Resume Tailoring Skill

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                        │
│ - 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 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)

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:

## 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:

## 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 needed here
3. REFINE FURTHER - Think more about articulation
4. DISCARD - Not relevant enough

Let me know for each experience."

Important Notes:

• Keep truthfulness bar high - help articulate, NEVER fabricate
• Focus on gaps and weak matches, not strong areas
• Time-box if needed (10-15 minutes typical)
• User can skip entirely if confident in library
• Recognize when to move on - don't exhaust user

Output: New experiences integrated into library, ready for matching

Phase 3: Assembly Phase

Goal: Fill approved template with best-matching content, with transparent scoring

Inputs:

• Approved template (from Phase 2)
• Resume library + discovered experiences (from Phase 0 + 2.5)
• Success profile (from Phase 1)

Process:

3.1 For Each Template Slot:

1. Extract all candidate bullets from library

   • All bullets from library database
   • All newly discovered experiences
   • Include source resume for each

2. Score each candidate (see matching-strategies.md)

   • Direct match (40%): Keywords, domain, technology, outcome
   • Transferable (30%): Same capability, different context
   • Adjacent (20%): Related tools, methods, problem space
   • Impact (10%): Achievement type alignment

   Overall = (Direct × 0.4) + (Transfer × 0.3) + (Adjacent × 0.2) + (Impact × 0.1)

3. Rank candidates by score

   • Sort high to low
   • Group by confidence band:
     • 90-100%: DIRECT
     • 75-89%: TRANSFERABLE
     • 60-74%: ADJACENT
     • <60%: WEAK/GAP

4. Present top 3 matches with analysis:

   TEMPLATE SLOT: {Role} - Bullet {N}
   SEEKING: {Requirement description}
   
   MATCHES:
   [DIRECT - 95%] "{bullet_text}"
     ✓ Direct: {what matches directly}
     ✓ Transferable: {what transfers}
     ✓ Metrics: {quantified impact}
     Source: {resume_name}
   
   [TRANSFERABLE - 78%] "{bullet_text}"
     ✓ Transferable: {what transfers}
     ✓ Adjacent: {what's adjacent}
     ⚠ Gap: {what's missing}
     Source: {resume_name}
   
   [ADJACENT - 62%] "{bullet_text}"
     ✓ Adjacent: {what's related}
     ⚠ Gap: {what's missing}
     Source: {resume_name}
   
   RECOMMENDATION: Use DIRECT match (95%)
   ALTERNATIVE: If avoiding repetition, use TRANSFERABLE (78%) with reframing
   

5. Handle gaps (confidence <60%):

   GAP IDENTIFIED: {Requirement}
   
   BEST AVAILABLE: {score}% - "{bullet_text}"
   
   REFRAME OPPORTUNITY: {If applicable}
   Original: "{text}"
   Reframed: "{adjusted_text}" (truthful because {reason})
   New confidence: {score}%
   
   OPTIONS:
   1. Use reframed version ({new_score}%)
   2. Acknowledge gap in cover letter
   3. Omit bullet slot (reduce allocation)
   4. Use best available with disclosure
   
   RECOMMENDATION: {Most appropriate option}
   

3.2 Content Reframing:

When good match (>60%) but terminology misaligned:

Apply strategies from matching-strategies.md:

• Keyword alignment (preserve meaning, adjust terms)
• Emphasis shift (same facts, different focus)
• Abstraction level (adjust technical specificity)
• Scale emphasis (highlight relevant aspects)

Show before/after for transparency:

REFRAMING APPLIED:
Bullet: {template_slot}

Original: "{original_bullet}"
Source: {resume_name}

Reframed: "{reframed_bullet}"
Changes: {what changed and why}
Truthfulness: {why this is accurate}

Checkpoint:

"I've matched content to your template. Here's the complete mapping:

COVERAGE SUMMARY:
- Direct matches: {N} bullets ({percentage}%)
- Transferable: {N} bullets ({percentage}%)
- Adjacent: {N} bullets ({percentage}%)
- Gaps: {N} ({percentage}%)

REFRAMINGS APPLIED: {N}
- {Example 1}
- {Example 2}

GAPS IDENTIFIED:
- {Gap 1}: {Recommendation}
- {Gap 2}: {Recommendation}

OVERALL JD COVERAGE: {percentage}%

Review the detailed mapping below. Any adjustments to:
- Match selections?
- Reframings?
- Gap handling?"

