Does mcd-saver-strategist run?

Yes. mcd-saver-strategist runs. 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 mcd-saver-strategist?

On a fresh, disposable machine, with every command recorded and the repository pinned at commit 6e2c462c1fd1. One attempt is recorded, and the full method is on the methodology page.

What did it cost to test mcd-saver-strategist?

The run that produced this verdict cost $0.00: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.

How long does mcd-saver-strategist take to install?

0.5 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 mcd-saver-strategist need a GPU?

Not to start. Argusic reached a running state on a machine with no GPU.

What goes wrong when installing mcd-saver-strategist?

Nothing did, in the recorded run: zero errors between clone and running.

Where is the evidence for mcd-saver-strategist?

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.

mcd-saver-strategist

麦当劳省钱点餐助手（麦门精算师）｜麦当劳优惠券组合优化 Skill：拉取麦当劳菜单与全部可用优惠券，算出最省的点餐组合，巨无霸套餐 ¥47 → ¥21.9（省 53%）。McDonalds MCP（麦当劳 MCP）coupon optimizer · McDonalds China MCP Server · Agent Skill for WorkBuddy · 麦当劳程序员创意开发大赛参赛作品

Runssource: GitHub homepagePythonMITcommit 6e2c462c1fd1

Python, MIT licensed. The project labels itself: agent skill, ai agent, coupon optimizer, coupons, deterministic optimization, fast food, mcd and mcdonalds.

mcd-saver-strategist runs. An Argusic agent installed it in 0.5 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.

All 4 Python scripts (optimizer, selftest, demo, bench) and the Node.js parity check run successfully. 46 self-tests pass. Demo shows 7 scenarios working end-to-end with sample data.
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

verdict: Runs
Argusic Score: 100 / 100
cost of the verifying run: $0.00 (measured)
recorded runs: 1
last tested: 2026-10-27
stars: 1,526
forks: 87
open issues: 2
watchers: 2
size: 8 MB
created: 2026-10-09
last push: 2026-10-19

Subject data from GitHub, linked at the top of this page, refreshed 2026-10-27. 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: 1 minutes
Errors hit and fixed: 0 hit, 0 fixed with no human help
How the result was proved: python3 selftest.py exited 0, 46 passed, 0 failed; python3 optimizer.py output correct results; node parity_check.mjs 17/17 passed
Model tokens used: 20,013
Exact commit tested: 6e2c462c1fd1
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.

20,013
All scripts pass flawlessly with zero errors. Here's the summary:
- **Python 3.12** is available (no `python` symlink, but `python3` works)
- **No dependencies to install** — the project uses pure Python standard library as advertised
- **All 4 core scripts** run cleanly on first try:
  - `optimizer.py` — optimization example output
  - `selftest.py` — **46 passed, 0 failed**
  - `demo.py` — 7 scenarios, all work
  - `bench.py` — benchmark runs (<130ms for 160 items)
- **Node.js parity check** — `parity_check.mjs` passes all 17 assertions
- **`mcp_probe.py`** correctly requires a real MCD MCP token (expected — no mock needed since the o…
- **No errors occurred** — everything worked out of the box

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 0.5 minutes, faster than the median of the 13 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 14 agent-skill projects Argusic has installed and timed, mcd-saver-strategist was the 3rd fastest to reach a running state, and 11 of 14 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 0.5 minutes after the clone.
Following its own README: the documented path worked with nothing to fix.

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	100.00	0.00	2026-10-27 08:02 UTC

Topics (from GitHub)

agent-skill ai-agent coupon-optimizer coupons deterministic-optimization fast-food mcd mcdonalds mcdonalds-mcp mcp mcp-server money-saving python workbuddy workbuddy-skill

Embed the badge

Markdown for the project README. It links back here; terms on the terms page.

[![Tested by Argusic](https://argusic.com/badge/mcd-saver-strategist.svg)](https://argusic.com/subject/mcd-saver-strategist)

Questions

Does mcd-saver-strategist run?: Yes. mcd-saver-strategist runs. 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 mcd-saver-strategist?: On a fresh, disposable machine, with every command recorded and the repository pinned at commit 6e2c462c1fd1. One attempt is recorded, and the full method is on the methodology page.
What did it cost to test mcd-saver-strategist?: The run that produced this verdict cost $0.00: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
How long does mcd-saver-strategist take to install?: 0.5 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 mcd-saver-strategist need a GPU?: Not to start. Argusic reached a running state on a machine with no GPU.
What goes wrong when installing mcd-saver-strategist?: Nothing did, in the recorded run: zero errors between clone and running.
How does mcd-saver-strategist compare with the alternatives?: Of the 14 agent-skill projects Argusic has installed and timed, mcd-saver-strategist was the 3rd fastest to reach a running state, and 11 of 14 reached one at all.
Where is the evidence for mcd-saver-strategist?: 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.