initial public release

modes/_shared.md

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+# System Context -- career-ops
+
+<!-- ============================================================
+     THIS FILE IS AUTO-UPDATABLE. Don't put personal data here.
+     
+     Your customizations go in modes/_profile.md (never auto-updated).
+     This file contains system rules, scoring logic, and tool config
+     that improve with each career-ops release.
+     ============================================================ -->
+
+## Sources of Truth
+
+| File | Path | When |
+|------|------|------|
+| cv.md | `cv.md` (project root) | ALWAYS |
+| article-digest.md | `article-digest.md` (if exists) | ALWAYS (detailed proof points) |
+| profile.yml | `config/profile.yml` | ALWAYS (candidate identity and targets) |
+| _profile.md | `modes/_profile.md` | ALWAYS (user archetypes, narrative, negotiation) |
+
+**RULE: NEVER hardcode metrics from proof points.** Read them from cv.md + article-digest.md at evaluation time.
+**RULE: For article/project metrics, article-digest.md takes precedence over cv.md.**
+**RULE: Read _profile.md AFTER this file. User customizations in _profile.md override defaults here.**
+
+---
+
+## Scoring System
+
+The evaluation uses 6 blocks (A-F) with a global score of 1-5:
+
+| Dimension | What it measures |
+|-----------|-----------------|
+| Match con CV | Skills, experience, proof points alignment |
+| North Star alignment | How well the role fits the user's target archetypes (from _profile.md) |
+| Comp | Salary vs market (5=top quartile, 1=well below) |
+| Cultural signals | Company culture, growth, stability, remote policy |
+| Red flags | Blockers, warnings (negative adjustments) |
+| **Global** | Weighted average of above |
+
+**Score interpretation:**
+- 4.5+ → Strong match, recommend applying immediately
+- 4.0-4.4 → Good match, worth applying
+- 3.5-3.9 → Decent but not ideal, apply only if specific reason
+- Below 3.5 → Recommend against applying (see Ethical Use in CLAUDE.md)
+
+## Posting Legitimacy (Block G)
+
+Block G assesses whether a posting is likely a real, active opening. It does NOT affect the 1-5 global score -- it is a separate qualitative assessment.
+
+**Three tiers:**
+- **High Confidence** -- Real, active opening (most signals positive)
+- **Proceed with Caution** -- Mixed signals, worth noting (some concerns)
+- **Suspicious** -- Multiple ghost indicators, user should investigate first
+
+**Key signals (weighted by reliability):**
+
+| Signal | Source | Reliability | Notes |
+|--------|--------|-------------|-------|
+| Posting age | Page snapshot | High | Under 30d=good, 30-60d=mixed, 60d+=concerning (adjusted for role type) |
+| Apply button active | Page snapshot | High | Direct observable fact |
+| Tech specificity in JD | JD text | Medium | Generic JDs correlate with ghost postings but also with poor writing |
+| Requirements realism | JD text | Medium | Contradictions are a strong signal, vagueness is weaker |
+| Recent layoff news | WebSearch | Medium | Must consider department, timing, and company size |
+| Reposting pattern | scan-history.tsv | Medium | Same role reposted 2+ times in 90 days is concerning |
+| Salary transparency | JD text | Low | Jurisdiction-dependent, many legitimate reasons to omit |
+| Role-company fit | Qualitative | Low | Subjective, use only as supporting signal |
+
+**Ethical framing (MANDATORY):**
+- This helps users prioritize time on real opportunities
+- NEVER present findings as accusations of dishonesty
+- Present signals and let the user decide
+- Always note legitimate explanations for concerning signals
+
+## Archetype Detection
+
+Classify every offer into one of these types (or hybrid of 2):
+
+| Archetype | Key signals in JD |
+|-----------|-------------------|
+| AI Platform / LLMOps | "observability", "evals", "pipelines", "monitoring", "reliability" |
+| Agentic / Automation | "agent", "HITL", "orchestration", "workflow", "multi-agent" |
+| Technical AI PM | "PRD", "roadmap", "discovery", "stakeholder", "product manager" |
+| AI Solutions Architect | "architecture", "enterprise", "integration", "design", "systems" |
+| AI Forward Deployed | "client-facing", "deploy", "prototype", "fast delivery", "field" |
+| AI Transformation | "change management", "adoption", "enablement", "transformation" |
+
+After detecting archetype, read `modes/_profile.md` for the user's specific framing and proof points for that archetype.