[Present full detailed mapping]

Wait for user approval before generation.

Output: Complete bullet-by-bullet mapping with confidence scores and reframings

Phase 4: Generation Phase

Goal: Create professional multi-format outputs

Inputs:

• Approved content mapping (from Phase 3)
• User's formatting preferences (from library analysis)
• Target role information (from Phase 1)

Process:

4.1 Markdown Generation:

Compile mapped content into clean markdown:

# {User_Name}

{Contact_Info}

---

## Professional Summary

{Summary_from_template}

---

## Key Skills

**{Category_1}:**
- {Skills_from_library_matching_profile}

**{Category_2}:**
- {Skills_from_library_matching_profile}

{Repeat for all categories}

---

## Professional Experience

### {Job_Title}
**{Company} | {Location} | {Dates}**

{Role_summary_if_applicable}

• {Bullet_1_from_mapping}
• {Bullet_2_from_mapping}
...

### {Next_Role}
...

---

## Education

**{Degree}** | {Institution} ({Year})
**{Degree}** | {Institution} ({Year})

Use user's preferences:

• Formatting style from library analysis
• Bullet structure pattern
• Section ordering
• Typical length (1-page vs 2-page)

Output: {Name}_{Company}_{Role}_Resume.md

4.2 DOCX Generation:

Use document-skills:docx:

REQUIRED SUB-SKILL: Use document-skills:docx

Create Word document with:
- Professional fonts (Calibri 11pt body, 12pt headers)
- Proper spacing (single within sections, space between)
- Clean bullet formatting (proper numbering config, NOT unicode)
- Header with contact information
- Appropriate margins (0.5-1 inch)
- Bold/italic emphasis (company names, titles, dates)
- Page breaks if 2-page resume

See docx skill documentation for:
- Paragraph and TextRun structure
- Numbering configuration for bullets
- Heading levels and styles
- Spacing and margins

Output: {Name}_{Company}_{Role}_Resume.docx

4.3 PDF Generation (Optional):

If user requests PDF:

OPTIONAL SUB-SKILL: Use document-skills:pdf

Convert DOCX to PDF OR generate directly
Ensure formatting preservation
Professional appearance for direct submission

Output: {Name}_{Company}_{Role}_Resume.pdf

4.4 Generation Summary Report:

Create metadata file:

# Resume Generation Report
**{Role} at {Company}**

**Date Generated:** {timestamp}

## Target Role Summary
- Company: {Company}
- Position: {Role}
- IC Level: {If known}
- Focus Areas: {Key areas}

## Success Profile Summary
- Key Requirements: {top 5}
- Cultural Fit Signals: {themes}
- Risk Factors Addressed: {mitigations}

## Content Mapping Summary
- Total bullets: {N}
- Direct matches: {N} ({percentage}%)
- Transferable: {N} ({percentage}%)
- Adjacent: {N} ({percentage}%)
- Gaps identified: {list}

## Reframing Applied
- {bullet}: {original} → {reframed} [Reason: {why}]
...

## Source Resumes Used
- {resume1}: {N} bullets
- {resume2}: {N} bullets
...

## Gaps Addressed

### Before Experience Discovery:
{Gap analysis showing initial state}

### After Experience Discovery:
{Gap analysis showing final state}

### Remaining Gaps:
{Any unresolved gaps with recommendations}

## Key Differentiators for This Role
{What makes user uniquely qualified}

## Recommendations for Interview Prep
- Stories to prepare
- Questions to expect
- Gaps to address

Output: {Name}_{Company}_{Role}_Resume_Report.md

Present to user:

"Your tailored resume has been generated!

