ai-job-search
The job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.
Runssource: GitHubPythonMITcommit 42ba4b475a8a
Python, MIT licensed. The project labels itself: ai, ai agents, career, claude code, cover letter, cv, interview preparation and job application.
ai-job-search runs. An Argusic agent installed it in 20 minutes and hit 4 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Python test suite (321 unittest tests) and all 6 Bun portal CLI test suites (231 tests) pass. LinkedIn search CLI makes live queries and returns results. 4 Python analysis tools (security_guards, framework_version, lint_skills, verify_pdf) run successfully.
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.
- Bun curl installer failed: unzip not installed3 minutes
- PyYAML not installed (6 tests skipped)1 minute
- LaTeX not available (lualatex/xelatex not found)
- salary_data.json not found
- Install time
- 20 minutes
- Cold machine to finish
- 8 minutes
- Errors hit and fixed
- 4 hit, 4 fixed with no human help
- How the result was proved
- python3 -m unittest discover -s tests/ exited 0, 321 passed; bun test across 6 portal CLIs all exited 0, 231 total passed; bun run linkedin-search/cli/src/cli.ts search --location Copenhagen --limit 2 --format table returned real results (2 listings); bun run linkedin-search/cli/src/cli.ts search --location 'San Francisco' --query engineer --limit 2 --format table returned real results (2 listings)
- Model tokens used
- 93,748
- Exact commit tested
- 42ba4b475a8a
- 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 1, 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.
**Salary data**: `salary_data.json` not present; the tool gracefully errors with a skip message — e… tokens used 93,748 Here's my final assessment: **Install**: Python 3.12.3 with no extra dependencies needed (stdlib only). PyYAML was missing for … **Tests**: - Python: 321/321 pass (after PyYAML install, 0 skip) - Bun CLIs: 231/231 pass across 6 portal skills (0 fail) - LinkedIn search: live query returned real listings (Copenhagen, SF) - Tools: `security_guards`, `check_framework_version`, `lint_skills` all OK **LaTeX**: Not available (no `lualatex`/`xelatex` in the container — requires root for TeX distribu… **Salary data**: `salary_data.json` not present; the tool gracefully errors with a skip message — e…
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 4 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 4 times, so the result is not a one-off.
What did not, or is not known
- Took 20 minutes to install, slower than the median of the 36 comparable projects Argusic has measured.
Of the 37 ai projects Argusic has installed and timed, ai-job-search was the 33rd fastest to reach a running state, and 30 of 37 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.
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)
aiai-agentscareerclaude-codecover-lettercvinterview-preparationjob-applicationjob-huntingjob-searchlatexresume
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/ai-job-search)Questions
- Does ai-job-search run?
- Yes. ai-job-search runs. Argusic installed and launched it on a clean machine in 20 minutes, hitting 4 errors on the way, and recorded the session.
- How did Argusic test ai-job-search?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 42ba4b475a8a. 4 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test ai-job-search?
- 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 ai-job-search take to install?
- 20 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 ai-job-search need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing ai-job-search?
- 4 things broke in the recorded run, and 4 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does ai-job-search compare with the alternatives?
- Of the 37 ai projects Argusic has installed and timed, ai-job-search was the 33rd fastest to reach a running state, and 30 of 37 reached one at all.
- Where is the evidence for ai-job-search?
- All 4 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.