Video Subtitle Error Fixer ⭐

The problem

Automated transcription often produces phonetic errors, poor line breaks, and drifting timestamps that make subtitles unreadable. Manually auditing SRT or VTT files against a transcript to fix technical jargon and speaker labels is a tedious, error-prone process.

What it does

• Identifies and corrects phonetically confused words, homophones, and broken phrases.
• Audits timestamp syntax to detect overlaps, sequence gaps, and unrealistic display durations.
• Optimizes line-breaks and segmentation based on natural phrase boundaries and semantic flow.
• Reconciles subtitle text against provided source transcripts to ensure technical terms and proper names are accurate.
• Standardizes speaker attribution and formatting across SRT and VTT formats.

Why this beats prompting it yourself

Generic LLM prompts often "hallucinate" spoken words or rewrite dialogue to be grammatically perfect, losing the original speaker's intent. This skill uses a multi-step workflow—Parse, Check, Correct, Validate—that prioritizes fidelity to the source and labels confidence levels for every change, ensuring you never silently lose critical meaning.

Use cases

• Reviewing AI-generated captions for technical webinars or product demos.
• Fixing line-break issues in SRT files for better readability on mobile screens.
• Auditing speaker labels in multi-person interview transcripts.
• Synchronizing VTT files where timestamps have drifted from the spoken content.

Known limitations

Precise speech-to-audio synchronization cannot be guaranteed without direct audio/video file access. Critical proper names are flagged rather than guessed if context is missing.