resume-tailoring
Installation
Copy the prompt below and send it to your AI assistant (e.g. Claude Code) — it will follow the instructions and install automatically.
Install the "resume-tailoring" skill by following the instructions at https://skill123.me/install/resume-tailoring.
Run in your terminal — downloads and installs to ~/.claude/skills/.
curl -fsSL https://skill123.me/install/resume-tailoring.sh | bash
Download the zip and extract it into your skills directory (e.g. ~/.claude/skills/), then restart your session.
⬇ Download v1.0.1 · 32 KBNot live-tested yet — this skill is rated on static analysis only.
About this skill
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
Documentation
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 researchmatching-strategies.md- Content matching algorithms and scoringbranching-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 ad
View full documentation
ditions (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
2. **Scan for markdown files:**
Use Glob tool: pattern="*.md" path={resume_directory}
Count files found
Announce: "Building resume library... found {N} resumes"
3. **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
4. **Build experience database structure:**
{
"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"
}
}
5. **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 f
Score breakdown
Assessment methodology
Version history
Bundle the four supporting reference files kept at the repository root (research-prompts, matching-strategies, branching-questions, multi-job-workflow).
Imported from varunr89/resume-tailoring-skill@master