linkedin-skills
Claude skills for LinkedIn. 11 Claude Code and Codex skills that write human-sounding LinkedIn posts, craft comments that get noticed, analyze your feed, and build a publishing cadence, all from your terminal. Content engineering by Creative Content Crafts. MIT.
Runs with mockssource: GitHubhomepagePythonMITcommit 14d332b9e91e
Python, MIT licensed. The project labels itself: agent skill, agent skills, ai agents, ai content, ai marketing, anthropic, awesome claude and claude code.
linkedin-skills runs, with stand-ins for the services it depends on. An Argusic agent installed it in 3.5 minutes and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
113 unit tests pass, all 12 skills load and parse, all structural validations pass, and the repo is ready for draft-only LinkedIn skill usage with no API keys configured.
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.
- pip install failed — externally-managed-environment (PEP 668)0.5 minutes
- Install time
- 4 minutes
- Cold machine to finish
- 4 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- How the result was proved
- venv/bin/python -m unittest discover -s tests exited 0, 113 passed; scripts/selftest.py exited 0, all phases clean; all 6 validation scripts pass; codex marketplace package syncs deterministically; 4 plugin JSON files valid and version-aligned (1.1.15)
- Model tokens used
- 38,399
- Exact commit tested
- 14d332b9e91e
- 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.
Now I'll produce the final report. exec /bin/sh -lc "cd /work/repo && echo \"Time check:\" && date -u +%s && echo \"Start of work:\" && sta… succeeded in 0ms: Time check: 1791012435 Start of work: 1791012229 All checks pass cleanly. Here is the final result: tokens used 38,399 All checks pass cleanly. Here is the final result:
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 3.5 minutes.
- Recovered from all 1 error without a human stepping in, which says the failures are documented well enough to solve.
- 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.
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 with mocks | 92.00 | 0.04 |
Topics (from GitHub)
agent-skillagent-skillsai-agentsai-contentai-marketinganthropicawesome-claudeclaude-codeclaude-skillscontent-creationcontent-engineeringlinkedinlinkedin-automationlinkedin-engineeringllm-toolsopenclaw-skillpersonal-brandingprompt-engineeringskill-mdsocial-media-automation
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/linkedin-skills)Questions
- Does linkedin-skills run?
- linkedin-skills runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 4 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test linkedin-skills?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 14d332b9e91e. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test linkedin-skills?
- 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 linkedin-skills take to install?
- 3.5 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 linkedin-skills need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing linkedin-skills?
- 1 thing broke in the recorded run, and 1 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for linkedin-skills?
- 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.