+
+## Global Rules
+
+### NEVER
+
+1. Invent experience or metrics
+2. Modify cv.md or portfolio files
+3. Submit applications on behalf of the candidate
+4. Share phone number in generated messages
+5. Recommend comp below market rate
+6. Generate a PDF without reading the JD first
+7. Use corporate-speak
+8. Ignore the tracker (every evaluated offer gets registered)
+
+### ALWAYS
+
+0. **Cover letter:** If the form allows it, ALWAYS include one. Same visual design as CV. JD quotes mapped to proof points. 1 page max.
+1. Read cv.md, _profile.md, and article-digest.md (if exists) before evaluating
+1b. **First evaluation of each session:** Run `node cv-sync-check.mjs`. If warnings, notify user.
+2. Detect the role archetype and adapt framing per _profile.md
+3. Cite exact lines from CV when matching
+4. Use WebSearch for comp and company data
+5. Register in tracker after evaluating
+6. Generate content in the language of the JD (EN default)
+7. Be direct and actionable -- no fluff
+8. Native tech English for generated text. Short sentences, action verbs, no passive voice.
+8b. Case study URLs in PDF Professional Summary (recruiter may only read this).
+9. **Tracker additions as TSV** -- NEVER edit applications.md directly. Write TSV in `batch/tracker-additions/`.
+10. **Include `**URL:**` in every report header.**
+
+### Tools
+
+| Tool | Use |
+|------|-----|
+| WebSearch | Comp research, trends, company culture, LinkedIn contacts, fallback for JDs |
+| WebFetch | Fallback for extracting JDs from static pages |
+| Playwright | Verify offers (browser_navigate + browser_snapshot). **NEVER 2+ agents with Playwright in parallel.** |
+| Read | cv.md, _profile.md, article-digest.md, cv-template.html |
+| Write | Temporary HTML for PDF, applications.md, reports .md |
+| Edit | Update tracker |
+| Canva MCP | Optional visual CV generation. Duplicate base design, edit text, export PDF. Requires `canva_resume_design_id` in profile.yml. |
+| Bash | `node generate-pdf.mjs` |
+
+### Time-to-offer priority
+- Working demo + metrics > perfection
+- Apply sooner > learn more
+- 80/20 approach, timebox everything
+
+---
+
+## Professional Writing & ATS Compatibility
+
+These rules apply to ALL generated text that ends up in candidate-facing documents: PDF summaries, bullets, cover letters, form answers, LinkedIn messages. They do NOT apply to internal evaluation reports.
+
+### Avoid cliché phrases
+- "passionate about" / "results-oriented" / "proven track record"
+- "leveraged" (use "used" or name the tool)
+- "spearheaded" (use "led" or "ran")
+- "facilitated" (use "ran" or "set up")
+- "synergies" / "robust" / "seamless" / "cutting-edge" / "innovative"
+- "in today's fast-paced world"
+- "demonstrated ability to" / "best practices" (name the practice)
+
+### Unicode normalization for ATS
+`generate-pdf.mjs` automatically normalizes em-dashes, smart quotes, and zero-width characters to ASCII equivalents for maximum ATS compatibility. But avoid generating them in the first place.
+
+### Vary sentence structure
+- Don't start every bullet with the same verb
+- Mix sentence lengths (short. Then longer with context. Short again.)
