social-media-research-skills

AI agent skills for social media research. Outlier posts, comment mining, competitor teardowns, ad libraries & trends across TikTok, Instagram, YouTube, Reddit, X, LinkedIn & more. Powered by ScrapeCreators. Works with Claude Code, Cursor, Codex, Gemini CLI.

Runs with mockssource: GitHubhomepagePythonMITcommit 64ba7b4dea71

Python, MIT licensed. The project labels itself: agent skills, ai agents, claude code, claude skills, competitor analysis, content marketing, cursor and influencer marketing.

social-media-research-skills runs, with stand-ins for the services it depends on. An Argusic agent installed it in 5 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.

All 13 SKILL.md files have valid frontmatter and non-empty bodies, the npx skills CLI installs the repo correctly (13 skills listed under .agents/skills/), and the validate-skills.py script passes cleanly.

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

verdict
Runs with mocks
Argusic Score
92 / 100
cost of the verifying run
$0.02 (measured)
recorded runs
1
last tested
stars
3,041
forks
40
open issues
3
watchers
10
size
0 MB
created
last push

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.

Time lost to each failure while testing social-media-research-skills, drawn from the recorded run
Every bar is a failure the agent recorded, with the time it cost. Drawn from this run, not from anywhere else.

What went wrong, in the agent's own words

The agent's own words, worst first, never rewritten into advice.

  • npx skills requires Node >=22.20.0 but system has v18.19.12 minutes
  • PyYAML not installed; validate-skills.py fails1 minute
Install time
5 minutes
Cold machine to finish
3 minutes
Errors hit and fixed
2 hit, 2 fixed with no human help
How the result was proved
npx skills list (via Node v22.20.0) listed all 13 skills; python3 scripts/validate-skills.py exited 0: 'Validated 13 skills'; git diff --check exited 0; content of source and installed SKILL.md files matches via diff
Model tokens used
31,904
Exact commit tested
64ba7b4dea71
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.

exec
/bin/sh -lc '# Quick time tracking
echo "Session started roughly at" && echo $((1790903926 - 300)) && echo "and ended at 1790903926, d…
 succeeded in 0ms:
Session started roughly at
1790903626
and ended at 1790903926, difference roughly
minutes: 5
Everything checks out. Here's the final report:
tokens used
31,904
Everything checks out. Here's the final report:

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

  • Recovered from all 2 errors 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.
  • Took 5 minutes to install, slower than the median of the 26 comparable projects Argusic has measured.
  • Measured once so far. A single attempt shows it can run, not that it runs reliably.

Of the 27 agent-skills projects Argusic has installed and timed, social-media-research-skills was the 18th fastest to reach a running state, and 18 of 27 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 byattemptoutcomescorecost USDwhen
Argusic Runner1/3Runs with mocks92.000.02

Topics (from GitHub)

agent-skillsai-agentsclaude-codeclaude-skillscompetitor-analysiscontent-marketingcursorinfluencer-marketingmarket-researchsocial-mediasocial-media-researchworkflow-automation

Embed the badge

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

[![Tested by Argusic](https://argusic.com/badge/social-media-research-skills.svg)](https://argusic.com/subject/social-media-research-skills)

Questions

Does social-media-research-skills run?
social-media-research-skills runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 5 minutes, hitting 2 errors on the way, and recorded the session.
How did Argusic test social-media-research-skills?
On a fresh, disposable machine, with every command recorded and the repository pinned at commit 64ba7b4dea71. One attempt is recorded, and the full method is on the methodology page.
What did it cost to test social-media-research-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-research-skills take to install?
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-research-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-research-skills?
2 things broke in the recorded run, and 2 were fixed without human help. Each one, and the time it cost, is listed on this page.
How does social-media-research-skills compare with the alternatives?
Of the 27 agent-skills projects Argusic has installed and timed, social-media-research-skills was the 18th fastest to reach a running state, and 18 of 27 reached one at all.
Where is the evidence for social-media-research-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.

Discussion