# Resume Tailoring

> 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

- **Type:** Skill
- **Install:** `agentstack add skill-varunr89-resume-tailoring-skill-resume-tailoring`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [varunr89](https://agentstack.voostack.com/s/varunr89)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [varunr89](https://github.com/varunr89)
- **Source:** https://github.com/varunr89/resume-tailoring-skill/tree/master/skills/resume-tailoring

## Install

```sh
agentstack add skill-varunr89-resume-tailoring-skill-resume-tailoring
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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:**

```python
# 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
   ```

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:**
   ```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"
     }
   }
   ```

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 for matching

**Code pattern:**
```python
# 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)

2. **Use industry-standard terminology:**
   - "Scientist III" → "Senior Research Scientist" (clearer seniority)
   - "Program Coordinator" → "Project Manager" (standard term)

3. **Add specialization when truthful:**
   - "Engineer" → "ML Engineer" (if ML work substantial)
   - "Researcher" → "Computational Ecologist" (if computational methods)

4. **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:**

```markdown
## Professional Summary
[GUIDANCE: {X} sentences emphasizing {themes from success profile}]
[REQUIRED ELEMENTS: {keywords from JD}]

## Key Skills
[STRUCTURE: {2-4 categories based on JD structure}]
[SOURCE: Extract from library matching success profile]

## Professional Experience

### [ROLE 1 - Most Recent/Relevant]
[CONSOLIDATION: {merge X positions OR keep separate}]
[TITLE OPTIONS:
  A: {emphasize aspect 1}
  B: {emphasize aspect 2}
  Recommended: {option with rationale}]
[BULLET ALLOCATION: {N bullets based on relevance + recency}]
[GUIDANCE: Emphasize {themes}, look for {experience types}]

Bullet 1: [SEEKING: {requirement type}]
Bullet 2: [SEEKING: {requirement type}]
...

### [ROLE 2]
...

## Education
[PLACEMENT: {top if required/recent, bottom if experience-heavy}]

## [Optional Sections]
[INCLUDE IF: {criteria from success profile}]
```

**Checkpoint:**
```
Present template to user:

"Here's the optimized resume structure for this role:

STRUCTURE:
{Section order and rationale}

ROLE CONSOLIDATION:
{Decisions with options}

TITLE REFRAMING:
{Proposed titles with alternatives}

BULLET ALLOCATION:
Role 1: {N} bullets (most relevant)
Role 2: {N} bullets
...

Does this structure work? Any adjustments to:
- Role consolidation?
- Title reframing?
- Bullet allocation?"

Wait for user approval before proceeding.
```

**Output:** Approved template skeleton with guidance for each section

### Phase 2.5: Experience Discovery (OPTIONAL)

**Goal:** Surface undocumented experiences through conversational discovery

**When to trigger:**
```
After template approval, if gaps identified:

"I've identified {N} gaps or areas where we have weak matches:
- {Gap 1}: {Current confidence}
- {Gap 2}: {Current confidence}
...

Would you like to do a structured brainstorming session to surface
any experiences you haven't documented yet?

This typically takes 10-15 minutes and often uncovers valuable content."

User can accept or skip.
```

**Branching Interview Process:**

**Approach:** Conversational with follow-up questions based on answers

**For each gap, conduct branching dialogue (see branching-questions.md):**

1. **Start with open probe:**
   - Technical gap: "Have you worked with {skill}?"
   - Soft skill gap: "Tell me about times you've {demonstrated_skill}"
   - Recent work: "What have you worked on recently?"

2. **Branch based on answer:**
   - YES/Strong → Deep dive (scale, challenges, metrics)
   - INDIRECT → Explore role and transferability
   - ADJACENT → Explore related experience
   - PERSONAL → Assess recency and substance
   - NO → Try broader category or move on

3. **Follow-up systematically:**
   - Ask "what," "how," "why" to get details
   - Quantify: "Any metrics?"
   - Contextualize: "Was this production?"
   - Validate: "Does this address the gap?"

4. **Capture immediately:**
   - Document experience as shared
   - Ask clarifying questions (dates, scope, impact)
   - Help articulate as resume bullet
   - Tag which gap(s) it addresses

**Capture Structure:**
```markdown
## Newly Discovered Experiences

### Experience 1: {Brief description}
- Context: {Where/when}
- Scope: {Scale, duration, impact}
- Addresses: {Which gaps}
- Bullet draft: "{Achievement-focused bullet}"
- Confidence: {How well fills gap - percentage}

### Experience 2: ...
```

**Integration Options:**

After discovery session:
```
"Great! I captured {N} new experiences. For each one:

1. ADD TO CURRENT RESUME - Integrate now
2. ADD TO LIBRARY ONLY - Save for future, not n

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [varunr89](https://github.com/varunr89)
- **Source:** [varunr89/resume-tailoring-skill](https://github.com/varunr89/resume-tailoring-skill)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-varunr89-resume-tailoring-skill-resume-tailoring
- Seller: https://agentstack.voostack.com/s/varunr89
- Browse the marketplace: https://agentstack.voostack.com/browse

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