+- Don't always use "X, Y, and Z" — sometimes two items, sometimes four
+
+### Prefer specifics over abstractions
+- "Cut p95 latency from 2.1s to 380ms" beats "improved performance"
+- "Postgres + pgvector for retrieval over 12k docs" beats "designed scalable RAG architecture"
+- Name tools, projects, and customers when allowed

modes/oferta.md

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+# Modo: oferta — Evaluación Completa A-G
+
+Cuando el candidato pega una oferta (texto o URL), entregar SIEMPRE los 7 bloques (A-F evaluation + G legitimacy):
+
+## Paso 0 — Detección de Arquetipo
+
+Clasificar la oferta en uno de los 6 arquetipos (ver `_shared.md`). Si es híbrido, indicar los 2 más cercanos. Esto determina:
+- Qué proof points priorizar en bloque B
+- Cómo reescribir el summary en bloque E
+- Qué historias STAR preparar en bloque F
+
+## Bloque A — Resumen del Rol
+
+Tabla con:
+- Arquetipo detectado
+- Domain (platform/agentic/LLMOps/ML/enterprise)
+- Function (build/consult/manage/deploy)
+- Seniority
+- Remote (full/hybrid/onsite)
+- Team size (si se menciona)
+- TL;DR en 1 frase
+
+## Bloque B — Match con CV
+
+Lee `cv.md`. Crea tabla con cada requisito del JD mapeado a líneas exactas del CV.
+
+**Adaptado al arquetipo:**
+- Si FDE → priorizar proof points de delivery rápida y client-facing
+- Si SA → priorizar diseño de sistemas e integrations
+- Si PM → priorizar product discovery y métricas
+- Si LLMOps → priorizar evals, observability, pipelines
+- Si Agentic → priorizar multi-agent, HITL, orchestration
+- Si Transformation → priorizar change management, adoption, scaling
+
+Sección de **gaps** con estrategia de mitigación para cada uno. Para cada gap:
+1. ¿Es un hard blocker o un nice-to-have?
+2. ¿Puede el candidato demostrar experiencia adyacente?
+3. ¿Hay un proyecto portfolio que cubra este gap?
+4. Plan de mitigación concreto (frase para cover letter, proyecto rápido, etc.)
+
+## Bloque C — Nivel y Estrategia
+
+1. **Nivel detectado** en el JD vs **nivel natural del candidato para ese arquetipo**
+2. **Plan "vender senior sin mentir"**: frases específicas adaptadas al arquetipo, logros concretos a destacar, cómo posicionar la experiencia de founder como ventaja
+3. **Plan "si me downlevelan"**: aceptar si comp es justa, negociar review a 6 meses, criterios de promoción claros
+
+## Bloque D — Comp y Demanda
+
+Usar WebSearch para:
+- Salarios actuales del rol (Glassdoor, Levels.fyi, Blind)
+- Reputación de compensación de la empresa
+- Tendencia de demanda del rol
+
+Tabla con datos y fuentes citadas. Si no hay datos, decirlo en vez de inventar.
+
+## Bloque E — Plan de Personalización
+
+| # | Sección | Estado actual | Cambio propuesto | Por qué |
+|---|---------|---------------|------------------|---------|
+| 1 | Summary | ... | ... | ... |
+| ... | ... | ... | ... | ... |
+
+Top 5 cambios al CV + Top 5 cambios a LinkedIn para maximizar match.
+
+## Bloque F — Plan de Entrevistas
+
+6-10 historias STAR+R mapeadas a requisitos del JD (STAR + **Reflection**):
+
+| # | Requisito del JD | Historia STAR+R | S | T | A | R | Reflection |
+|---|-----------------|-----------------|---|---|---|---|------------|
+
+The **Reflection** column captures what was learned or what would be done differently. This signals seniority — junior candidates describe what happened, senior candidates extract lessons.
+
+**Story Bank:** If `interview-prep/story-bank.md` exists, check if any of these stories are already there. If not, append new ones. Over time this builds a reusable bank of 5-10 master stories that can be adapted to any interview question.
+
+**Seleccionadas y enmarcadas según el arquetipo:**
+- FDE → enfatizar velocidad de entrega y client-facing
+- SA → enfatizar decisiones de arquitectura
+- PM → enfatizar discovery y trade-offs
+- LLMOps → enfatizar métricas, evals, production hardening
+- Agentic → enfatizar orchestration, error handling, HITL
+- Transformation → enfatizar adopción, cambio organizacional
+
+Incluir también:
+- 1 case study recomendado (cuál de sus proyectos presentar y cómo)
+- Preguntas red-flag y cómo responderlas (ej: "¿por qué vendiste tu empresa?", "¿tienes equipo de reports?")
