Weekly routine

A weekly job search review you can run in one chat

Follow-ups due, applications that have gone quiet, what is actually getting you interviews, and a short plan for next week. One prompt you reuse every week, plus the follow-ups to act on it.

Why a weekly review

A job search produces a lot of loose ends. You applied somewhere two weeks ago and meant to follow up. Three applications from last month are still marked applied, though nobody is going to reply. You have a feeling one kind of role gets more callbacks, but nothing to check it against.

None of these is hard to fix. They just need looking at on a schedule. With Lamr connected, your assistant can read your whole pipeline in one go, so the review takes one prompt and a few minutes of decisions.

ReadReviewOne read-only prompt
FreeFollow upDrafts you send yourself
FreeClose quiet onesKeep the pipeline honest
CreditsPick next rolesScreen, then tailor the best
Look first, then act. Only the last step usually spends credits.

The one prompt

Save this somewhere you can paste it every week. It only reads, so it costs no credits and changes nothing:

Weekly review (read-only)
Run my weekly job search review in Lamr. Read only, don't change anything yet.

1. Follow-ups due today or overdue, most overdue first.
2. Applications still marked applied that I added more than 3 weeks ago.
3. My numbers: applications submitted, response rate, offer rate, current streak, and submissions this week.
4. What's working: interview rate by fit score and by source. Quote the counts, and say plainly if there's too little data to compare.
5. At most three things I should do next week, each tied to something above.

Lamr's numbers match the Stats page in the web app. Days are counted in UTC, so a streak can differ by a day from what your browser shows near midnight.

Run my weekly job search review in Lamr. Read only, don't change anything yet.
get_jobs_needing_follow_upFree
list_applicationsFree
get_pipeline_statsFree
get_outcome_insightsFree
2 follow-ups due: Northwind (4 days overdue), Fenwick (due today).3 quiet applications added over 3 weeks ago, still marked applied.This week: 4 submitted. 9 of 41 got a response so far.Too few interviews yet to compare fit-score bands.
Illustration. Your review will reflect your own pipeline.

Clear the follow-ups

A follow-up that never gets sent does nothing. Ask your assistant to draft them all at once, using what Lamr knows about each application, then push each date out so the reminder comes back if nobody answers:

Draft the follow-ups
For each follow-up that's due in Lamr, read the application and draft a short follow-up email I can send myself: name the role, give one specific reason I fit based on that application, and ask one clear question. Don't send anything. After I confirm the drafts, move each of those follow-up dates 7 days out.

Between reviews, the Lamr app shows a banner whenever follow-ups are due, so the weekly pass is a backstop rather than the only reminder.

Close the applications that have gone quiet

An application with no reply after several weeks and a follow-up is very likely over. Closing it keeps your pipeline honest, so the numbers and the review stay readable. Lamr has no separate closed status, so mark these as rejected. Their history stays intact if one of them comes back to life.

Close stale applications
Mark these applications as rejected in Lamr: [Company A], [Company B], [Company C]. Show me the result for each one, and tell me if any of them couldn't be updated.

Read your own results, not averages

Advice about which roles to apply for is usually built on someone else's data. Lamr's outcome insights use yours: which fit-score bands, resume variants, sources and templates actually got you interviews, and how long replies take.

The numbers are deliberately cautious. Only applications that have heard back, or have waited three weeks, count toward a rate. A group with fewer than five applications shows counts but no percentage. And a difference only becomes a finding when it is large enough not to be chance. Early in a search the honest answer is often that nothing stands out yet.

Ask what's working
Using my Lamr outcome insights, which fit-score band and which source actually get me interviews? Quote the counts, not just percentages, and tell me if the sample is too small to act on.
Fit 80+
5 of 16 got an interview
Fit 60 to 79
2 of 22
Under 60
0 of 9
Unscored
1 of 3, too few to compare
Example data, not a benchmark. A group under five applications shows counts only.

Plan next week

Finish by deciding where next week's effort goes. If your results show interviews come from strong matches, the plan is fewer applications, picked by fit:

Pick next week's roles
Here are [5] new postings I found. Track each in Lamr and screen my fit. Rank them by fit score with the main gap for each, and tell me which two are worth tailoring for this week given what my outcome insights show.

[posting 1]
----------
[posting 2]
----------
[and so on]
Prepare for this week's interviews
Which of my Lamr applications are at the interview stage? For each one that doesn't have interview prep yet, ask me before generating it.

Screening costs 1 credit per posting and interview prep 2 per application. Everything else in this guide is free.

Quick answers

Does the review run automatically every week?

No. You run it by pasting the prompt. Between reviews, the Lamr app shows a banner whenever a follow-up is due.

Does the assistant send my follow-up emails?

No. It drafts them for you to review and send yourself. Lamr never contacts a recruiter or an employer on your behalf.

Why does it say there is too little data to compare?

Rates only count applications that have heard back or waited three weeks, and groups with fewer than five applications get no percentage. That keeps two lucky interviews from reading as a pattern. The insights fill in as your search goes on.

Is marking an application rejected permanent?

No. It is a status like any other, and every change is recorded with its date. If a company replies later, move it back to applied or forward to interview.

Lamr MCPAsk Claude, ChatGPT, or Codex.Job Application TrackerDocuments, notes, status, and history.
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