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I automated the repetitive parts of my job search across seven platforms, but I did not automate applications themselves. The workflow scans openings, scores them against my resume, drafts application material for promising roles, and sends them to Slack for my review. I still decide whether to apply and complete each application.
What the seven-board workflow automates
In my account, the daily scan covers Lever, Greenhouse, Ashby, SmartRecruiters, Workday, Indeed and Google through JobSpy, and Hiring.cafe. The tool gathers openings, compares them with my resume using an LLM, and assigns each one a score from 0 to 100.
For a role above a selected score threshold, it drafts a tailored cover letter and a “why this company” response. It then sends a Slack card containing the role and its application link. A weekly report summarizes roles found, scored, and applied to. These are features and outcomes described by the author, not independently verified performance results.
Where a person remains in control
The tool does not submit applications. As I put it in my account, “The tool does not auto-submit applications. That was deliberate.” I review each Slack card, decide whether the role is worth pursuing, and complete the application myself.
Slack also acts as a lightweight status interface: I can mark a role as applied or ask for interview preparation. For interview prep, the workflow can produce STAR stories mapped to likely questions. This keeps the system focused on finding and preparing for opportunities rather than making application decisions on my behalf.
How the pieces fit together
The project is described as a Python CLI with a thin client for each platform and a pluggable model backend. Its documented workflow includes a scheduled daily scan, deduplication by job ID and by title plus company, a per-company cap, and balancing across platforms. A Slack bot monitors reactions to role cards, while weekly reporting tracks activity.
The public GitHub repository README documents profile configuration, optional JobSpy installation for Indeed and Google scraping, local-model and Anthropic API backend options, and an MIT license. Those are repository details; they should not be read as a guarantee that setup or platform integrations remain compatible today.
Choosing a model backend
The author describes a local model as free and the Anthropic API as faster. Those are the author’s characterizations, not a current price comparison or measured speed benchmark. The sources do not report tests comparing latency, cost, or quality.
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- PLAN YOUR WEEK CLEARLY – Use this weekly to do list and weekly planner pad to organize priorities and manage workflow across the week
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- Local model: Consider the setup and computing resources required, along with how the model handles your resume and job details.
- API backend: Check current provider pricing, service terms, data handling, and the practical latency for your workload before sending personal information.
The choice depends on infrastructure, recurring usage cost, latency, and privacy requirements. Review the current provider and project documentation before configuring either option.
What I learned—and what the account does not prove
I found that matching openings against my actual resume reduced noise compared with relying only on keywords. The Slack cards and reaction-based tracking helped me keep up with the workflow, and retaining a human decision step made the system feel trustworthy to me. These are personal observations, not controlled findings that will apply to every job seeker.
The account does not report measured time saved, total applications, callback or interview rates, job offers, or results compared with manual searching. It therefore shows how the workflow is designed and how its author experienced it, but it cannot establish that automation improves job-search outcomes.
Who may find this approach useful
This workflow is most relevant if you want one place to review openings from several boards and reduce repetitive resume matching and drafting. It is not a hands-off application bot: the person still checks fit, verifies generated text, makes the apply decision, and completes the submission. Generated cover letters and interview stories should be treated as drafts and checked for accuracy before use.
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The underlying problem—repetitive searching, copying details, and tailoring materials—is the author’s framing of their own process, not a measured claim about all job seekers. The account appeared on DEV Community on September 16, 2026; the linked repository README may change independently.
Quick Recap
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