job-hunter: AI-Driven Three-Stage Job Search Automation Workflow Practice

job-hunter is a configuration-driven job search workspace. It automates the entire workflow from job discovery to interview preparation through collaboration between three AI Agents: Scout, Application Builder, and Interview Coach.All personal data is stored locally, addressing pain points in traditional job search processes such as complexity, time consumption, quality degradation due to fatigue, and privacy risks from AI tools.

Traditional Job Search Pain Points

• Complex process: Need to manage multiple tasks (job screening, resume customization, interview preparation) simultaneously, which is time-consuming and prone to quality drops due to fatigue
• Privacy risks: Most AI job search tools require uploading sensitive information with no data security guarantees

Project Positioning

To address these pain points, job-hunter builds a three-stage AI Agent workflow covering the entire job search process. It adheres to data localization principles, and uses the .gitignore mechanism to prevent accidental submission of sensitive information.

1. Scout

Responsibilities: Job discovery and screening. Search for jobs across multiple channels based on user career preferences (type, company, location, etc.), generate a recommendation list via intelligent matching and priority sorting, and support deduplication and status tracking

2. Application Builder

Responsibilities: Generate customized application materials, including resume optimization (keyword matching), cover letter writing, project description adjustment, and ATS format standardization

3. Interview Coach

Responsibilities: Interview preparation, providing technical question prediction, STAR method answer framework, mock interview feedback, and company research information organization

Core Advantages

• Separation of Concerns: Split into configuration layer (user-defined requirements), logic layer (Agent workflow), and skill layer (AI capability encapsulation)
• Reusable & Shareable: Configuration files support backup, version control, and community sharing (with sensitive info removed)
• Privacy First: Local storage of personal data, .gitignore isolation for sensitive files, optional encrypted storage

Design Goals: Lower user entry barriers and ensure data security

Skill System

Modular encapsulation of reusable AI capabilities: resume customization, cover letter generation, interview Q&A, company research, salary negotiation, etc. A unified interface facilitates reuse and community contributions

Technical Structure

• scripts/: Automation scripts (batch operations, scheduled tasks)
• skills/: AI skill implementation
• workflow/: Agent collaboration logic
• PLAYBOOK.md: User guide

Extensibility

Supports adding recruitment platform adapters, custom skills, workflow modifications, and integration with external tools (calendar, email, etc.)

• Fresh Graduates: Explore industry positions and accumulate job search experience
• Career Changers: Customize resumes and strategies for new fields to bridge experience gaps
• Passive Job Seekers: Maintain market sensitivity and capture high-quality opportunities
• Bulk Applicants: Preserve material quality and avoid fatigue-induced errors

Core Value: Improve job search efficiency and quality, reduce privacy risks

Project Value

Demonstrates the implementation path of AI Agents in vertical scenarios: decompose complex tasks into professional Agent collaboration, achieve personalization via configuration-driven design, and adhere to privacy bottom lines

Development Insights

1. Complex tasks should be split into professional Agents instead of all-in-one Agents
2. Configuration priority lowers user entry barriers
3. Privacy design needs to be considered at the architecture level
4. Encapsulating AI capabilities into reusable modules improves efficiency

Trend: More AI Agent solutions for vertical scenarios will emerge, creating practical value