FILES CREATED:
- {Name}_{Company}_{Role}_Resume.md
- {Name}_{Company}_{Role}_Resume.docx
- {Name}_{Company}_{Role}_Resume_Report.md
{- {Name}_{Company}_{Role}_Resume.pdf (if requested)}

QUALITY METRICS:
- JD Coverage: {percentage}%
- Direct Matches: {percentage}%
- Newly Discovered: {N} experiences

Review the files and let me know:
1. Save to library (recommended)
2. Need revisions
3. Save but don't add to library"

Phase 5: Library Update (CONDITIONAL)

Goal: Optionally add successful resume to library for future use

When: After user reviews and approves generated resume

Checkpoint Question:

"Are you satisfied with this resume?

OPTIONS:
1. YES - Save to library
   → Adds resume to permanent location
   → Rebuilds library database
   → Makes new content available for future resumes

2. NO - Need revisions
   → What would you like to adjust?
   → Make changes and re-present

3. SAVE BUT DON'T ADD TO LIBRARY
   → Keep files in current location
   → Don't enrich database
   → Useful for experimental resumes

Which option?"

If Option 1 (YES - Save to library):

Process:

1. Move resume to library:

   Source: {current_directory}/{Name}_{Company}_{Role}_Resume.md
   Destination: {resume_library}/{Name}_{Company}_{Role}_Resume.md
   
   Also move:
   - .docx file
   - .pdf file (if exists)
   - _Report.md file
   

2. Rebuild library database:

   Re-run Phase 0 library initialization
   Parse newly created resume
   Add bullets to experience database
   Update keyword/theme indices
   Tag with metadata:
     - target_company: {Company}
     - target_role: {Role}
     - generated_date: {timestamp}
     - jd_coverage: {percentage}
     - success_profile: {reference to profile}
   

3. Preserve generation metadata:

   {
     "resume_id": "{Name}_{Company}_{Role}",
     "generated": "{timestamp}",
     "source_resumes": ["{resume1}", "{resume2}"],
     "reframings": [
       {
         "original": "{text}",
         "reframed": "{text}",
         "reason": "{why}"
       }
     ],
     "match_scores": {
       "bullet_1": 95,
       "bullet_2": 87,
       ...
     },
     "newly_discovered": [
       {
         "experience": "{description}",
         "bullet": "{text}",
         "addresses_gap": "{gap}"
       }
     ]
   }
   

4. Announce completion:

   "Resume saved to library!
   
   Library updated:
   - Total resumes: {N}
   - New content variations: {N}
   - Newly discovered experiences added: {N}
   
   This resume and its new content are now available for future tailoring sessions."
   

If Option 2 (NO - Need revisions):

"What would you like to adjust?"

[Collect user feedback]
[Make requested changes]
[Re-run relevant phases]
[Re-present for approval]

[Repeat until satisfied or user cancels]

If Option 3 (SAVE BUT DON'T ADD TO LIBRARY):

"Resume files saved to current directory:
- {Name}_{Company}_{Role}_Resume.md
- {Name}_{Company}_{Role}_Resume.docx
- {Name}_{Company}_{Role}_Resume_Report.md

Not added to library - you can manually move later if desired."

Benefits of Library Update:

• Grows library with each successful resume
• New bullet variations become available
• Reframings that work can be reused
• Discovered experiences permanently captured
• Future sessions start with richer library
• Self-improving system over time

Output: Updated library database + metadata preservation (if Option 1)

Error Handling & Edge Cases

Edge Case 1: Insufficient Resume Library

SCENARIO: User has only 1-2 resumes, limited content

HANDLING:
"⚠️ Limited resume library detected ({N} resumes).

This may result in:
- Fewer matching options
- More gaps in coverage
- Less variety in bullet phrasing

RECOMMENDATIONS:
- Proceed with available content (I'll do my best!)
- Consider adding more resumes after this generation
- Experience Discovery phase will be especially valuable

Continue? (Y/N)"

Edge Case 2: No Good Matches (confidence <60% for critical requirement)

SCENARIO: Template slot requires experience user doesn't have

HANDLING:
"❌ GAP: {Requirement}

No matches found with confidence >60%

OPTIONS:
1. Run Experience Discovery - might surface undocumented work
2. Reframe best available ({score}%) - I'll show you the reframing
3. Omit bullet slot - reduce template allocation
4. Note for cover letter - emphasize learning ability

Which approach?"

