Data Model

Entity Relationship

erDiagram
    Job ||--o| JdAnalysis : analyzed
    Job ||--o{ Application : applied
    Resume ||--o{ TailoredResume : tailored
    Resume ||--|{ ResumeSection : contains
    Resume ||--o{ SkillEntry : "generates profile"
    TailoredResume ||--|{ TailoredSection : contains
    TailoredResume }o--|| Job : for
    Application }o--o| TailoredResume : uses

    Job {
        uuid id PK
        string platform
        string platform_job_id
        string title
        string company
        string location
        int salary_min
        int salary_max
        string url
        text description_raw
        bool pr_required "nullable"
        bool citizenship_required "nullable"
        bool visa_sponsorship "nullable"
        string status
        datetime scraped_at
    }

    JdAnalysis {
        uuid job_id FK
        json required_skills
        json preferred_skills
        int years_experience
        json keywords
        float match_score
        text match_reasoning
    }

    Resume {
        uuid id PK
        bool is_base
        string format
    }

    ResumeSection {
        uuid id PK
        uuid resume_id FK
        string section_type
        text content
        bool tailorable
    }

    TailoredResume {
        uuid id PK
        uuid base_resume_id FK
        uuid job_id FK
        text diff_summary
        bool approved
    }

    Application {
        uuid id PK
        uuid job_id FK
        uuid tailored_resume_id FK
        string status
        datetime applied_at
    }

    SkillEntry {
        uuid id PK
        string name
        string category
        int proficiency "1-10"
        float years
        json evidence
        string source
    }

Key Entities

Job

The central entity. Tracks a job posting from discovery through application.

#![allow(unused)]
fn main() {
pub enum Platform { Seek, LinkedIn, Indeed }

pub enum JobStatus {
    New,        // Just scraped
    Analyzed,   // JD analyzed by LLM
    Matched,    // User marked as interesting
    Skipped,    // User passed
    Applied,    // Submitted
    Rejected,   // Got rejection
    Interview,  // Interview scheduled
    Offer,      // Received offer
}
}

JdAnalysis

LLM-generated analysis attached 1:1 to a Job. Key fields:

• required_skills / preferred_skills — extracted from JD
• keywords — ATS keywords to include in resume
• match_score — 0.0 to 1.0, computed against UserProfile
• match_reasoning — human-readable explanation

Resume & TailoredResume

Base resume imported once and split into sections. Each section marked tailorable: true/false.

Tailored resumes only store modified sections — unchanged sections inherit from base at render time.

#![allow(unused)]
fn main() {
pub enum SectionType {
    Summary,          // tailorable
    Education,        // protected
    WorkExperience,   // tailorable (bullets only)
    Internship,       // tailorable (bullets only)
    Projects,         // tailorable
    Technologies,     // tailorable
    Extracurricular,  // protected
    References,       // protected
}
}

Application

Tracks the lifecycle from preparation to outcome.

#![allow(unused)]
fn main() {
pub enum ApplicationStatus {
    Prepared,      // Resume tailored, ready
    Submitted,     // User confirmed
    Acknowledged,  // Company acknowledged
    Interview,     // Scheduled
    Rejected,
    Offer,
}
}

Skill Profile

A structured representation of the user’s technical abilities, auto-generated from resume analysis and adjustable manually. Used by JD Analysis to compute precise match scores.

#![allow(unused)]
fn main() {
pub enum SkillCategory {
    Language,      // Rust, Python, TypeScript
    Framework,     // Django, React, axum
    Database,      // SQLite, PostgreSQL, SQL Server
    Tool,          // Docker, Git, CI/CD
    Cloud,         // AWS, GCP, Azure
    Concept,       // System Design, Async, REST API
}

pub enum SkillSource {
    ResumeAnalysis,  // Auto-extracted from resume by LLM
    Manual,          // User-provided
    Both,            // Auto-extracted then user-adjusted
}
}

Each SkillEntry includes:

• proficiency — 1-10 scale (1 = awareness, 5 = working proficiency, 8 = expert, 10 = authority)
• years — years of experience with this skill
• evidence — specific projects/roles that demonstrate this skill (e.g. “Efision ERP — async/tokio/axum”)
• source — whether it was auto-extracted, manually added, or both

SQLite Schema

All JSON arrays (skills, keywords) stored as TEXT with serde_json. UUIDs as TEXT. Dates in ISO 8601.

See crates/kairos-db/migrations/V1__initial.sql for the full schema.