interview-processor

You can generate an interview summary by providing the transcript, candidate resume, and job description. The skill cleans transcription errors, organizes discussion topics, and outputs structured candidate feedback with interviewer self-evaluation.

Yes, the skill generates customized interview outlines by analyzing job requirements, candidate resumes, interviewer preferences, and evaluation goals. This transforms preparation data into structured interview planning documents.

Yes, knowledge graph storage integration is required. The skill uses memories-off entities and structured workflows to store interview methodologies, question sets, and interviewer profiles, preserving hiring insights for future use.

Processing raw interview notes and transcripts through the skill is the best way to extract real interviewer questions. It identifies actual questions asked, cleans transcription errors, and organizes discussion topics systematically.

Transcript analysis cleans raw interview text by correcting transcription mistakes during processing. It extracts real interviewer questions, organizes discussion topics, and produces structured candidate feedback and interviewer self-evaluation.

Preserving hiring insights is necessary when building a reusable recruitment knowledge base. The skill stores interview methodologies, question sets, and interviewer profiles via memories-off entities to enable standardized future evaluations.