Resume Creator

A comprehensive resume creation skill that uses first-principles thinking, Google XYZ format, web research, and iterative visual refinement to craft tailored, professional resumes.

When to Use This Skill

• User wants to create a new resume
• User wants to update/optimize an existing resume
• User mentions a job application, job posting, or target company
• User asks about resume formatting or CV creation
• User wants to tailor their resume for a specific role

Process Overview

Phase 1: Information Gathering

1. Read existing materials (if available):

   • Existing resume (PDF, Word, or text)
   • LinkedIn profile screenshots (Claude cannot directly access LinkedIn URLs)
   • LinkedIn posts for achievements and speaking engagements
   • Portfolio or personal website

2. Understand the target:

   • Job description (if provided)
   • Target company and role
   • Industry/role type
   • Career goals

3. Research the target company using web search:

   • Company culture and values
   • Tech stack and engineering practices
   • Recent news, funding, products
   • What they look for in candidates
   • Company AUM/size/metrics for context
   • Example searches:
     • "{company} engineering blog hiring"
     • "{company} careers culture values"
     • "{role} at {company} interview what they look for"
     • "{company} AUM assets under management" (for finance)

4. Gather missing information by asking the user:

   • Recent experience not on resume
   • Specific achievements with metrics
   • Skills and technologies used
   • Projects and speaking engagements
   • Time spent on projects (for speed metrics)
   • Client details (AUM, size, industry)

Phase 2: Google XYZ Format Analysis

The XYZ Formula: "Accomplished [X] as measured by [Y] by doing [Z]"

• X = Achievement/outcome (action verb: Built, Architected, Shipped, Led)
• Y = Quantifiable metric (%, time, money, users, accuracy)
• Z = How you did it (method, technology, approach)

Before writing, analyze each bullet:

Bullet	X (What)	Y (Metric)	Z (How)	Score
Example	Built connector	2 weeks, 1000s docs	Delta API, Redis	3/3 ✓

Target: 100% of bullets should score 3/3

Common metrics to extract from user:

• Time to build ("in 2 weeks", "in 1 week")
• Accuracy improvements ("125% improvement", "90% accuracy", "<3% error rate")
• Scale ("1000s of docs", "400+ rounds", "90+ companies")
• Cost savings ("reducing time from hours to minutes", "50% faster")
• Client context ("$100B+ AUM client", "Fortune 500")
• Audience size ("150+ builders", "100+ attendees")

Phase 3: First-Principles Analysis

Before writing, analyze from first principles:

1. Research what hiring managers look for:

   • Web search: "{role} resume what hiring managers look for 2024"
   • Web search: "Google XYZ resume format"
   • Understand the <8 second resume scan reality

2. Alignment analysis: Create a table mapping: | Job Requirement | User's Experience | Gap/Strength |

3. Paul Graham / YC style considerations (for startup roles):

   • Lead with what you BUILT, not job titles
   • Show speed of execution ("shipped in X weeks", "built in 2 weeks")
   • Quantify everything (%, numbers, scale)
   • Builder tone: "Built", "Shipped", "Architected", "Won" not "Responsible for"
   • Remove corporate buzzwords

4. Avoid redundancy:

   • Check if metrics in bullets duplicate header/subheader info
   • Example: Don't say "Fortune 500 clients" in bullet if header says "Serving Fortune 500 clients"

Phase 4: LaTeX Resume Creation

Use the Harvard-style LaTeX template with:

• Clean header (name, location, contact, links)
• No colored header bars - clean white background
• Section order: Experience → Projects & Speaking → Skills → Education → Leadership
• € symbol for currencies
• 1 page maximum (critical)

Key formatting:

• Font: Helvetica Neue (or similar sans-serif)
• Colors: Navy blue (#14-2D-4B / RGB 20,45,75) for sections
• Margins: ~0.5 inches
• Line spacing: 1.05
• Use \setstretch{1.05} for readability

Punctuation guidelines:

• Use commas or semicolons to connect clauses, NOT em dashes (--)
• Em dashes (--) only for date ranges in headers (e.g., "Sept 2025 -- Present")
• Use semicolons to separate distinct achievements in one bullet

Link formatting:

• Add [link] in small navy text next to items with LinkedIn/external proof
• Format: {\color{sectioncolor}\footnotesize[\href{URL}{link}]}

Phase 5: Iterative Visual Refinement

Critical: After creating the LaTeX file, iterate visually:

1. Compile to PDF:

   xelatex -interaction=nonstopmode resume.tex
   

2. Check page count: Must be exactly 1 page

   • If 2 pages: reduce spacing, tighten text, combine bullets
   • Adjust \titlespacing*{\section}{0pt}{6pt}{2pt} if needed
   • Adjust \setlist[itemize]{itemsep=1pt, parsep=0pt, topsep=1pt}

3. Check for issues:

   • Does it fit on 1 page?
   • Is spacing balanced?
   • Are there overflow issues?
   • Is typography clean?
   • Any redundant information?

4. Iterate until perfect

Phase 6: Final Delivery

1. Save final PDF: Resume_[Name]_[Role]_[Year].pdf
2. Keep .tex source file with same naming
3. Clean up temp files (.aux, .log, .out)
4. Open PDF for user

Content Guidelines

Experience Bullets - XYZ Examples

Strong XYZ bullets:

• Built SharePoint connector in 2 weeks enabling auto-indexing of 1000s of enterprise docs, reducing admin setup from hours to minutes
• Architected Snowflake sub-agent for NL-to-SQL, improving query accuracy by 125%; embedded at $100B+ AUM client, drove 4+ validation cycles
• Built agentic funding extraction with <3% error rate on 400+ rounds, validated against hand-labeled data and proprietary providers
• Delivered DSPy live optimization talk to 150+ builders, featured in global newsletter (50K+ subscribers)

Weak bullets to avoid:

• Responsible for platform development (no metric, no how)
• Worked on various projects (vague)
• Built connector using Redis (no metric, no outcome)

Combining Related Bullets

When two bullets are related, combine them:

• Before: "Architected Snowflake agent" + "Embedded as Field Engineer at client"
• After: "Architected Snowflake sub-agent for NL-to-SQL, improving accuracy by 125%; embedded at $100B+ AUM client, drove 4+ validation cycles"

Skills Organization

• AI/ML: LangChain, LangGraph, DSPy, MCP, OpenAI/Anthropic/Google APIs, RAG, Vector DBs, Embeddings
• Full-Stack: Next.js, React, TypeScript, Tailwind, Node.js, Python, REST APIs
• Data & Infra: Postgres, Snowflake, Redis, Microsoft Graph, GCP, Azure, Docker
• Languages: German (native), English (fluent)

How Users Should Use This Skill

For best results, provide:

1. Your current resume (PDF or text)
2. LinkedIn screenshots (profile, experience, posts) — Claude cannot directly access LinkedIn URLs
3. The job posting or target company/role
4. Any recent achievements not on your resume
5. Metrics: time spent, accuracy numbers, scale, client details

Example:

Help me update my resume for the AI Engineer role at [Company].
Here's my current resume: [attach PDF]
LinkedIn posts: [attach screenshots]

Some context:
- Built the SharePoint connector in 2 weeks
- Client has $100B+ AUM
- Achieved 90% accuracy after 4 validation cycles