transcribe-refiner

Original：🇺🇸 English

Translated

Clean and reconstruct raw auto-generated captions (Zoom, YouTube, Teams, Google Meet, Otter.ai, etc.) into readable, coherent transcripts. Use when the user provides raw caption files (.txt, .vtt, .srt), meeting transcripts with timestamps and speaker tags, or asks to clean up/refine a transcript. Handles: timestamp removal, speaker tag normalization, filler word removal, broken sentence reconstruction, transcription error correction, paragraph formation. Preserves every piece of substantive content while removing noise. Trigger phrases: 'clean this transcript', 'refine captions', 'fix this transcript', 'process Zoom captions', 'clean up meeting notes'.

1installs

Sourceprakharmnnit/skills-and-personas

Added on2026-02-09

NPX Install

npx skill4agent add prakharmnnit/skills-and-personas transcribe-refiner

SKILL.md Content

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Transcribe Refiner - Caption Cleanup Engine

Transform raw auto-generated captions into clean, readable transcripts with zero content loss.

Core Purpose

Auto-generated captions (Zoom, YouTube, Teams, etc.) are messy: fragmented sentences, timestamps everywhere, speaker tags on every line, filler words, transcription errors. This skill reconstructs them into coherent, flowing text that can be consumed by humans or downstream skills (like lecture-alchemist).

Critical Rules

Zero Content Loss

Every substantive statement, technical term, concept, question, and answer from the raw captions MUST appear in the output. Only noise is removed, never content.

Remove: Timestamps, redundant speaker tags, filler words (um, uh, basically, right?, you know), technical interruptions ("can you hear me?", "let me share my screen"), duplicate sentences from reconnection.

Preserve: Every teaching point, code reference, question asked, answer given, tangent with value, name, URL, command, or technical term.

Smart Error Correction

Auto-captions make predictable errors. Fix them using domain context:

Common Error	Likely Correct	Domain Clue
"lowest function"	"loss function"	AI/ML context
"wait"	"weight"	neural network context
"epic"	"epoch"	training context
"by Torch"	"PyTorch"	ML framework
"relaunch bowl"	"relaunch poll"	Zoom context
"solidity" vs "Solidity"	capitalize if Web3	Web3 context
"know JS"	"Node.js"	WebDev context
"react" vs "React"	capitalize if framework	WebDev context

When uncertain about a correction, keep the original and flag it:

[unclear: "original text"]

Speaker Handling

Identify unique speakers from tags
Normalize names (e.g.,
```
[rishabh]
```
→
```
**Rishabh:**
```
)
Only include speaker attribution at natural conversation changes
For single-speaker lectures, omit speaker tags entirely after initial identification
For Q&A, clearly mark:
```
**Student:**
```
and
```
**Instructor:**
```

Input Formats

Format	Characteristics	Handling
Zoom captions (.txt)	`[speaker] HH:MM:SS\ntext`	Strip timestamps, merge fragments
YouTube (.vtt/.srt)	Numbered blocks with timecodes	Strip timecodes and sequence numbers
Otter.ai	Speaker-labeled paragraphs	Normalize speaker labels
Teams	Timestamped speaker blocks	Strip timestamps, merge
Raw paste	Mixed format	Auto-detect and clean

Processing Steps

Strip noise - Remove timestamps, sequence numbers, formatting artifacts
Merge fragments - Join broken sentences across caption blocks
Remove filler - Strip "um", "uh", "basically", "right?", "you know" (but keep if they carry meaning like "right?" as a genuine question)
Fix transcription errors - Use domain context to correct obvious misrecognitions
Remove technical interruptions - "Can you hear me?", "Let me share my screen", "Is my screen visible?", connection issues
Form paragraphs - Group related sentences into natural paragraphs by topic
Identify sections - Insert
```
---
```
breaks at major topic transitions
Normalize Q&A - Clearly separate questions from instruction
Add metadata header - Speaker(s), estimated duration, domain detected

Output Format

markdown

# Transcript: [Topic/Title if identifiable]

**Speaker(s):** [Name(s)]
**Estimated Duration:** [from timestamp range]
**Domain:** [Auto-detected: WebDev / AI-ML / Web3 / DSA / General]
**Cleaning Notes:** [e.g., "Fixed 12 transcription errors, removed ~45 filler instances"]

---

[Clean, flowing paragraphs organized by topic]

[Natural paragraph breaks at topic changes]

---

[Next topic section]

---

## Q&A Segments

**Student:** [Question]

**Instructor:** [Answer]

Topic Inventory (Anti-Loss System)

This is the critical mechanism that prevents data loss across the pipeline. After cleaning, generate a Topic Inventory at the end of output -- a manifest of every substantive item found in the transcript.

markdown

## Topic Inventory

### Concepts Mentioned
1. [Concept] - paragraph [N]
2. [Concept] - paragraph [N]
...

### Technical Terms Introduced
- [term]: first mentioned in paragraph [N]
...

### Code/Commands Referenced
- [code snippet or command] - paragraph [N]
...

### Questions Asked (Q&A)
- Q: [question summary] - paragraph [N]
...

### Names/Resources Mentioned
- [name, URL, tool, book, etc.]
...

### Corrections Applied
| Original Caption | Corrected To | Confidence |
|-----------------|-------------|------------|
| "lowest function" | "loss function" | High |
| "epic" | "epoch" | High |
| [unclear text] | [kept as-is] | Low |

### Stats
- Raw caption blocks: [N]
- Substantive paragraphs produced: [N]
- Filler instances removed: [N]
- Transcription errors corrected: [N]
- Uncertain corrections flagged: [N]

This inventory travels to the next stage (lecture-alchemist) for cross-verification. Every item in this inventory MUST appear in the final notes.

Timestamp Anchors

Preserve approximate timestamps as hidden anchors for key topic transitions. Format:

markdown

<!-- T:20:36:30 --> Neural network architecture introduction
<!-- T:20:45:12 --> Activation functions
<!-- T:21:03:45 --> Training loop

These allow the reader to jump back to the recording at specific points.

Quality Checklist

Before output, verify:

Every teaching point from raw input is in the output
Topic Inventory is complete and accurate
Transcription errors corrected using domain context
Uncertain corrections flagged with
```
[unclear: ...]
```
Filler words removed without losing meaning
Sentences properly merged (no mid-word breaks)
Q&A segments clearly separated
Technical interruptions removed
Timestamp anchors placed at topic transitions
Output reads as natural, flowing text

Pipeline Position

This skill is Stage 1 in the lecture processing pipeline:

transcribe-refiner (this) → clean transcript + Topic Inventory
lecture-alchemist → structured study notes (verifies against inventory)
concept-cartographer → visual diagrams (verifies against inventory)
obsidian-markdown → Obsidian vault formatting

transcribe-refiner

NPX Install

Tags

SKILL.md Content

Transcribe Refiner - Caption Cleanup Engine

Core Purpose

Critical Rules

Zero Content Loss

Smart Error Correction

Speaker Handling

Input Formats

Processing Steps

Output Format

Topic Inventory (Anti-Loss System)

Timestamp Anchors

Quality Checklist

Pipeline Position