jd-process

To normalize a job description, provide the raw JD text or a file path to trigger extraction into a standardized template. This process parses summary fields and skill sections into structured YAML frontmatter without guessing missing company details.

Fit scoring computes a weighted match score by comparing normalized JD requirements against your master profile. It summarizes identified gaps and generates optional analysis or decision reports to help evaluate whether the opportunity aligns with your career goals.

Yes, company research and reuse allows you to refresh a canonical company brief and tie it to a new role. This ensures consistent employer context across multiple job descriptions without manually re-entering background information for fit analysis.

Opportunity tracking prepares a YAML snippet for the tracker schema and updates the JD catalog. It performs ID collision checks against config/jd_catalog.csv and applies conditional branching logic to handle new roles, re-posts, and re-opened positions automatically.

Gap analysis identifies missing skills or experience between a job description and your master profile during the scoring process. It is needed when evaluating role fit for career planning, producing summarized gap outputs that highlight areas for development before applying.

Yes, YAML frontmatter-driven execution is required to automate end-to-end JD intake, normalization, and scoring. It drives the template extraction, company brief reuse, fit scoring, and opportunity tracker integration across the entire career planning workflow.