+
+## Bloque G — Posting Legitimacy
+
+Analyze the job posting for signals that indicate whether this is a real, active opening. This helps the user prioritize their effort on opportunities most likely to result in a hiring process.
+
+**Ethical framing:** Present observations, not accusations. Every signal has legitimate explanations. The user decides how to weigh them.
+
+### Signals to analyze (in order):
+
+**1. Posting Freshness** (from Playwright snapshot, already captured in Paso 0):
+- Date posted or "X days ago" -- extract from page
+- Apply button state (active / closed / missing / redirects to generic page)
+- If URL redirected to generic careers page, note it
+
+**2. Description Quality** (from JD text):
+- Does it name specific technologies, frameworks, tools?
+- Does it mention team size, reporting structure, or org context?
+- Are requirements realistic? (years of experience vs technology age)
+- Is there a clear scope for the first 6-12 months?
+- Is salary/compensation mentioned?
+- What ratio of the JD is role-specific vs generic boilerplate?
+- Any internal contradictions? (entry-level title + staff requirements, etc.)
+
+**3. Company Hiring Signals** (2-3 WebSearch queries, combine with Block D research):
+- Search: `"{company}" layoffs {year}` -- note date, scale, departments
+- Search: `"{company}" hiring freeze {year}` -- note any announcements
+- If layoffs found: are they in the same department as this role?
+
+**4. Reposting Detection** (from scan-history.tsv):
+- Check if company + similar role title appeared before with a different URL
+- Note how many times and over what period
+
+**5. Role Market Context** (qualitative, no additional queries):
+- Is this a common role that typically fills in 4-6 weeks?
+- Does the role make sense for this company's business?
+- Is the seniority level one that legitimately takes longer to fill?
+
+### Output format:
+
+**Assessment:** One of three tiers:
+- **High Confidence** -- Multiple signals suggest a real, active opening
+- **Proceed with Caution** -- Mixed signals worth noting
+- **Suspicious** -- Multiple ghost job indicators, investigate before investing time
+
+**Signals table:** Each signal observed with its finding and weight (Positive / Neutral / Concerning).
+
+**Context Notes:** Any caveats (niche role, government job, evergreen position, etc.) that explain potentially concerning signals.
+
+### Edge case handling:
+- **Government/academic postings:** Longer timelines are standard. Adjust thresholds (60-90 days is normal).
+- **Evergreen/continuous hire postings:** If the JD explicitly says "ongoing" or "rolling," note it as context -- this is not a ghost job, it is a pipeline role.
+- **Niche/executive roles:** Staff+, VP, Director, or highly specialized roles legitimately stay open for months. Adjust age thresholds accordingly.
+- **Startup / pre-revenue:** Early-stage companies may have vague JDs because the role is genuinely undefined. Weight description vagueness less heavily.
+- **No date available:** If posting age cannot be determined and no other signals are concerning, default to "Proceed with Caution" with a note that limited data was available. NEVER default to "Suspicious" without evidence.
+- **Recruiter-sourced (no public posting):** Freshness signals unavailable. Note that active recruiter contact is itself a positive legitimacy signal.
+
+---
+
+## Post-evaluación
+
+**SIEMPRE** después de generar los bloques A-G:
+
+### 1. Guardar report .md
+
+Guardar evaluación completa en `reports/{###}-{company-slug}-{YYYY-MM-DD}.md`.
