By 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:
Job description (text or URL)
Resume library location (defaults to
resumes/in current directory)
Workflow:
Build library from existing resumes
Research company/role
Create template (with user checkpoint)
Optional: Branching experience discovery
Match content with confidence scoring
Generate MD + DOCX + PDF + Report
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 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:
Locate resume directory:
User provides path OR default to ./resumes/ Validate directory existsScan for markdown files:
Use Glob tool: pattern="*.md" path={resume_directory} Count files found Announce: "Building resume library... found {N} resumes"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
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" } }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:
Emphasize different aspects:
"Graduate Researcher" → "Research Software Engineer" (if coding-heavy)
"Data Science Lead" → "Technical Program Manager" (if leadership)
Use industry-standard terminology:
"Scientist III" → "Senior Research Scientist" (clearer seniority)
"Program Coordinator" → "Project Manager" (standard term)
Add specialization when truthful:
"Engineer" → "ML Engineer" (if ML work substantial)
"Researcher" → "Computational Ecologist" (if computational methods)
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):
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?"
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
Follow-up systematically:
Ask "what," "how," "why" to get details
Quantify: "Any metrics?"
Contextualize: "Was this production?"
Validate: "Does this address the gap?"
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:
Extract all candidate bullets from library
All bullets from library database
All newly discovered experiences
Include source resume for each
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)
Rank candidates by score
Sort high to low
Group by confidence band:
90-100%: DIRECT
75-89%: TRANSFERABLE
60-74%: ADJACENT
<60%: WEAK/GAP
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 reframingHandle 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:
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 fileRebuild 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}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}" } ] }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