Python, MIT licensed. The project labels itself: ai, ai assistant, ai tools, automation, gemini, goofish, open source and openai.
ai-goofish-monitor runs, with stand-ins for the services it depends on. An Argusic agent installed it in 4 minutes and hit 4 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Python backend with 117 passing tests and API server running on port 8000; frontend Vue build blocked by Node 18.19.1 < 20.19 requirement.
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.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- test_save_to_jsonl strict equality failed because result_storage_service decorates records with metadata fields
- test_failure_guard_auto_recovers_on_cookie_change failed due to filesystem mtime race on tmpfs
- test_frontend_build_output_path_is_consistent_across_configs failed because .dockerignore contained web-ui/dist
- Frontend build failed: Node 18.19.1 is too old for Vite 7.x (requires Node 20.19+)
- Install time
- 4 minutes
- Cold machine to finish
- 7 minutes
- Errors hit and fixed
- 4 hit, 4 fixed with no human help
- How the result was proved
- pytest: 117 passed, 3 skipped. uvicorn: /docs returns 200, /auth/status returns authenticated properly.
- Model tokens used
- 277,065
- Exact commit tested
- f85d140b6b45
- 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.
| **Unit tests** | 85/85 passed |
| **Integration tests** | 29/29 passed |
| **Standalone tests** | 3/3 passed |
| **Total test suite** | **117 passed, 3 skipped (live-only)** |
| **App launch** | uvicorn started, `/docs` → 200, `/auth/status` → 200 `{"authenticated":true}` |
| **Frontend build** | **Failed** — Node 18.19.1 is too old for Vite 7.x (requires ≥20.19) |
## Verify command
```bash
cd /work/repo && . venv/bin/activate && PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 python -m pytest
# → 117 passed, 3 skipped
```
The 3 skipped tests are live smoke tests (`tests/live/test_live_smoke.py`) marked with `@pytest.mar…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 4 minutes, faster than the median of the 36 comparable projects Argusic has measured.
- Recovered from all 4 errors without a human stepping in, which says the failures are documented well enough to solve.
- 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
- Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
Of the 37 ai projects Argusic has installed and timed, ai-goofish-monitor was the 16th fastest to reach a running state, and 30 of 37 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 | 3/3 | Runs with mocks | 92.00 | 0.04 | |
| Argusic Runner | 2/3 | Runs with mocks | 92.00 | 0.06 | |
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.08 |
Topics (from GitHub)
aiai-assistantai-toolsautomationgeminigoofishopen-sourceopenaiplaywrighttoolxian-yuxianyuxianyu-bot
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/ai-goofish-monitor)Questions
- Does ai-goofish-monitor run?
- ai-goofish-monitor runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 4 minutes, hitting 4 errors on the way, and recorded the session.
- How did Argusic test ai-goofish-monitor?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit f85d140b6b45. 3 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test ai-goofish-monitor?
- 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-goofish-monitor take to install?
- 4 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-goofish-monitor need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing ai-goofish-monitor?
- 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 ai-goofish-monitor compare with the alternatives?
- Of the 37 ai projects Argusic has installed and timed, ai-goofish-monitor was the 16th fastest to reach a running state, and 30 of 37 reached one at all.
- Where is the evidence for ai-goofish-monitor?
- 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.