evaluate-a-candidate

To evaluate a candidate from a LinkedIn profile, you provide the public URL and the skill scrapes the page data to score it against your role rubric. This generates a 0-100 numeric score with sub-scores, red flags, and interview probes.

Yes, you can batch multiple resume PDFs for candidate screening by processing them into individual per-candidate records. The system appends dated evaluation sections to each candidate file and writes an overall ranked summary to outputs.json.

Yes, candidate evaluation explicitly avoids protected-class inference when scoring resumes or LinkedIn profiles against the role rubric. It focuses strictly on criterion-level pass, borderline, or fail reasons to generate objective evidence and interview probes.

To generate a ranked pipeline from LinkedIn scraping, you need linked integrations for the chosen source, people context, and a role-specific requirements rubric. This allows the system to scrape profile URLs and output numeric bands with sub-scores.

The best way to generate interview probes from a candidate dossier is to run the resume or LinkedIn profile through the role rubric scoring process. It automatically produces actionable interview guidance and red flags without inventing credentials.