social-media-skills
Runs with mockssource: GitHubPythonMITcommit 8cefb5b6d037
Python, MIT licensed.
social-media-skills runs, with stand-ins for the services it depends on. 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.
17 skill folders each with a valid SKILL.md pass YAML frontmatter checks, name-folder matching, and description rules; Codex CLI discovers all 17 from .agents/skills/ via the app-server; the README copy-loop installs all 17 into a fresh project with no errors; no external provider access, visual rendering, or user activation was tested.
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 56 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
- under a minute
- Cold machine to finish
- 5 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- python3 tests/test-codex-portability.py --codex passed (live Codex app-server discovered all 17 skills via skills/list); bash validate-skills.sh exited 0 (17 skills, 87 structural checks all passed); README copy-loop installed 17 skills in a fresh project; marketplace.json is valid JSON
- Model tokens used
- 54,710
- Exact commit tested
- 8cefb5b6d037
- 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.
<defs> ... </g> </svg> exec /bin/sh -lc 'echo "validate-skills.sh exit code: $(cd /work/repo && bash validate-skills.sh > /dev/… succeeded in 77ms: validate-skills.sh exit code: 0 All checks are done. Here's the summary: tokens used 54,710 All checks are done. Here's the summary:
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 0.5 minutes, faster than the median of the 30 comparable projects Argusic has measured.
- Nothing broke on the way: zero errors between clone and running.
- Licensed MIT, as reported by its host.
- Measured 2 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 31 Python projects Argusic has installed and timed, social-media-skills was the 4th fastest to reach a running state, and 16 of 31 reached one at all.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 2/3 | Runs with mocks | 92.00 | 0.02 | |
| Argusic Runner | 1/3 | Did not run | 20.00 | 0.16 |
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/social-media-skills)Questions
- Does social-media-skills run?
- social-media-skills runs, with mocks standing in for external services. 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 social-media-skills?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 8cefb5b6d037. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test social-media-skills?
- The run that produced this verdict cost $0.02: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does social-media-skills 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 social-media-skills need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing social-media-skills?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does social-media-skills compare with the alternatives?
- Of the 31 Python projects Argusic has installed and timed, social-media-skills was the 4th fastest to reach a running state, and 16 of 31 reached one at all.
- Where is the evidence for social-media-skills?
- All 2 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.