+
+- `{###}` = siguiente número secuencial (3 dígitos, zero-padded)
+- `{company-slug}` = nombre de empresa en lowercase, sin espacios (usar guiones)
+- `{YYYY-MM-DD}` = fecha actual
+
+**Formato del report:**
+
+```markdown
+# Evaluación: {Empresa} — {Rol}
+
+**Fecha:** {YYYY-MM-DD}
+**Arquetipo:** {detectado}
+**Score:** {X/5}
+**Legitimacy:** {High Confidence | Proceed with Caution | Suspicious}
+**PDF:** {ruta o pendiente}
+
+---
+
+## A) Resumen del Rol
+(contenido completo del bloque A)
+
+## B) Match con CV
+(contenido completo del bloque B)
+
+## C) Nivel y Estrategia
+(contenido completo del bloque C)
+
+## D) Comp y Demanda
+(contenido completo del bloque D)
+
+## E) Plan de Personalización
+(contenido completo del bloque E)
+
+## F) Plan de Entrevistas
+(contenido completo del bloque F)
+
+## G) Posting Legitimacy
+(contenido completo del bloque G)
+
+## H) Draft Application Answers
+(solo si score >= 4.5 — borradores de respuestas para el formulario de aplicación)
+
+---
+
+## Keywords extraídas
+(lista de 15-20 keywords del JD para ATS optimization)
+```
+
+### 2. Registrar en tracker
+
+**SIEMPRE** registrar en `data/applications.md`:
+- Siguiente número secuencial
+- Fecha actual
+- Empresa
+- Rol
+- Score: promedio de match (1-5)
+- Estado: `Evaluada`
+- PDF: ❌ (o ✅ si auto-pipeline generó PDF)
+- Report: link relativo al report .md (ej: `[001](reports/001-company-2026-01-01.md)`)
+
+**Formato del tracker:**
+
+```markdown
+| # | Fecha | Empresa | Rol | Score | Estado | PDF | Report |
+```

modes/pdf.md

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+# Modo: pdf — Generación de PDF ATS-Optimizado
+
+## Pipeline completo
+
+1. Lee `cv.md` como fuentes de verdad
+2. Pide al usuario el JD si no está en contexto (texto o URL)
+3. Extrae 15-20 keywords del JD
+4. Detecta idioma del JD → idioma del CV (EN default)
+5. Detecta ubicación empresa → formato papel:
+   - US/Canada → `letter`
+   - Resto del mundo → `a4`
+6. Detecta arquetipo del rol → adapta framing
+7. Reescribe Professional Summary inyectando keywords del JD + exit narrative bridge ("Built and sold a business. Now applying systems thinking to [domain del JD].")
+8. Selecciona top 3-4 proyectos más relevantes para la oferta
+9. Reordena bullets de experiencia por relevancia al JD
+10. Construye competency grid desde requisitos del JD (6-8 keyword phrases)
+11. Inyecta keywords naturalmente en logros existentes (NUNCA inventa)
+12. Genera HTML completo desde template + contenido personalizado
+13. Lee `name` de `config/profile.yml` → normaliza a kebab-case lowercase (e.g. "John Doe" → "john-doe") → `{candidate}`
+14. Escribe HTML a `/tmp/cv-{candidate}-{company}.html`
+15. Ejecuta: `node generate-pdf.mjs /tmp/cv-{candidate}-{company}.html output/cv-{candidate}-{company}-{YYYY-MM-DD}.pdf --format={letter|a4}`
+15. Reporta: ruta del PDF, nº páginas, % cobertura de keywords
+
+## Reglas ATS (parseo limpio)
+
+- Layout single-column (sin sidebars, sin columnas paralelas)
+- Headers estándar: "Professional Summary", "Work Experience", "Education", "Skills", "Certifications", "Projects"
+- Sin texto en imágenes/SVGs
+- Sin info crítica en headers/footers del PDF (ATS los ignora)
+- UTF-8, texto seleccionable (no rasterizado)
+- Sin tablas anidadas
+- Keywords del JD distribuidas: Summary (top 5), primer bullet de cada rol, Skills section
+
+## Diseño del PDF
+
+- **Fonts**: Space Grotesk (headings, 600-700) + DM Sans (body, 400-500)
