varunr89/resume-tailoring-skill/resume-tailoring

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                        │
│ - Update library with approved resumes                      │
└─────────────────────────────────────────────────────────────┘

Time Savings:

• 3 jobs: ~40 min (vs 45 min sequential) = 11% savings
• 5 jobs: ~55 min (vs 75 min sequential) = 27% savings

Quality: Same depth as single-job workflow (research, matching, generation)

See `multi-job-workflow.md` for complete implementation details.

Phase 0: Library Initialization

Always runs first - builds fresh resume database

Process:

1. Locate resume directory:

`` User provides path OR default to ./resumes/ Validate directory exists ``

1. Scan for markdown files:

`` Use Glob tool: pattern="*.md" path={resume_directory} Count files found Announce: "Building resume library... found {N} resumes" ``

1. Parse each resume:

For each resume file:

• Use Read tool to load content
• Extract sections: roles, bullets, skills, education
• Identify patterns: bullet structure, length, formatting

1. Build experience database structure:

``json { "roles": [ { "role_id": "company_title_year", "company": "Company Name", "title": "Job Title", "dates": "YYYY-YYYY", "description": "Role summary", "bullets": [ { "text": "Full bullet text", "themes": ["leadership", "technical"], "metrics": ["17x improvement", "$3M revenue"], "keywords": ["cross-functional", "program"], "source_resumes": ["resume1.md"] } ] } ], "skills": { "technical": ["Python", "Kusto", "AI/ML"], "product": ["Roadmap", "Strategy"], "leadership": ["Stakeholder mgmt"] }, "education": [...], "user_preferences": { "typical_length": "1-page|2-page", "section_order": ["summary", "experience", "education"], "bullet_style": "pattern" } } ``

1. Tag content automatically:

• Themes: Scan for keywords (leadership, technical, analytics, etc.)
• Metrics: Extract numbers, percentages, dollar amounts
• Keywords: Frequent technical terms, action verbs

Output: In-memory database ready for matching

Code pattern:

# Pseudo-code for reference
library = {
    "roles": [],
    "skills": {},
    "education": []
}
for resume_file in glob("resumes/*.md"):
    content = read(resume_file)
    roles = extract_roles(content)
    for role in roles:
        role["bullets"] = tag_bullets(role["bullets"])
        library["roles"].append(role)
return library

Phase 1: Research Phase

Goal: Build comprehensive "success profile" beyond just the job description

Inputs:

• Job description (text or URL from user)
• Optional: Company name if not in JD

Process:

1.1 Job Description Parsing:

Use research-prompts.md JD parsing template
Extract: requirements, keywords, implicit preferences, red flags, role archetype

1.2 Company Research:

WebSearch queries:
- "{company} mission values culture"
- "{company} engineering blog"
- "{company} recent news"
 
Synthesize: mission, values, business model, stage

1.3 Role Benchmarking:

WebSearch: "site:linkedin.com {job_title} {company}"
WebFetch: Top 3-5 profiles
Analyze: common backgrounds, skills, terminology
 
If sparse results, try similar companies

1.4 Success Profile Synthesis:

Combine all research into structured profile (see research-prompts.md template)
 
Include:
- Core requirements (must-have)
- Valued capabilities (nice-to-have)
- Cultural fit signals
- Narrative themes
- Terminology map (user's background → their language)
- Risk factors + mitigations

Checkpoint:

Present success profile to user:
 
"Based on my research, here's what makes candidates successful for this role:
 
{SUCCESS_PROFILE_SUMMARY}
 
Key findings:
- {Finding 1}
- {Finding 2}
- {Finding 3}
 
Does this match your understanding? Any adjustments?"
 
Wait for user confirmation before proceeding.

Output: Validated success profile document

Phase 2: Template Generation

Goal: Create resume structure optimized for this specific role

Inputs:

• Success profile (from Phase 1)
• User's resume library (from Phase 0)

Process:

2.1 Analyze User's Resume Library:

Extract from library:
- All roles, titles, companies, date ranges
- Role archetypes (technical contributor, manager, researcher, specialist)
- Experience clusters (what domains/skills appear frequently)
- Career progression and narrative

2.2 Role Consolidation Decision:

When to consolidate:

• Same company, similar responsibilities
• Target role values continuity over granular progression
• Combined narrative stronger than separate
• Page space constrained

When to keep separate:

• Different companies (ALWAYS separate)
• Dramatically different responsibilities that both matter
• Target role values specific progression story
• One position has significantly more relevant experience

Decision template:

For {Company} with {N} positions:
 
OPTION A (Consolidated):
Title: "{Combined_Title}"
Dates: "{First_Start} - {Last_End}"
Rationale: {Why consolidation makes sense}
 
OPTION B (Separate):
Position 1: "{Title}" ({Dates})
Position 2: "{Title}" ({Dates})
Rationale: {Why separate makes sense}
 
RECOMMENDED: Option {A/B} because {reasoning}

2.3 Title Reframing Principles:

Core rule: Stay truthful to what you did, emphasize aspect most relevant to target

Strategies:

1. Emphasize different aspects:

• "Graduate Researcher" → "Research Software Engineer" (if coding-heavy)
• "Data Science Lead" → "Technical Program Manager" (if leadership)

1. Use industry-standard terminology:

• "Scientist III" → "Senior Research Scientist" (clearer seniority)
• "Program Coordinator" → "Project Manager" (standard term)

1. Add specialization when truthful:

• "Engineer" → "ML Engineer" (if ML work substantial)
• "Researcher" → "Computational Ecologist" (if computational methods)

1. Adjust seniority indicators:

• "Lead" vs "Senior" vs "Staff" based on scope

Constraints:

• NEVER claim work you didn't do
• NEVER inflate seniority beyond defensible
• Company name and dates MUST be exact
• Core responsibilities MUST be accurate

2.4 Generate Template Structure: