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Mode: pdf — ATS-Optimized PDF Generation

Full Pipeline

  1. cv.md is already in your context via opencode.json:instructions — use it as the source of truth, do NOT Read it again
  2. Ask the user for the JD if not already in context (text or URL)
  3. Extract 15-20 keywords from the JD
  4. Detect JD language → CV language (EN default)
  5. Detect company location → paper format:
    • US/Canada → letter
    • Rest of the world → a4
  6. Detect role archetype → adapt framing
  7. Rewrite Professional Summary injecting JD keywords + exit narrative bridge ("Built and sold a business. Now applying systems thinking to [JD domain].")
  8. Select the top 3-4 most relevant projects for the offer
  9. Reorder experience bullets by relevance to the JD
  10. Build competency grid from JD requirements (6-8 keyword phrases)
  11. Inject keywords naturally into existing achievements (NEVER fabricate)
  12. Generate complete HTML from template + personalized content
  13. Write HTML to /tmp/cv-candidate-{company}.html
  14. Run: npx job-forge pdf /tmp/cv-candidate-{company}.html output/cv-candidate-{company}-{YYYY-MM-DD}.pdf --format={letter|a4}
  15. Report: PDF path, page count, keyword coverage %

Apply these ATS rules for clean parsing

  • Single-column layout (no sidebars, no parallel columns)
  • Standard headers: "Professional Summary", "Work Experience", "Education", "Skills", "Certifications", "Projects"
  • No text in images/SVGs
  • No critical info in PDF headers/footers (ATS ignores them)
  • UTF-8, selectable text (not rasterized)
  • No nested tables
  • JD keywords distributed across: Summary (top 5), first bullet of each role, Skills section

Apply this PDF design

  • Fonts: Space Grotesk (headings, 600-700) + DM Sans (body, 400-500)
  • Fonts self-hosted: fonts/
  • Header: name in Space Grotesk 24px bold + gradient line linear-gradient(to right, hsl(187,74%,32%), hsl(270,70%,45%)) 2px + contact row
  • Section headers: Space Grotesk 13px, uppercase, letter-spacing 0.05em, cyan primary color
  • Body: DM Sans 11px, line-height 1.5
  • Company names: accent purple color hsl(270,70%,45%)
  • Margins: 0.6in
  • Background: pure white

Sort sections for the "6-second recruiter scan"

  1. Header (large name, gradient, contact, portfolio link)
  2. Professional Summary (3-4 lines, keyword-dense)
  3. Core Competencies (6-8 keyword phrases in flex-grid)
  4. Work Experience (reverse chronological)
  5. Projects (top 3-4 most relevant)
  6. Education & Certifications
  7. Skills (languages + technical)

Apply ethical keyword injection (truth-based)

Examples of legitimate reformulation:

  • JD says "RAG pipelines" and CV says "LLM workflows with retrieval" → change to "RAG pipeline design and LLM orchestration workflows"
  • JD says "MLOps" and CV says "observability, evals, error handling" → change to "MLOps and observability: evals, error handling, cost monitoring"
  • JD says "stakeholder management" and CV says "collaborated with team" → change to "stakeholder management across engineering, operations, and business"

NEVER add skills the candidate does not have. Only reformulate real experience using the exact vocabulary of the JD.

Write in a style that defeats AI-detection (CRITICAL)

ATS platforms (Indeed, LinkedIn, Workday) increasingly flag AI-generated CVs. The PDF generation MUST produce text that reads as human-written. This is not about deception — it's about ensuring the candidate's real experience isn't filtered out by an automated detector before a human ever sees it.

Apply sentence-structure variations

  • Vary sentence length deliberately. Mix short punchy fragments ("Shipped in 3 weeks.") with longer compound sentences. AI text tends toward uniform medium-length sentences.
  • Start bullets differently. Don't begin every bullet with a past-tense action verb. Mix structures: "Led...", "The team needed X, so I...", "After discovering Y, rebuilt Z to..."
  • Use the candidate's actual phrasing from cv.md when possible. The candidate's own words have idiosyncratic phrasing that AI-generated text lacks. Preserve their voice — reformulate for keywords, but don't rewrite the whole sentence.

Select words carefully

  • Avoid AI-hallmark phrases. Never use: "leveraged", "utilized", "spearheaded", "orchestrated" (as a metaphor for "managed"), "cutting-edge", "passionate about", "drive innovation", "synergy", "holistic approach", "navigate complex", "foster collaboration". These are the first things detectors look for.
  • Use plain, specific verbs. "Built" not "architected". "Ran" not "orchestrated". "Fixed" not "remediated". "Cut costs by" not "optimized cost efficiency". The more specific and concrete, the more human it reads.
  • Preserve technical jargon as-is. Real engineers say "k8s" not "Kubernetes orchestration platform". Keep the candidate's working shorthand.

Apply structural variations

  • Don't over-polish. A real CV has minor asymmetries — one job has 4 bullets, another has 3. One bullet is 2 lines, the next is 1. Don't normalize everything to uniform length.
  • Keep the Professional Summary under 4 sentences. AI-generated summaries tend to be dense paragraphs that try to cover everything. A human writes a tighter summary and lets the experience section do the work.
  • Don't repeat the same metric in both the summary and a bullet. Humans don't do this. Pick the best place for each number.

Check the draft before generating HTML

After drafting all CV content, review it once against these checks.

  1. Do 3+ bullets start with the same word? → Rewrite the openings.
  2. Are all bullets the same length (± 25 chars)? → Vary them.
  3. Does any sentence contain 2+ words from the AI-hallmark list in "Choose words carefully"? → Rewrite.
  4. Does the summary read like a paragraph from a cover letter? → Make it more telegraphic.

Use this HTML template

Use the template in cv-template.html. Replace the {{...}} placeholders with personalized content:

Placeholder Content
{{LANG}} en or es
{{PAGE_WIDTH}} 8.5in (letter) or 210mm (A4)
{{NAME}} (from profile.yml)
{{EMAIL}} (from profile.yml)
{{LINKEDIN_URL}} (from profile.yml)
{{LINKEDIN_DISPLAY}} (from profile.yml)
{{PORTFOLIO_URL}} (from profile.yml) (or /es depending on language)
{{PORTFOLIO_DISPLAY}} (from profile.yml) (or /es depending on language)
{{LOCATION}} (from profile.yml)
{{SECTION_SUMMARY}} Professional Summary / Resumen Profesional
{{SUMMARY_TEXT}} Personalized summary with keywords
{{SECTION_COMPETENCIES}} Core Competencies / Competencias Core
{{COMPETENCIES}} <span class="competency-tag">keyword</span> × 6-8
{{SECTION_EXPERIENCE}} Work Experience / Experiencia Laboral
{{EXPERIENCE}} HTML for each job with reordered bullets
{{SECTION_PROJECTS}} Projects / Proyectos
{{PROJECTS}} HTML for top 3-4 projects
{{SECTION_EDUCATION}} Education / Formación
{{EDUCATION}} HTML for education
{{SECTION_CERTIFICATIONS}} Certifications / Certificaciones
{{CERTIFICATIONS}} HTML for certifications
{{SECTION_SKILLS}} Skills / Competencias
{{SKILLS}} HTML for skills

Update the tracker post-generation

Update the tracker when the offer is already registered: change PDF from ❌ to ✅.