resume-tailor

Compose tailored resume with no fabricated content. Uses LLM to rewrite bullet points to align with job description keywords while strictly adhering to facts.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/resume-tailor

SKILL.md

Skill: Resume Tailor

Summary

The core engine of ROLESENSE.ai. It takes a Master Resume and a Job Description, calculates a match score, and generates a tailored version of the resume.

When to Use

The agent SHOULD use this skill when:

The user requests to "tailor," "optimize," or "rewrite" a resume for a job.
The user asks for a "Match Score" or "Gap Analysis."
The user wants to export the final PDF.

Primary Goal

Generate a tailored resume that maximizes keyword overlap with the Job Description WITHOUT fabricating experience.

High-Level Procedure

Load Data: Retrieve Master Resume (JSON) and Job Description (JSON).
Score: Calculate Cosine Similarity between resume embeddings and JD embeddings.
Analyze: Identify missing keywords (Gap Analysis).
Tailor (RAG):
- For each experience block, retrieve relevant bullets.
- Rephrase bullets to highlight JD keywords using scripts/rewrite_bullet.py.
Verify: Run scripts/audit_fabrication.py to ensure no new facts were added.
Format: Generate final layout/PDF.

Inputs

master_resume_json: The source data.
job_description_json: The target requirements.

Constraints & Guardrails

NON-FABRICATION: The audit_fabrication.py script MUST return True before outputting the result.
ATS Compliance: Output must avoid columns or graphics if "ATS Mode" is selected.
User Approval: Always present the "Diff" (changes made) before finalizing.

References

/examples/tailoring_examples.md: Examples of good vs. bad rewriting (hallucination prevention).
/scripts/vectorize.py: Handles Ollama embedding generation.
/scripts/audit_fabrication.py: Logic to compare entities in source vs. output.