Career Ops

"Companies use AI to filter candidates. I gave candidates AI to choose companies." — Santiago Fernandez (@santifer)

Career Ops is an AI-powered job search command center adapted from the career-ops open source project (MIT license) by Santiago Fernandez de Valderrama. The original system evaluated 740+ job offers, generated 100+ tailored CVs, and helped Santiago land a Head of Applied AI role.

This cabinet adapts that methodology into a structured, visual, agent-driven system.

Philosophy

• Filter, don't spray. This is not a mass-apply tool. It helps you find the few offers genuinely worth your time.
• Human-in-the-loop. AI evaluates and recommends. You decide and submit. Never auto-apply.
• Quality over quantity. Don't apply to anything scoring below 4.0/5.
• Data-driven. Every decision backed by evaluation scores, pipeline metrics, and pattern analysis.

How It Works

1. Configure your profile in [[profile]] — your CV, proof points, skills matrix, and search criteria
2. Scan portals using [[portals]] — 45+ companies across Greenhouse, Ashby, Lever, Wellfound
3. Evaluate offers with the 6-block system in [[evaluations]] — each job gets blocks A through F
4. Generate tailored CVs in [[cv-lab]] — ATS-optimized, keyword-injected, one per application
5. Track your pipeline on the [[pipeline]] board — from discovered to offer with full status history
6. Prep for interviews using [[interview-prep]] — STAR+R stories, negotiation scripts, company research
7. Analyze patterns in [[analytics]] — rejection patterns, channel ROI, pipeline velocity

Your Team — 10 Agents

Adapted from the 16 modes in the original career-ops system, organized into 10 specialized agents:

Core Pipeline

• Pipeline Conductor — orchestrates the full auto-pipeline: URL → evaluation → CV → application → tracker. Also handles batch processing and pipeline integrity.
• Evaluator — runs the 6-block A-F+G evaluation on every job offer, multi-offer comparison, and scoring
• CV Tailor — generates ATS-optimized, keyword-injected CVs per application with proof point selection
• Scanner — discovers new roles across 30+ company portals using 3-level scanning (Playwright, API, WebSearch)

Interview & Negotiation

• Interview Strategist — builds STAR+R stories, company-specific prep packets, negotiation playbooks
• Application Assistant — helps fill application forms with personalized answers, cover letters, and tone calibration

Intelligence & Networking

• Researcher — deep company research across 6 axes: strategy, movements, culture, challenges, competitors, candidate angle
• Outreach Specialist — LinkedIn outreach (4 contact-type frameworks) and follow-up cadence management

Strategy & Growth

• Pattern Analyst — rejection pattern detection, funnel analysis, channel ROI, archetype performance, strategic recommendations
• Career Advisor — evaluates courses/certifications and portfolio projects against career goals and skill gaps

The 6-Block Evaluation System

Every job offer is evaluated across six blocks:

Block	Name	What It Covers
A	Role Summary	Title, level, team, scope, growth potential
B	CV Match Analysis	Skills overlap, gap identification, keyword alignment
C	Level Strategy	Seniority fit, over/under-leveling risk, career trajectory
D	Compensation Research	Market benchmarks, equity analysis, total comp modeling
E	Personalization Tips	Company culture signals, application angle, differentiators
F	Interview Prep	Likely questions, STAR+R stories to load, company-specific intel

Attribution

This cabinet is adapted from santifer/career-ops by Santiago Fernandez de Valderrama, licensed under MIT. The original project provides Claude Code skills, a Go terminal dashboard, PDF generation via Playwright, and portal scanning — this cabinet adapts the methodology and evaluation framework into the cabinet format.