opportunity-from-jd

To convert a raw job description into a structured YAML opportunity entry, the Skill extracts role_title and company_display fields without guessing, then generates an active item with initialized id, stage, priority, and next_action fields ready for data/opportunities.yaml.

Job description ingestion for pipeline tracking works by parsing raw JD text to populate validated assumptions for stage and priority fields, outputting a ready-to-append YAML snippet alongside an optional CSV catalog row for consistent opportunity initialization.

Yes, you can generate a CSV catalog row from a saved JD file. The Skill optionally prepares a job catalog CSV line for config/jd_catalog.csv to support catalog tracking and normalization alongside the primary YAML output.

The best way to bootstrap consistent IDs and next-action fields from unstructured JD text is using an automated extraction tool that validates assumptions and outputs structured YAML, eliminating manual data entry errors during opportunity initialization.

Yes, this job description parsing tool fills missing pipeline fields automatically. It creates stage, next_action, next_action_date, priority, notes, and updated_at fields using clear, validated assumptions when information is missing from the raw JD document.

You should not use automated YAML generation for job opportunity tracking when the raw job description lacks explicit role_title or company_display information, as the Skill requires these fields for extraction and does not guess them.