/resume-tailoring

name: resume-tailoring
description: Use when creating tailored resumes for job applications - researches company/role, creates optimized templates, conducts branching experience discovery to surface undocumented skills, and generates professional multi-format resumes from user's resume library while maintaining factual integrity

Resume Tailoring Skill

Overview

Generates high-quality, tailored resumes optimized for specific job descriptions while maintaining factual integrity. Builds resumes around the holistic person by surfacing undocumented experiences through conversational discovery.

**Core Principle:** Truth-preserving optimization - maximize fit while maintaining factual integrity. Never fabricate experience, but intelligently reframe and emphasize relevant aspects.

**Mission:** A person's ability to get a job should be based on their experiences and capabilities, not on their resume writing skills.

When to Use

Use this skill when:

• User provides a job description and wants a tailored resume
• User has multiple existing resumes in markdown format
• User wants to optimize their application for a specific role/company
• User needs help surfacing and articulating undocumented experiences

**DO NOT use for:**

• Generic resume writing from scratch (user needs existing resume library)
• Cover letters (different skill)
• LinkedIn profile optimization (different skill)

Quick Start

**Required from user:** 1. Job description (text or URL) 2. Resume library location (defaults to `resumes/` in current directory)

**Workflow:** 1. Build library from existing resumes 2. Research company/role 3. Create template (with user checkpoint) 4. Optional: Branching experience discovery 5. Match content with confidence scoring 6. Generate MD + DOCX + PDF + Report 7. User review → Optional library update

Implementation

See supporting files:

• `research-prompts.md` - Structured prompts for company/role research
• `matching-strategies.md` - Content matching algorithms and scoring
• `branching-questions.md` - Experience discovery conversation patterns

Workflow Details

Multi-Job Detection

**Triggers when user provides:**

• Multiple JD URLs (comma or newline separated)
• Phrases: "multiple jobs", "several positions", "batch", "3 jobs"
• List of companies/roles: "Microsoft PM, Google TPM, AWS PM"

**Detection Logic:**

# Pseudo-code
def detect_multi_job(user_input):
    indicators = [
        len(extract_urls(user_input)) > 1,
        any(phrase in user_input.lower() for phrase in
            ["multiple jobs", "several positions", "batch of", "3 jobs", "5 jobs"]),
        count_company_mentions(user_input) > 1
    ]
    return any(indicators)

**If detected:**

"I see you have multiple job applications. Would you like to use
multi-job mode?

BENEFITS:
- Shared experience discovery (faster - ask questions once for all jobs)
- Batch processing with progress tracking
- Incremental additions (add more jobs later)

TIME COMPARISON (3 similar jobs):
- Sequential single-job: ~45 minutes (15 min × 3)
- Multi-job mode: ~40 minutes (15 min discovery + 8 min per job)

Use multi-job mode? (Y/N)"

**If user confirms Y:**

• Use multi-job workflow (see multi-job-workflow.md)

**If user confirms N or single job detected:**

• Use existing single-job workflow (Phase 0 onwards)

**Backward Compatibility:** Single-job workflow completely unchanged.

**Multi-Job Workflow:**

When multi-job mode is activated, see `multi-job-workflow.md` for complete workflow.

**High-Level Multi-Job Process:**

┌─────────────────────────────────────────────────────────────┐
│ PHASE 0: Intake & Batch Initialization                      │
│ - Collect 3-5 job descriptions                              │
│ - Initialize batch structure                                │
│ - Run library initialization (once)                         │
└─────────────────────────────────────────────────────────────┘
                           ↓
┌─────────────────────────────────────────────────────────────┐
│ PHASE 1: Aggregate Gap Analysis                            │
│ - Extract requirements from all JDs                         │
│ - Cross-reference against library                           │
│ - Build unified gap map (deduplicate)                       │
│ - Prioritize: Critical → Important → Job-specific          │
└─────────────────────────────────────────────────────────────┘
                           ↓
┌─────────────────────────────────────────────────────────────┐
│ PHASE 2: Shared Experience Discovery                       │
│ - Single branching interview covering ALL gaps              │
│ - Multi-job context for each question                       │
│ - Tag experiences with job relevance                        │
│ - Enrich library with discoveries                           │
└─────────────────────────────────────────────────────────────┘
                           ↓
┌─────────────────────────────────────────────────────────────┐
│ PHASE 3: Per-Job Processing (Sequential)                   │
│ For each job:                                               │
│   ├─ Research (company + role benchmarking)                 │
│   ├─ Template generation                                    │
│   ├─ Content matching (uses enriched library)              │
│   └─ Generation (MD + DOCX + Report)                        │
│ Interactive or Express mode                                 │
└─────────────────────────────────────────────────────────────┘
                           ↓
┌─────────────────────────────────────────────────────────────┐
│ PHASE 4: Batch Finalization                                │
│ - Generate batch summary                                    │
│ - User reviews all resumes together                         │
│ - Approve/revise individual or batch