cheat-on-content
You're reading this. The skill predicted it. A workflow that turns every post into a calibrated experiment, score, blind-predict, retro, evolve. The future doesn't reward effort, it rewards those who see the pattern first. 1M followers in a month, not luck, system.
Runs with mockssource: GitHubPythonMITcommit d6b737bf79c1
Python, MIT licensed.
cheat-on-content runs, with stand-ins for the services it depends on. An Argusic agent installed it in 4.2 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
Install via bash install.sh --codex symlinks 16 skills into ~/.codex/skills/; diff_pct.py computes string similarity using difflib fallback; 3 core tool unit test suites pass with 13 total tests; all hook scripts parse correctly; --reinstall-hooks copies hook scripts into user projects; uninstall/install cycle is clean.
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.
- Install time
- 4 minutes
- Cold machine to finish
- 5 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- bash tools/diff_pct_test.sh exited 0, 3/3 passed (difflib fallback); python3 tools/score-curve.py emitted CSV with 3 predictions found and 3 with retro data; python3 adapters/perf-data/linkedin-session/test_extract.py 7/7 passed; python3 -m unittest adapters/perf-data/wechat-channels/test_adapter.py 3/3 passed; bash adapters/perf-data/wechat-channels/test_run_contract.sh passed; all 3 hook scripts pass bash -n syntax check; bash install.sh --reinstall-hooks copied hooks successfully
- Model tokens used
- 45,659
- Exact commit tested
- d6b737bf79c1
- 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.
Starting: 1790612400 Ending: 1790612649 exec /bin/sh -lc 'echo "scale=2; (1790612649 - 1790612400) / 60" | bc' in /work/repo exited 127 in 0ms: /bin/sh: 1: bc: not found exec /bin/sh -lc 'python3 -c "print((1790612649 - 1790612400) / 60)"' in /work/repo succeeded in 0ms: 4.15 tokens used 45,659
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 4.2 minutes, faster than the median of the 16 comparable projects Argusic has measured.
- Nothing broke on the way: zero errors between clone and running.
- Licensed MIT, as reported by its host.
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.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 17 Python projects Argusic has installed and timed, cheat-on-content was the 8th fastest to reach a running state, and 9 of 17 reached one at all.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.03 |
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/cheat-on-content)Questions
- Does cheat-on-content run?
- cheat-on-content runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 4 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test cheat-on-content?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit d6b737bf79c1. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test cheat-on-content?
- 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 cheat-on-content take to install?
- 4.2 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 cheat-on-content need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing cheat-on-content?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does cheat-on-content compare with the alternatives?
- Of the 17 Python projects Argusic has installed and timed, cheat-on-content was the 8th fastest to reach a running state, and 9 of 17 reached one at all.
- Where is the evidence for cheat-on-content?
- 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.