[Don't force matches - be transparent about gaps]

Edge Case 3: Research Phase Failures

SCENARIO: WebSearch fails, LinkedIn unavailable, company info sparse

HANDLING:
"⚠️ Limited company research available.

What I found:
- {Available info}

What's missing:
- {Missing areas}

Falling back to job description-only analysis.
Result: Template will be optimized for JD but may miss cultural nuances.

Do you have additional context about:
- Company culture?
- Team structure?
- Technologies used?

(Optional - you can share or skip)"

[Proceed with best-effort approach]

Edge Case 4: Job Description Quality Issues

SCENARIO: Vague JD, missing requirements, poorly written

HANDLING:
"⚠️ Job description lacks detail in areas:
- {Missing area 1}
- {Missing area 2}

This may limit template optimization.

Do you have additional context about:
- {Question 1}
- {Question 2}

(Optional - I'll work with what's available)"

[Extract what's possible, proceed]

Edge Case 5: Ambiguous Role Consolidation

SCENARIO: Unclear whether to merge roles or keep separate

HANDLING:
"🤔 Ambiguous consolidation decision for {Company}

OPTION A (Consolidated): {Rationale}
OPTION B (Separate): {Rationale}

Both are defensible. Which do you prefer?

(This becomes your preference for similar situations)"

[Remember preference for future]

Edge Case 6: Resume Length Constraints

SCENARIO: Too much good content, exceeds 2 pages

HANDLING:
"⚠️ Content exceeds 2 pages (current: {N} bullets, ~{pages} pages)

PRUNING SUGGESTIONS (ranked by relevance):
Remove:
- {Bullet X}: {score}% match (lowest)
- {Bullet Y}: {score}% match
...

Keep all bullets and accept >2 pages?
OR
Remove {N} bullets to fit 2 pages?

Your preference?"

[User decides priority]

Error Recovery:

• All checkpoints allow going back to previous phase
• User can request adjustments at any checkpoint
• Generation failures (DOCX/PDF) fall back to markdown-only
• Progress saved between phases (can resume if interrupted)

Graceful Degradation:

• Research limited → Fall back to JD-only analysis
• Library small → Work with available + emphasize discovery
• Matches weak → Transparent gap identification
• Generation fails → Provide markdown + error details

Usage Examples

Example 1: Internal Role (Same Company)

USER: "I want to apply for Principal PM role in 1ES team at Microsoft.
      Here's the JD: {paste}"

SKILL:
1. Library Build: Finds 29 resumes
2. Research: Microsoft 1ES team, internal culture, role benchmarking
3. Template: Features PM2 Azure Eng Systems role (most relevant)
4. Discovery: Surfaces VS Code extension, Bhavana AI side project
5. Assembly: 92% JD coverage, 75% direct matches
6. Generate: MD + DOCX + Report
7. User approves → Library updated with new resume + 6 discovered experiences

RESULT: Highly competitive application leveraging internal experience

Example 2: Career Transition (Different Domain)

USER: "I'm a TPM trying to transition to ecology PM role. JD: {paste}"

SKILL:
1. Library Build: Finds existing TPM resumes
2. Research: Ecology sector, sustainability focus, cross-domain transfers
3. Template: Reframes "Technical Program Manager" → "Program Manager,
             Environmental Systems" emphasizing systems thinking
4. Discovery: Surfaces volunteer conservation work, graduate research in
             environmental modeling
5. Assembly: 65% JD coverage - flags gaps in domain-specific knowledge
6. Generate: Resume + gap analysis with cover letter recommendations

RESULT: Bridges technical skills with environmental domain

Example 3: Career Gap Handling

USER: "I have a 2-year gap while starting a company. JD: {paste}"

SKILL:
1. Library Build: Finds pre-gap resumes
2. Research: Standard analysis
3. Template: Includes startup as legitimate role
4. Discovery: Surfaces skills developed during startup (fundraising,
             product development, team building)
5. Assembly: Frames gap as entrepreneurial experience
6. Generate: Resume presenting gap as valuable experience

