Jobs_Applier_AI_Agent_AIHawk
Give it your resume, get the jobs that fit, select them and apply. Runs in your terminal, with a browser you can watch.
Runs with mockssource: GitHubPythonMITcommit eb54b9b4f757
Python, MIT licensed. The project labels itself: ai agent, ai agents, automation, browser automation, job application, job board, job search and mcp.
Jobs_Applier_AI_Agent_AIHawk runs, with stand-ins for the services it depends on. An Argusic agent installed it in 4 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
The repository installs from source with all dependencies, the full automated suite passes (266 non-browser + 6 real-browser), and an end-to-end search-and-apply run through a mock OrcaRouter over a real local job index fills and submits a live Greenhouse application form, writing a decoded proof screenshot.
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. 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.
- Invisible-playwright-mcp / Firefox and all 16 bundled plugins install cleanly; the apply flow failed with a generic ExceptionGroup leaving no diagnostics in the TUI38 minutes
- First E2E scripted runs failed: the mock gateway I stood up initially didn't return OpenAI-shaped choices and the profile handler wasn't matched because the schema name is search_profile, not profile7 minutes
- Install time
- 4 minutes
- Cold machine to finish
- 51 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- pytest -m "not browser" -> 266 passed; pytest tests/test_real_browser.py -> 6 passed; aihawk --version -> aihawk, version 1.0.0; ORCAROUTER_API_KEY=sk-test python <e2e flow> -> resume loaded, 600 fitting postings, 25 shortlisted, 25 scored, apply_to_job on https://job-boards.greenhouse.io/10alabs/jobs/4467496009 -> Outcome submitted with evidence 'Thank you, your application was received' and proof PNG decarbonized as PNG 1920x947
- Model tokens used
- 3,810,485
- Exact commit tested
- eb54b9b4f757
- 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
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.
PY" in /work/repo succeeded in 0ms: cv.md: True answers.yaml: True key file: True jobs.db: True 1134592 proofs: ['greenhouse-io-10alabs-jobs-4467496009.png', 'greenhouse-io-healthie-jobs-4469749009.png',… applied.csv rows: 6 Everything is verified. Final report: tokens used 3,810,485 Everything is verified. 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
- Installed in 4 minutes, faster than the median of the 70 comparable projects Argusic has measured.
- 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.
- 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 71 ai-agent projects Argusic has installed and timed, Jobs_Applier_AI_Agent_AIHawk was the 30th fastest to reach a running state, and 44 of 71 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 by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 2/3 | Runs with mocks | 92.00 | 0.81 | |
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.02 |
Topics (from GitHub)
ai-agentai-agentsautomationbrowser-automationjob-applicationjob-boardjob-searchmcpplaywrightpythonresumeterminal-app
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/jobs-applier-ai-agent-aihawk)Questions
- Does Jobs_Applier_AI_Agent_AIHawk run?
- Jobs_Applier_AI_Agent_AIHawk runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 4 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test Jobs_Applier_AI_Agent_AIHawk?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit eb54b9b4f757. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test Jobs_Applier_AI_Agent_AIHawk?
- The run that produced this verdict cost $0.81: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does Jobs_Applier_AI_Agent_AIHawk take to install?
- 4 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 Jobs_Applier_AI_Agent_AIHawk need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing Jobs_Applier_AI_Agent_AIHawk?
- 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 Jobs_Applier_AI_Agent_AIHawk compare with the alternatives?
- Of the 71 ai-agent projects Argusic has installed and timed, Jobs_Applier_AI_Agent_AIHawk was the 30th fastest to reach a running state, and 44 of 71 reached one at all.
- Where is the evidence for Jobs_Applier_AI_Agent_AIHawk?
- 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.