career-ops is an open-source, AI-powered command center designed to revolutionize the job search experience for developers and professionals. Created by Applied AI Operator Santiago Fernández de Valderrama, the system originated from his personal search where he evaluated 740 job listings to secure a role. Unlike cloud-based SaaS platforms that require users to upload sensitive personal data, career-ops is a local-first, CLI-based tool that runs directly on your machine. By integrating with existing AI coding agents like Claude Code, Gemini CLI, and GitHub Copilot, it offers a sophisticated, private environment for managing complex job application funnels without vendor lock-in or telemetry.

The tool functions as a rigorous filtering system rather than a mass-application bot. It emphasizes high-conviction matches by utilizing a transparent six-dimension rubric that rates job postings from 1.0 to 5.0, with a strict recommendation to avoid applying to roles scoring below 4.0. Users interact with the tool through their preferred AI terminal environment, where it performs tasks ranging from scraping hundreds of company career portals to generating tailored CVs and drafting context-aware responses for open-ended application questions. The architecture is designed to respect the candidate’s time by automating the administrative burden of job hunting while keeping the user in full control of all final submissions.

Some of the key features are:

• Rubric-Guided Evaluation: scores job postings against a six-dimension framework including match, compensation, and cultural alignment
• CLI-Agnostic Operation: functions seamlessly with Claude Code, Codex, OpenCode, Gemini CLI, Qwen, and GitHub Copilot
• Automated Form Drafting: reads Greenhouse, Ashby, and Lever application forms to generate tailored responses grounded in your CV and the specific job description
• Local-First Privacy: keeps all personal data, resume history, and application tracking on your local machine with no cloud storage or account requirement
• Transparent Methodology: provides a publicly auditable scoring rubric that explains how every assessment is reached, including specific citations from your CV
• Integrated Pipeline Tracking: monitors application status and interview progress directly within a terminal-based dashboard
• Zero-Token Portals: pre-configured scrapers for over 150 company career pages to conduct on-demand job discovery without consuming paid API tokens
• Tailored Document Generation: creates customized PDF resumes optimized for specific roles without overwriting your master CV file

To operate the system, users initialize a workspace locally, which then guides them through a conversational onboarding process to understand their career goals and professional profile. Once configured, a user can provide a job listing URL to initiate the 'auto-pipeline' command. The assistant then reads the job description, analyzes it against the user's uploaded CV, generates a detailed report of matches and gaps, prepares an application strategy, and provides draft answers for any supplemental form questions. The workflow is iterative, allowing the user to refine their profile or compare multiple offers as they receive them.

Some common use cases include:

• Technical Job Hunting: streamlining the application process for engineers who want to manage their search entirely within their existing command-line workflow
• High-Quality Filtering: rapidly disqualifying poor-fit job postings to focus time and energy on positions that match specific salary and cultural requirements
• Application Customization: generating personalized responses to 'Why this role?' or project-related questions in portal forms to increase interview conversion rates
• Career Pipeline Management: maintaining an organized, terminal-based view of multiple ongoing job applications, interview preparations, and market comparisons