RESULT: Gap becomes strength showing initiative and diverse skills

Example 4: Multi-Job Batch (3 Similar Roles)

USER: "I want to apply for these 3 TPM roles:
      1. Microsoft 1ES Principal PM
      2. Google Cloud Senior TPM
      3. AWS Container Services Senior PM
      Here are the JDs: {paste 3 JDs}"

SKILL:
1. Multi-job detection: Triggered (3 JDs detected)
2. Intake: Collects all 3 JDs, initializes batch
3. Library Build: Finds 29 resumes (once)
4. Gap Analysis: Identifies 14 gaps, 8 unique after deduplication
5. Shared Discovery: 30-minute session surfaces 5 new experiences
   - Kubernetes CI/CD for nonprofits
   - Azure migration for university lab
   - Cross-functional team leadership examples
   - Recent hackathon project
   - Open source contributions
6. Per-Job Processing (×3):
   - Job 1 (Microsoft): 85% coverage, emphasizes Azure/1ES alignment
   - Job 2 (Google): 88% coverage, emphasizes technical depth
   - Job 3 (AWS): 78% coverage, addresses AWS gap in cover letter recs
7. Batch Finalization: All 3 resumes reviewed, approved, added to library

RESULT: 3 high-quality resumes in 40 minutes vs 45 minutes sequential
        5 new experiences captured, available for future applications
        Average coverage: 84%, all critical gaps resolved

Example 5: Incremental Batch Addition

WEEK 1:
USER: "I want to apply for 3 jobs: {Microsoft, Google, AWS}"
SKILL: [Processes batch as above, completes in 40 min]

WEEK 2:
USER: "I found 2 more jobs: Stripe and Meta. Add them to my batch?"
SKILL:
1. Load existing batch (includes 5 previously discovered experiences)
2. Intake: Adds Job 4 (Stripe), Job 5 (Meta)
3. Incremental Gap Analysis: Only 3 new gaps (vs 14 original)
   - Payment systems (Stripe-specific)
   - Social networking (Meta-specific)
   - React/frontend (both)
4. Incremental Discovery: 10-minute session for new gaps only
   - Surfaces payment processing side project
   - React work from bootcamp
   - Large-scale system design course
5. Per-Job Processing (×2): Jobs 4, 5 processed independently
6. Updated Batch Summary: Now 5 jobs total, 8 experiences discovered

RESULT: 2 additional resumes in 20 minutes (vs 30 min if starting from scratch)
        Time saved by not re-asking 8 previous gaps: ~20 minutes

Testing Guidelines

Manual Testing Checklist:

Test 1: Happy Path

- Provide JD with clear requirements
- Library with 10+ resumes
- Run all phases without skipping
- Verify generated files
- Check library update
PASS CRITERIA:
- All files generated correctly
- JD coverage >70%
- No errors in any phase

Test 2: Minimal Library

- Provide only 2 resumes
- Run through workflow
- Verify gap handling
PASS CRITERIA:
- Graceful warning about limited library
- Still produces reasonable output
- Gaps clearly identified

Test 3: Research Failures

- Use obscure company with minimal online presence
- Verify fallback to JD-only
PASS CRITERIA:
- Warning about limited research
- Proceeds with JD analysis
- Template still reasonable

Test 4: Experience Discovery Value

- Run with deliberate gaps in library
- Conduct experience discovery
- Verify new experiences integrated
PASS CRITERIA:
- Discovers genuine undocumented experiences
- Integrates into final resume
- Improves JD coverage

Test 5: Title Reframing

- Test various role transitions
- Verify title reframing suggestions
PASS CRITERIA:
- Multiple options provided
- Truthfulness maintained
- Rationales clear

Test 6: Multi-format Generation

- Generate MD, DOCX, PDF, Report
- Verify formatting consistency
PASS CRITERIA:
- All formats readable
- Formatting professional
- Content identical across formats

Regression Testing:

After any SKILL.md changes:
1. Re-run Test 1 (happy path)
2. Verify no functionality broken
3. Commit only if passes

Source: varunr89/resume-tailoring-skill — distributed by TomeVault.