career-ops

Analyzes job descriptions across 10 weighted dimensions (skill match, compensation, growth, location, company stability, role fit, market demand, interview difficulty, timeline, and cultural alignment) to produce a normalized 1.0-5.0 score. Uses Claude Code with a shared scoring archetype system (_shared.md) that defines evaluation rubrics, enabling consistent A-F grade mapping across 740+ evaluations. The evaluation engine in oferta.md handles single JD analysis while ofertas.md performs comparative ranking across multiple opportunities.

Generates tailored resume PDFs for each target job description using a keyword-injection engine that maps JD requirements to candidate skills. The generate-pdf.mjs script processes CV HTML templates with embedded font assets, injects keywords extracted from the target JD, and outputs ATS-compliant PDFs. Uses a CV HTML template system with configurable fonts and styling, ensuring each PDF is customized for the specific role while maintaining ATS readability (no complex graphics, semantic HTML structure). The system produced 100+ tailored CVs during the original 740-evaluation search.

Manages candidate profile, job search preferences, and system configuration through YAML-based configuration files (config/profile.example.yml) and environment variables (.envrc). The profile system stores candidate skills, experience, education, and preferences (target roles, salary range, location constraints), which are referenced by all downstream skills (evaluation, resume generation, outreach). The configuration system enables users to customize evaluation weights, job board sources (portals.yml), and language preferences without modifying code. Profile templates (modes/_profile.template.md) enable quick setup for new users.

Provides system health checks and data validation through utility scripts (doctor.mjs, verify-pipeline.mjs, cv-sync-check.mjs) that validate configuration, check API connectivity, verify data integrity, and ensure consistency between CV templates and application tracker. The doctor.mjs script performs comprehensive health checks (API keys, file permissions, required dependencies), while verify-pipeline.mjs validates the application tracker for missing data, inconsistent statuses, and orphaned records. cv-sync-check.mjs ensures that generated CVs match the current candidate profile.

Manages system versioning and updates through update-system.mjs script and VERSION file, enabling users to track system versions and apply updates safely. The update system checks for new releases, validates compatibility, and applies incremental updates to configuration files and scripts. Version tracking enables reproducibility (users can specify which version of career-ops was used for a job search) and enables rollback if updates introduce issues.

Maintains a single source of truth for all job applications using a flat-file markdown database (data/applications.md) instead of a traditional database. The system includes three Node.js scripts: merge-tracker.mjs consolidates application data from multiple sources, dedup-tracker.mjs removes duplicate entries using fuzzy matching on company/role/date, and normalize-statuses.mjs standardizes status values (applied, interviewing, rejected, offer, etc.) across inconsistent user input. This architecture enables version control (Git history), human-readable data, and easy auditing without external dependencies.

Orchestrates large-scale job discovery and evaluation through a bash-based batch runner (batch-runner.sh) that processes multiple job sources in parallel. The system uses scan.md (Claude Code skill) to discover new roles from configured job portals (portals.yml), and batch-prompt.md as a worker template that applies evaluation logic to each discovered JD. The batch runner manages job queuing, parallel execution limits, and result aggregation, enabling processing of 100+ job postings in a single run. Results feed into the application tracker for downstream pipeline stages (apply, outreach, interview prep).

Provides interview readiness through two mechanisms: (1) a story bank system that stores and retrieves candidate anecdotes indexed by skill/competency, enabling Claude to generate interview responses using relevant personal examples, and (2) pattern analysis scripts that extract recurring themes from past interviews and applications to identify weak areas. The interview-prep.md skill file orchestrates story retrieval, question generation, and response coaching. Pattern analysis scripts examine application tracker data to identify which skills/experiences correlate with positive outcomes, informing interview preparation focus areas.

Automates LinkedIn outreach through the contacto.md Claude Code skill, which generates personalized connection requests and messages based on the target role, company, and candidate profile. The system maintains contact history in the application tracker, enabling follow-up cadence management and relationship tracking. Messages are generated with role-specific context (e.g., mentioning shared skills or company interests) rather than generic templates. The outreach system integrates with the application pipeline, allowing users to send targeted messages to recruiters or hiring managers after initial application.

Provides a real-time terminal user interface (TUI) dashboard built in Go that visualizes the job application pipeline, showing application status distribution, timeline progression, and key metrics. The dashboard reads from the flat-file application tracker (data/applications.md) and renders interactive screens for pipeline overview, individual application details, and follow-up scheduling. The Go implementation enables fast rendering and low resource usage compared to web-based dashboards, making it suitable for local development environments.

Provides internationalization (i18n) support through dedicated Claude Code skill files for non-English job markets. The system includes language-specific modes (e.g., Spanish, Portuguese, French) that adapt evaluation criteria, resume generation, and outreach messaging to regional hiring practices and terminology. For example, the Spanish mode (README.es.md) handles regional salary expectations, required certifications, and cultural communication norms. Each language mode maintains its own scoring archetypes and message templates, enabling consistent quality across markets.

Provides intelligent form-filling assistance through the apply.md Claude Code skill, which analyzes job application forms and generates context-appropriate responses for common fields (cover letter, motivation, technical questions). The system uses the candidate profile, target job description, and story bank to generate personalized answers that align with the role requirements. Claude Code's ability to analyze form structure and generate field-specific content enables semi-automated application completion without requiring pre-built templates for each company.

Analyzes job market trends and candidate skill gaps to recommend targeted training and development activities. The deep.md and training.md Claude Code skills examine the candidate's current skills against high-demand skills in target roles, identifying gaps and recommending specific courses, projects, or certifications. The system integrates with the application tracker to identify which skills correlate with successful applications, informing development priorities. Project recommendations are generated based on portfolio gaps identified through job description analysis.