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:

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:

   bash

   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