+- **Fonts self-hosted**: `fonts/`
+- **Header**: nombre en Space Grotesk 24px bold + línea gradiente `linear-gradient(to right, hsl(187,74%,32%), hsl(270,70%,45%))` 2px + fila de contacto
+- **Section headers**: Space Grotesk 13px, uppercase, letter-spacing 0.05em, color cyan primary
+- **Body**: DM Sans 11px, line-height 1.5
+- **Company names**: color accent purple `hsl(270,70%,45%)`
+- **Márgenes**: 0.6in
+- **Background**: blanco puro
+
+## Orden de secciones (optimizado "6-second recruiter scan")
+
+1. Header (nombre grande, gradiente, contacto, link portfolio)
+2. Professional Summary (3-4 líneas, keyword-dense)
+3. Core Competencies (6-8 keyword phrases en flex-grid)
+4. Work Experience (cronológico inverso)
+5. Projects (top 3-4 más relevantes)
+6. Education & Certifications
+7. Skills (idiomas + técnicos)
+
+## Estrategia de keyword injection (ético, basado en verdad)
+
+Ejemplos de reformulación legítima:
+- JD dice "RAG pipelines" y CV dice "LLM workflows with retrieval" → cambiar a "RAG pipeline design and LLM orchestration workflows"
+- JD dice "MLOps" y CV dice "observability, evals, error handling" → cambiar a "MLOps and observability: evals, error handling, cost monitoring"
+- JD dice "stakeholder management" y CV dice "collaborated with team" → cambiar a "stakeholder management across engineering, operations, and business"
+
+**NUNCA añadir skills que el candidato no tiene. Solo reformular experiencia real con el vocabulario exacto del JD.**
+
+## Template HTML
+
+Usar el template en `cv-template.html`. Reemplazar los placeholders `{{...}}` con contenido personalizado:
+
+| Placeholder | Contenido |
+|-------------|-----------|
+| `{{LANG}}` | `en` o `es` |
+| `{{PAGE_WIDTH}}` | `8.5in` (letter) o `210mm` (A4) |
+| `{{NAME}}` | (from profile.yml) |
+| `{{PHONE}}` | (from profile.yml — include with its separator only when `profile.yml` has a non-empty `phone` value; omit both `<span>` and `<span class="separator">` otherwise) |
+| `{{EMAIL}}` | (from profile.yml) |
+| `{{LINKEDIN_URL}}` | [from profile.yml] |
+| `{{LINKEDIN_DISPLAY}}` | [from profile.yml] |
+| `{{PORTFOLIO_URL}}` | [from profile.yml] (o /es según idioma) |
+| `{{PORTFOLIO_DISPLAY}}` | [from profile.yml] (o /es según idioma) |
+| `{{LOCATION}}` | [from profile.yml] |
+| `{{SECTION_SUMMARY}}` | Professional Summary / Resumen Profesional |
+| `{{SUMMARY_TEXT}}` | Summary personalizado con keywords |
+| `{{SECTION_COMPETENCIES}}` | Core Competencies / Competencias Core |
+| `{{COMPETENCIES}}` | `<span class="competency-tag">keyword</span>` × 6-8 |
+| `{{SECTION_EXPERIENCE}}` | Work Experience / Experiencia Laboral |
+| `{{EXPERIENCE}}` | HTML de cada trabajo con bullets reordenados |
+| `{{SECTION_PROJECTS}}` | Projects / Proyectos |
+| `{{PROJECTS}}` | HTML de top 3-4 proyectos |
+| `{{SECTION_EDUCATION}}` | Education / Formación |
+| `{{EDUCATION}}` | HTML de educación |
+| `{{SECTION_CERTIFICATIONS}}` | Certifications / Certificaciones |
+| `{{CERTIFICATIONS}}` | HTML de certificaciones |
+| `{{SECTION_SKILLS}}` | Skills / Competencias |
+| `{{SKILLS}}` | HTML de skills |
+
+## Canva CV Generation (optional)
+
+If `config/profile.yml` has `canva_resume_design_id` set, offer the user a choice before generating:
+- **"HTML/PDF (fast, ATS-optimized)"** — existing flow above
+- **"Canva CV (visual, design-preserving)"** — new flow below
+
+If the user has no `canva_resume_design_id`, skip this prompt and use the HTML/PDF flow.
