Skill_Seekers
Convert documentation websites, GitHub repositories, and PDFs into Claude AI skills with automatic conflict detection
Runssource: GitHubhomepagePythonMITcommit f3972efa33fa
Python, MIT licensed. The project labels itself: ai tools, ast parser, automation, claude ai, claude skills, code analysis, conflict detection and documentation.
Skill_Seekers runs. An Argusic agent installed it in 8 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
skill-seekers 3.10.0.dev0 installed and all 3857 unit/adaptor/scraper tests pass without external API keys or network dependencies.
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
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.
- Pytest not resolved via dependency-groups on first install pass2 minutes
- Externally managed Python — needed virtual environment1 minute
- Install time
- 8 minutes
- Cold machine to finish
- 6 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- pytest tests/ -m "not slow and not integration and not e2e and not network and not serial and not mcp_only" exited 0, 3857 passed, 148 skipped, 0 failed; skill-seekers doctor exited 0 with all critical checks passed
- Model tokens used
- 125,574
- Exact commit tested
- f3972efa33fa
- 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
Watch the session
The whole run, from a clean machine, at the speed it actually happened. Nothing is cut and nothing is sped up.
A recording of attempt 2, run . The verdict above comes from a different attempt, so read this as one session and not as the current result. The terminal recording it was made from is the evidence, stored with a sha256 fingerprint; the video is a rendering of it. Nothing loads from the video host until you press play.
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
[--issue-state {open,closed,all}]
[--max-comments N] [--per-issue-files]
The project installs and runs perfectly. Let me compile the results:
**Install:** Created a venv, installed `-e ".[dev]"` plus additional test deps (pytest wasn't bundl…
**Tests:** 3857 passed, 148 skipped (optional deps like mcp, chromadb, etc.), 239 deselected (slow/…
**CLI verification:** `skill-seekers doctor` reports all critical checks passed, `skill-seekers --v…
tokens used
125,574
The project installs and runs perfectly. Let me compile the results:
**Install:** Created a venv, installed `-e ".[dev]"` plus additional test deps (pytest wasn't bundl…
**Tests:** 3857 passed, 148 skipped (optional deps like mcp, chromadb, etc.), 239 deselected (slow/…
**CLI verification:** `skill-seekers doctor` reports all critical checks passed, `skill-seekers --v…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.
- Recovered from all 2 errors without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
- Measured 3 times, so the result is not a one-off.
What did not, or is not known
- Took 8 minutes to install, slower than the median of the 3 comparable projects Argusic has measured.
Of the 4 ai-tools projects Argusic has installed and timed, Skill_Seekers was the 3rd fastest to reach a running state, and 3 of 4 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 8 minutes after the clone.
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
Topics (from GitHub)
ai-toolsast-parserautomationclaude-aiclaude-skillscode-analysisconflict-detectiondocumentationdocumentation-generatorgithubgithub-scrapermcpmcp-servermulti-sourceocrpdfpythonweb-scraping
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/skill-seekers)Questions
- Does Skill_Seekers run?
- Yes. Skill_Seekers runs. Argusic installed and launched it on a clean machine in 8 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test Skill_Seekers?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit f3972efa33fa. 3 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test Skill_Seekers?
- 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 Skill_Seekers take to install?
- 8 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 Skill_Seekers need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing Skill_Seekers?
- 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 Skill_Seekers compare with the alternatives?
- Of the 4 ai-tools projects Argusic has installed and timed, Skill_Seekers was the 3rd fastest to reach a running state, and 3 of 4 reached one at all.
- Where is the evidence for Skill_Seekers?
- All 3 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.