resume-tailor

Resume Tailor (evidence-only, with skill confirmation)

Tailors the current CV DOCX for one job using cv_tailor.py. Claude does the comparison and asks the user which missing skills they really have; the script applies the edits and exports a PDF (the temporary DOCX is deleted). No Gemini call, so it uses none of the 20/day free quota.

Several postings at once (user gives 2+ URLs): use the batch flow below instead of repeating the steps per job.

Batch flow (several postings)

1. python cv_tailor.py jds <scratchpad>/jds <url1> <url2> ... fetches all JDs in parallel into jd_1.txt, jd_2.txt, ... (input order). For any line marked FAILED, open the page in the browser or ask the user to paste it.
2. Read the CV(s) once (cv, and the AI CV with BASE_CV_DOCX=... if any posting is AI-focused).
3. Compare each JD (step 3 below). Ask ONE AskUserQuestion round for all jobs: merge the missing skills across postings and ask each skill once (a confirmed skill counts for every job that needs it).
4. Write <scratchpad>/batch.json: [{"company": "Acme Backend", "base": "main", "edits": {"replace": [...], "add_skills": [...]}}, ...] base is main (BASE_CV_DOCX) or ai (BASE_CV_DOCX_AI). Use a distinct company per posting (e.g. company + role) so the PDFs don't overwrite each other.
5. python cv_tailor.py apply-batch <scratchpad>/batch.json (optional --workers N, default 3). All edits are checked before any PDF is made; PDFs export in parallel, one Word instance per worker.
6. Report per job as in step 7 (one table per job is fine).

Steps

1. Get the job description. Run python cv_tailor.py jd "<url>". Any job link works: LinkedIn, Welcome to the Jungle, Ashby and Greenhouse use the site's own data for a clean description; other sites (company career pages, Workday, etc.) fall back to reading the page. If it fails (login walls, closed jobs), ask the user to paste the description.
2. Read the CV. Run python cv_tailor.py cv (prints each paragraph of the base CV DOCX).
3. Compare. List the JD's skills/requirements, in English even if the JD is French. Split them into:
   • already on the CV (nothing to do, or surface better wording if the evidence is there)
   • missing from the CV
4. Ask with multi-select. Use AskUserQuestion with multiSelect: true: up to 4 questions of up to 4 missing skills each, mandatory JD skills first. Question text like "Which of these have you used in real work?". The user can type extra detail in "Other" (e.g. the tool they used). If a confirmed skill needs context to be described honestly (where/how), ask a short follow-up.
5. Draft edits (rules below) and write them to a JSON file in the scratchpad: {"replace": [{"old": "...", "new": "..."}], "add_skills": ["..."]}. Each old must sit inside ONE run of the DOCX. Bold highlights are separate runs, so keep each edit within a plain or a bold segment of the paragraph text from step 2.
6. Apply. Run python cv_tailor.py apply "<Company>" <edits.json>.
7. Report the "Words changed" output from apply (shown as removed -> added), the confirmed skills added, the page count, the JD skills still missing, and the output paths.