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Version: 0.1.21

Find Jobs and Apply Workflow

Goal​

This guide documents the main intended pre-application workflow in JobOps.

If you follow this order, you get the strongest results from discovery, scoring, tailoring, and tracking.

1) Run a pipeline first​

From the Jobs page, use the top-right pipeline/run control.

What this does:

  • fetches jobs from enabled extractors
  • scores relevance against your resume/profile
  • optionally tailors top jobs and prepares PDFs

Important:

  • Some scrapers are slower and can take significant time.
  • Larger scrape ranges and more sources increase run duration.

2) Configure pipeline advanced settings​

In pipeline advanced settings, configure:

  • how many jobs to discover (approximate target)
  • minimum score threshold for tailoring
  • how many jobs should be tailored/generated

This directly controls how many jobs appear downstream in discovered and ready.

3) Review the Discovered column​

After the run, discovered is populated with jobs found by extractors.

For each discovered job:

  • review the suitability score
  • read the AI fit justification in Fit Assessment
  • decide whether the opportunity is worth advancing

4) Work from Ready for applications​

ready jobs are the primary application queue.

These jobs already have tailored PDFs generated for the specific job description, using the workflow described in Reactive Resume.

At this stage:

  1. Open job details.
  2. Download the tailored PDF.
  3. Submit your application externally.

5) Mark jobs as applied in JobOps​

After submitting, return to JobOps and mark the job as applied.

Effects:

  • job moves to the applied state
  • configured completion webhook(s) are triggered
  • job is included in overview analytics

This completes the detailed pre-application loop.

What happens next​

Once a job is marked applied, it becomes part of:

  • pipeline outcome analytics on Overview
  • optional post-application workflows (inbox/review routing)

Practical tips​

  • Start with conservative run sizes while tuning sources.
  • Increase tailored-job count only after score thresholds feel calibrated.
  • Expect scraper runtime variance by source.
  • Keep resume/project context up to date so scoring/tailoring quality stays high.