Transcript Summarizer

Transcript Summarizer Skill

Purpose

Convert meeting transcripts into structured, actionable summaries. Supports multiple transcript formats and LLM backends.

Environment Variables

When to Use

• After a meeting recording has been transcribed
• Processing VTT/SRT captions from video calls
• Summarizing pasted meeting notes
• Extracting action items and decisions from long discussions

Output Structure

Each meeting produces a directory with three files:

${REFLEX_TRANSCRIPT_DST_DIR:-./meetings}/
└── <YYYY-MM-DD>/
    └── <HH-MM>/
        ├── original.txt    # Raw transcript (unmodified source)
        ├── readable.md     # Cleaned, formatted transcript
        └── summary.md      # Structured summary (stored in Qdrant)

File Descriptions

original.txt

• Exact copy of the input transcript
• Preserves VTT/SRT timestamps, formatting artifacts, etc.
• Useful for debugging or re-processing with different settings

readable.md

• Cleaned transcript with preprocessing applied (see Transcript Format Preprocessing)
• Speaker labels normalized
• Timestamps and artifacts removed
• Consecutive same-speaker lines merged
• Human-readable format for reviewing what was actually said

summary.md

• Structured summary following the Summary Template
• This is the file stored in Qdrant for RAG retrieval
• Contains executive summary, decisions, action items, etc.

Directory Naming

• Date: ISO format YYYY-MM-DD (e.g., 2024-01-15)
• Time: 24-hour format HH-MM (e.g., 14-30 for 2:30 PM)
• If meeting time is unknown, use 00-00 or prompt user

Workflow

1. Copy original transcript to original.txt
2. Clean transcript using format-specific preprocessing → readable.md
3. Summarize cleaned transcript → summary.md
4. Store summary.md content in Qdrant with metadata pointing to directory

Summary Template

The summarizer produces this structured output:

# Meeting Summary: <title>

**Date:** <YYYY-MM-DD>
**Attendees:** <comma-separated names>
**Duration:** <if detectable from timestamps>

## Executive Summary

<2-4 sentence overview of the meeting's purpose and outcomes>

## Key Topics

1. **<Topic>** - <1-sentence description>
2. **<Topic>** - <1-sentence description>
   (3-7 topics)

## Decisions Made

- **<Decision>**: <reasoning or context>
- **<Decision>**: <reasoning or context>

## Action Items

| Action | Owner | Deadline |
|--------|-------|----------|
| <task> | <person> | <date or TBD> |

## Open Questions

- <Question or unresolved item>
- <Question or unresolved item>

Extraction Cues

Decisions

Look for phrases indicating agreement or resolution:

• "let's go with", "we decided", "agreed", "the plan is"
• "we'll use", "going forward", "the approach will be"
• Unanimous or majority agreement markers

Action Items

Look for commitment language:

• "I'll do", "I will", "I can take that"
• "can you", "please handle", "your task is"
• "@name" followed by a task
• "by Friday", "next week", "before the release"

Open Questions

Look for unresolved items:

• "TBD", "to be determined", "parking lot"
• "we need to figure out", "open question"
• "let's revisit", "follow up on"
• Questions without clear answers in the transcript

Attendees

• Speaker labels (e.g., "John:", "Sarah Smith:")
• "attendees:", "participants:", "present:"
• Names mentioned in greetings ("hi John", "thanks Sarah")

Transcript Format Preprocessing

VTT (WebVTT)

• Strip WEBVTT header and metadata lines
• Remove timestamp lines (00:00:00.000 --> 00:00:05.000)
• Remove position/alignment tags (<c>, align:, position:)
• Deduplicate rolling captions (many VTT files repeat lines with slight timestamp shifts)
• Merge consecutive lines from same speaker

SRT (SubRip)

• Strip sequence numbers (standalone integers)
• Remove timestamp lines (00:00:00,000 --> 00:00:05,000)
• Remove blank separator lines
• Merge consecutive same-speaker lines

Plain Text

• Use as-is
• Detect speaker labels: Name:, [Name], SPEAKER_01:
• Normalize speaker label formats for consistency

DOCX

• Extract paragraph text via python-docx
• Preserve heading structure
• Strip formatting artifacts

Google Doc

• Fetched via Google Workspace MCP as plain text
• Treat same as plain text after retrieval

Long Transcript Strategy

Threshold: 30,000 words

Single Pass (<30K words)

Send entire cleaned transcript to LLM with the system prompt and template.

Two-Pass Chunked (>30K words)

1. Chunk: Split at ~20,000 word boundaries, preferring natural breaks (speaker changes, topic shifts, timestamp gaps)
2. Extract: Summarize each chunk independently, extracting topics, decisions, action items, and questions
3. Synthesize: Combine chunk summaries into a single coherent summary, deduplicating items and merging topics

Qdrant Storage Schema

Always store summaries in Qdrant for RAG retrieval. The full summary content must be stored in the information field to enable semantic search across meeting contents.

Information Field (embedded content): Store the complete generated summary markdown, including:

• Executive summary
• Key topics with descriptions
• Decisions with reasoning
• Action items (as formatted text)
• Open questions

This enables queries like "what did we decide about X?" or "who is responsible for Y?" to find relevant meetings.

Metadata Fields:

source: "meeting_transcript"
content_type: "meeting_summary"
harvested_at: "<ISO 8601 timestamp>"

# Meeting context
meeting_title: "<title>"
meeting_date: "<YYYY-MM-DD>"
meeting_time: "<HH-MM>"
attendees: "<comma-separated names>"
output_dir: "<path to YYYY-MM-DD/HH-MM directory>"
source_format: "<vtt|srt|txt|docx|gdoc|pasted>"

# Extracted counts (for filtering)
action_item_count: <integer>
decision_count: <integer>
topics: "<comma-separated key topics>"

# Classification
category: "business"
type: "meeting_summary"
confidence: "high"

LLM System Prompt

The summarize.py script uses this system prompt:

You are a meeting transcript summarizer. Your job is to extract structured
information from meeting transcripts.

Given a transcript, produce a summary with these sections:
- Executive Summary (2-4 sentences)
- Key Topics (3-7 bullet points)
- Decisions Made (with reasoning)
- Action Items (action, owner, deadline as table rows)
- Open Questions (unresolved items)

Rules:
- Only include information explicitly stated in the transcript
- If attendees are not clear, note "Attendees not identified"
- If no decisions were made, state "No explicit decisions recorded"
- Mark deadlines as "TBD" when not specified
- Keep the executive summary factual, not interpretive