AI-powered resume tailoring skill for Claude Code that researches roles, surfaces undocumented experiences, and creates tailored resumes from an existing library. It aims to ensure job opportunities are based on capabilities, not resume writing skills, benefiting job seekers by producing high-quality, optimized resumes and saving application time.

How It Works

This skill employs an AI-driven approach to generate tailored resumes. It performs deep research into company culture and role requirements, then uses conversational branching interviews to discover and surface undocumented experiences. Content selection is confidence-scored with transparent gap identification, ensuring factual integrity and truth-preserving reframing. A key innovation is multi-job batch processing, which allows for efficient tailoring across multiple similar roles by sharing experience discovery and aggregating gap analysis.

Quick Start & Requirements

• Primary install: Clone the repository: git clone https://github.com/varunr89/resume-tailoring-skill.git ~/.claude/skills/resume-tailoring. Alternatively, manually create the directory and download files.
• Prerequisites: Claude Code with skills enabled; at least 1-2 resumes in markdown format. Optional but recommended: WebSearch capability, document-skills plugin for DOCX/PDF generation, and 10+ resumes in the library for optimal results.
• Resume Library Setup: Create a ~/resumes/ directory and place markdown resumes within it.
• Links: No external quick-start or documentation links are provided; the repository serves as the primary resource.

Highlighted Details

• Multi-Job Batch Processing: Efficiently process 3-5 similar jobs with shared experience discovery, aggregate gap analysis, and deduplication, offering 11-27% time savings over sequential processing.
• Deep Research: Analyzes company culture, role requirements, and success profiles, leveraging external capabilities like WebSearch.
• Branching Discovery: Surfaces undocumented work through dynamic, conversational follow-up questions.
• Smart Matching: Utilizes confidence-scored content selection and transparent gap identification for truth-preserving reframing.
• Multi-Format Output: Generates professional markdown, ATS-friendly DOCX, print-ready PDF, and interview preparation reports.
• Self-Improving Library: The skill's knowledge base grows with each successful resume generation.

Maintenance & Community

The project is hosted on GitHub, with issues and discussions managed via GitHub Issues and Discussions. A roadmap indicates future development including cover letter generation, LinkedIn profile optimization, and multi-language support.

Licensing & Compatibility

This project is licensed under the MIT License, which is generally permissive for commercial use and integration into closed-source projects.

Limitations & Caveats

Functionality for DOCX/PDF generation and deep company research is dependent on optional plugins (document-skills) and capabilities (WebSearch); the skill gracefully falls back to markdown-only output or JD-only analysis if these are unavailable. Optimal performance and discovery are enhanced with a larger resume library (10+ resumes).