recommendation-letter

name	recommendation-letter
description	Generate support, recommendation, appreciation, acceptance, and dispense (short-stay invitation) letters for students and candidates of Prof. Martin Otis / LAR.i, in LaTeX compiled to PDF. Ingests the candidate's own files (CV, transcript, project description, motivation letter) and highlights both the candidate's dossier and the professor's own experience with the candidate. Trigger on: recommendation letter, support letter, appreciation letter, acceptance letter, invitation letter, dispense / work permit exemption letter, lettre d'appui, lettre de recommandation, lettre d'appreciation, lettre d'acceptation, lettre d'invitation, lettre de dispense, PFLA, ELAP, CRSNG, FRQNT, MITACS, or /recommendation-letter.
allowed-tools	["Read","Write","Edit","Bash","AskUserQuestion","Glob"]

Recommendation Letter Generator

Two generation tracks, six letter types. All output goes to out/.

• Authored track (scholarship, academic_position, industry_position, appreciation; French or English): Claude writes the body prose, the Python script wraps it in the shared scaffold.
• Form track (acceptance, dispense; French only): the script fills a fixed administrative template from the config fields. Do not paraphrase the fixed wording (objet line, immigration paragraph).

The script scripts/generate_letter.py is standard-library only (no pip install). pdflatex compiles the PDF; if absent, the .tex is still produced.

Candidate status (drives how the candidate is named)

• applicant: admission candidate, not yet a student -> "le candidat / M. X".
• current_student: on the team, not graduated -> "X, etudiant(e) au ".
• graduated: degree completed -> "le Dr X" / "Dr. X" (uses candidate_title).

Funding provider (acceptance / dispense)

• supervisor: the professor funds via an organism (MITACS, CRSNG, FRQNT, BEFM, internal RA/TA).
• candidate: the candidate's own funding (external scholarship, or self / family funded).
• combination: both.

Workflow

1. Ingest candidate files from $ARGUMENTS (a folder or a list of paths): CV, transcript, project description, motivation letter. Extract GPA, course names, publications, project facts, dates. Read PDFs/text directly.
2. Elicit gaps with AskUserQuestion:
   • Round 1 context: letter_type, language, target, target_institution, limit, reference_number; for form types also invitation_pair and funding_provider.
   • Round 2 profile: candidate_name, candidate_gender, candidate_status, candidate_title, candidate_program.
   • Round 3 type-specific fields, plus a question for the professor's own experience / relationship with the candidate (authored track). Pre-fill from the ingested files; ask only what is missing or uncertain; mark anything unknown [A COMPLETER].
3. Confirm a JSON summary of all fields with the user before generating.
4. Author body_tex (authored track only): a direct opening verdict, the candidate's dossier highlights, a distinct paragraph on the professor's own experience, an honest weighing of strengths, and a closing offer of availability. Follow references/quality-patterns.md. Style hygiene: no em dash, straight quotes, no invisible characters (AI-usage < 20%). Never fabricate a grade, a publication, or a funding amount; leave [A COMPLETER].
5. Generate: write the config to a scratchpad JSON and run python .claude/skills/recommendation-letter/scripts/generate_letter.py --config <json> --output out/.
6. Read the warnings (placeholder, over-limit, status/title mismatch, style hygiene, 120-day). Fix the body or fields and re-run as needed.
7. Deliver the .tex and .pdf from out/ (two of each when invitation_pair=both).

Notes

• The two form types reproduce the exact administrative wording; the script owns it. Do not rewrite the immigration paragraph.
• The dispense letter subject keeps "MOINS DE 120 JOURS" even when the stay is longer (the work-permit exemption threshold is the reference); the script warns and appends the extra immigration clause.
• All shipped eval / test data is synthetic (Candidat Exemple ...). Never commit a real candidate's personal data to the repository.