offerlens

Convert local interview recordings into structured Chinese interview review reports.

Updated May 17, 2026
One-click install
npx skills add https://github.com/YoungBossX/OfferLens --skill offerlens
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: offerlens
Source: https://github.com/YoungBossX/OfferLens/tree/main/skill/offerlens
Command: npx skills add https://github.com/YoungBossX/OfferLens --skill offerlens

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It turns local interview audio/video into a structured Chinese interview review so you can quickly identify what you said, how well it matched the interviewer’s intent, and what to improve next.

Core Features & Use Cases

  • Local-first pipeline: extracts audio locally, runs local ASR (whisperx/faster-whisper/mlx-whisper/openai-whisper), infers speaker roles, and keeps evidence on your machine by default.
  • Interview signal extraction: converts transcripts into interviewer questions, your cleaned answers, key follow-ups, and concise sourced fixes.
  • Compact multi-format report: renders Markdown/HTML and optionally LaTeX/PDF as a compact, review-ready report (not a raw transcript or long course plan).

Quick Start

Ask the AI to run an OfferLens interview review for your local interview file and produce the compact sourced Markdown/HTML report.

Frequently Asked Questions about offerlens

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate a structured interview review from local audio recordings?

The Skill performs local ASR and speaker diarization to separate interviewer and candidate turns, extracting interviewer questions and your cleaned answers into a compact sourced Markdown report for targeted interview practice.

Do I need ffmpeg and a local ASR backend to extract interview Q&A from recordings?

Yes, ffmpeg is required for audio extraction, and you must install at least one local ASR backend like whisperx, faster-whisper, mlx-whisper, or openai-whisper to transcribe the interview recording locally.

Can I convert an interview transcript into a LaTeX PDF report locally?

Yes, you can convert interview transcripts into a LaTeX PDF report locally, provided you have the optional LaTeX environment installed alongside the required local ASR backend and ffmpeg dependencies.

What is the best way to review technical interview answers without uploading audio to the cloud?

The best way is a privacy-first local pipeline that processes audio on your machine, keeping all evidence local while generating concise suggested improvements and high-risk technical point summaries from your interview answers.

Does speaker diarization work for separating interviewer and candidate roles in a local transcript?

Yes, the Skill infers interviewer and candidate turns from the local transcript, applying speaker role inference to separate questions and answers for code-question review and interviewer question coverage analysis.