# REINIT Profile Configuration
# Copy this file to config/profile.yml and fill in your details.
# This is the single source of truth for your personal data across all modes.

candidate:
  full_name: "Jane Smith"
  email: "jane@example.com"
  phone: "+1-555-0123"
  location: "San Francisco, CA"
  linkedin: "linkedin.com/in/janesmith"
  portfolio_url: "https://janesmith.dev"
  github: "github.com/janesmith"
  twitter: "https://x.com/janesmith"

target_roles:
  # Your North Star roles — what you're optimizing for
  primary:
    - "Senior AI Engineer"
    - "Staff ML Engineer"
  # Archetypes help the evaluation system score fit
  archetypes:
    - name: "AI/ML Engineer"
      level: "Senior/Staff"
      fit: "primary"        # primary = dream role, secondary = good fit, adjacent = stretch
    - name: "AI Product Manager"
      level: "Senior"
      fit: "secondary"
    - name: "Solutions Architect"
      level: "Mid-Senior"
      fit: "adjacent"

narrative:
  # Your professional headline (1 line)
  headline: "ML Engineer turned AI product builder"
  # Your exit story — what makes you unique
  exit_story: "Built and sold my SaaS after 5 years. Now focused on applied AI at scale."
  # Your top 3-5 superpowers
  superpowers:
    - "End-to-end ML pipelines"
    - "Fast prototyping (idea to prod in 2 weeks)"
    - "Cross-functional communication"
  # Proof points — projects, articles, case studies with measurable impact
  proof_points:
    - name: "Project Alpha"
      url: "https://janesmith.dev/project-alpha"
      hero_metric: "Reduced inference latency 40%"
    - name: "Open Source Tool"
      url: "https://github.com/janesmith/tool"
      hero_metric: "2K+ GitHub stars"
  # Optional: dashboard/demo URL with credentials
  # dashboard:
  #   url: "https://janesmith.dev/demo"
  #   password: "demo-2026"

compensation:
  target_range: "$150K-200K"     # Your target total comp
  currency: "USD"
  minimum: "$120K"               # Walk-away number
  location_flexibility: "Remote preferred, 1 week/month on-site possible"

location:
  country: "United States"
  city: "San Francisco"
  timezone: "PST"
  visa_status: "No sponsorship needed"
  # For remote roles outside your country:
  # onsite_availability: "1 week/month in any city"

# Optional follow-up cadence preferences for `node src/tracker/followup-cadence.mjs`.
# CLI flags still win for one-off runs (for example `--applied-days 10`).
# followup_cadence:
#   applied_first_days: 7
#   applied_subsequent_days: 7
#   applied_max_followups: 2
#   responded_initial_days: 1
#   responded_subsequent_days: 3
#   interview_thankyou_days: 1

cv:
  output_format: "html" # "html" (default) or "latex"
  # (Optional) Canva resume design ID for visual CV generation via /reinit pdf.
  # Find it in your Canva design URL: https://www.canva.com/design/DAxxxxxxx/...
  # The ID starts with "D" and is 11 characters long.
  # canva_resume_design_id: "DAxxxxxxxxx"

# ── Cover Letter Settings ──────────────────────────────────────────────────
#
# Used by `/reinit cover` and the cover letter sub-flow in `/reinit pdf`.
# All fields are optional — omit any you don't need.

cover_letter:
  # Your notice period in calendar days. Surfaced as a prompt default when the
  # JD requests an immediate start. The user confirms the actual value before it
  # appears in the letter.
  notice_period_days: 30

  # Your current professional domain (used to detect domain gaps vs the JD).
  # Plain English, e.g. "digital media", "fintech", "healthcare IT".
  primary_domain: "your current domain"

  # Languages you are actively learning. Each entry MAY produce a closing
  # sentence in that language IF the JD location matches one of the listed
  # countries AND the user confirms inclusion during the cover letter flow.
  # Remove the block entirely if not applicable.
  language_learning:
    - language: Spanish
      current_level: B1
      target_level: B2
      target_date: "end of 2026"
      # The sentence to include, written in that language. Keep it one line.
      sentence: "Estoy aprendiendo español y espero alcanzar el nivel B2 a finales de 2026."
      # Only trigger this entry when the JD location is in one of these countries.
      countries: [Spain, Mexico, Argentina, Colombia, Chile]

# ── Auto-PDF threshold ──────────────────────────────────────────────────────
#
# Auto-PDF threshold during evaluation (used by `/reinit pipeline` and
# `bash batch/batch-runner.sh`).
#
# An offer's tailored CV PDF is auto-generated only when its score is
# >= this threshold. Generating a PDF costs ~30-60s per offer (Playwright
# launch + HTML render), and most offers in a scan score 2.x/3.x and never
# become an actual application — so the gate avoids wasted renders.
#
# Default (key absent OR commented out): 3.0 — the original gate of
# `/reinit pipeline`. Both evaluation paths apply the same default, so
# a batch run and an interactive run treat the same offer identically.
#
# Raise it (e.g. 4.0) to auto-generate PDFs only for stronger matches and
# leave the rest report-only — those can still be produced on demand via
# `/reinit pdf {company-slug}`. Fractional thresholds work (e.g. 3.5 —
# half-point scores like 3.5/5 occur in evaluations). Set it to 0 to
# generate a PDF for every offer.
# auto_pdf_score_threshold: 4.0