py/ai-job-search
1
0
Fork 0
mirror of https://github.com/MadsLorentzen/ai-job-search.git synced 2026-09-17 08:36:25 +00:00
Code Issues Packages Projects Releases Wiki Activity

fix(tracker): persist the application deadline end to end (#319) (#324)

Browse Source 
The deadline is written at every moment it is provably in hand and survives
every write that follows: seen_jobs.json base field, /rank stored-value urgency
+ expiry sweep with Step 4 persistence, tracker 14th column with header-line-only
migration for existing files, /scrape-path extraction (assistant SKILL.md 1.3.2
-> 1.3.3), preserve-unparsed-fields in /outcome and /gmail-sync, notion-sync
deadline precedence. Design, scope analysis, and the folded refinements by
jakob1379 (#319, #328).

Co-authored-by: Jakob Stender Guldberg <17257805+jakob1379@users.noreply.github.com>
This commit is contained in:
Oscar Madera
2026-08-16 19:56:45 +02:00
committed by GitHub
co-authored by Jakob Stender Guldberg
parent 5c6ffe8aa7
commit c855e11d22
12 changed files with 247 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.claude/skills/upskill/SKILL.md
+1 -1
View File
@@ -35,7 +35,7 @@ In targeted mode, derive a slug from the job title and company for the report fi
### Aggregate mode
1. Read `job_search_tracker.csv`. Extract all rows. The columns are:
`date, company, sector, role, role_type, channel, status, contact_person, fit_rating, notes, cv_file, cover_letter_file, source`
`date, company, sector, role, role_type, channel, status, contact_person, fit_rating, notes, cv_file, cover_letter_file, source, deadline`
2. For each row, note the `role`, `company`, and `fit_rating`. The `fit_rating` column is a 0–100 score where 100 = perfect fit. You will use it to weight gaps — a lower fit rating means the role exposed more gaps.
3. Read `job_scraper/seen_jobs.json`. Keep entries with `"status": "ranked"` and `rank_score >= 45` — the Moderate Fit floor from `04-job-evaluation.md` (below that, a job is Weak/Poor Fit and would otherwise dominate the heatmap with jobs the user shouldn't chase). For each kept entry, note its `title`, `company`, `rank_score`, and — when present — its recorded `gaps`. An entry with no `gaps` field (ranked before gap persistence existed) is skipped, counted, and reported once in the terminal: *"N ranked jobs were scored before gap persistence and contribute nothing; `/rank --all` re-scores them."* Never back-fill a missing `gaps` field by guessing from the title.
4. Read `.claude/skills/job-application-assistant/01-candidate-profile.md` to get the candidate's current skills and experience.