+
+### Canva workflow
+
+#### Step 1 — Duplicate the base design
+
+a. `export-design` the base design (using `canva_resume_design_id`) as PDF → get download URL
+b. `import-design-from-url` using that download URL → creates a new editable design (the duplicate)
+c. Note the new `design_id` for the duplicate
+
+#### Step 2 — Read the design structure
+
+a. `get-design-content` on the new design → returns all text elements (richtexts) with their content
+b. Map text elements to CV sections by content matching:
+   - Look for the candidate's name → header section
+   - Look for "Summary" or "Professional Summary" → summary section
+   - Look for company names from cv.md → experience sections
+   - Look for degree/school names → education section
+   - Look for skill keywords → skills section
+c. If mapping fails, show the user what was found and ask for guidance
+
+#### Step 3 — Generate tailored content
+
+Same content generation as the HTML flow (Steps 1-11 above):
+- Rewrite Professional Summary with JD keywords + exit narrative
+- Reorder experience bullets by JD relevance
+- Select top competencies from JD requirements
+- Inject keywords naturally (NEVER invent)
+
+**IMPORTANT — Character budget rule:** Each replacement text MUST be approximately the same length as the original text it replaces (within ±15% character count). If tailored content is longer, condense it. The Canva design has fixed-size text boxes — longer text causes overlapping with adjacent elements. Count the characters in each original element from Step 2 and enforce this budget when generating replacements.
+
+#### Step 4 — Apply edits
+
+a. `start-editing-transaction` on the duplicate design
+b. `perform-editing-operations` with `find_and_replace_text` for each section:
+   - Replace summary text with tailored summary
+   - Replace each experience bullet with reordered/rewritten bullets
+   - Replace competency/skills text with JD-matched terms
+   - Replace project descriptions with top relevant projects
+c. **Reflow layout after text replacement:**
+   After applying all text replacements, the text boxes auto-resize but neighboring elements stay in place. This causes uneven spacing between work experience sections. Fix this:
+   1. Read the updated element positions and dimensions from the `perform-editing-operations` response
+   2. For each work experience section (top to bottom), calculate where the bullets text box ends: `end_y = top + height`
+   3. The next section's header should start at `end_y + consistent_gap` (use the original gap from the template, typically ~30px)
+   4. Use `position_element` to move the next section's date, company name, role title, and bullets elements to maintain even spacing
+   5. Repeat for all work experience sections
+d. **Verify layout before commit:**
+   - `get-design-thumbnail` with the transaction_id and page_index=1
+   - Visually inspect the thumbnail for: text overlapping, uneven spacing, text cut off, text too small
+   - If issues remain, adjust with `position_element`, `resize_element`, or `format_text`
+   - Repeat until layout is clean
+d. Show the user the final preview and ask for approval
+e. `commit-editing-transaction` to save (ONLY after user approval)
+
+#### Step 5 — Export and download PDF
+
+a. `export-design` the duplicate as PDF (format: a4 or letter based on JD location)
+b. **IMMEDIATELY** download the PDF using Bash:
+   ```bash
+   curl -sL -o "output/cv-{candidate}-{company}-canva-{YYYY-MM-DD}.pdf" "{download_url}"
+   ```
+   The export URL is a pre-signed S3 link that expires in ~2 hours. Download it right away.
+c. Verify the download:
+   ```bash
+   file output/cv-{candidate}-{company}-canva-{YYYY-MM-DD}.pdf
+   ```
+   Must show "PDF document". If it shows XML or HTML, the URL expired — re-export and retry.
+d. Report: PDF path, file size, Canva design URL (for manual tweaking)
+
+#### Error handling
+
+- If `import-design-from-url` fails → fall back to HTML/PDF pipeline with message
+- If text elements can't be mapped → warn user, show what was found, ask for manual mapping
+- If `find_and_replace_text` finds no matches → try broader substring matching
+- Always provide the Canva design URL so the user can edit manually if auto-edit fails
+
+## Post-generación
+
+Actualizar tracker si la oferta ya está registrada: cambiar PDF de ❌ a ✅.