Summarize Call

Alternative to local: set ELEVENLABS_API_KEY to use ElevenLabs Scribe for transcription + diarization in one call (paid, faster, no setup).

Configuration

The skill reads these variables at runtime. Override any of them via environment variables, or edit the defaults here:

VAULT_ROOT       = $VAULT_ROOT        # auto-detected if not set (see Step 0a)
MEETINGS_DIR     = 03 Meetings
PEOPLE_DIR       = 04 People
DAILY_DIR        = 02 Daily
TEMPLATES_DIR    = _Templates

All paths below are relative to $VAULT_ROOT.

Trigger

When the user provides a call recording (MP4, MOV, WAV, MP3, M4A, etc.) and wants it transcribed, summarized, and documented.

Inputs

• Recording: file path to the recording
• Participants: names of the people on the call
• Date/time: extract from filename if possible, otherwise ask
• Speaker count: default 2, ask if ambiguous

Step 0: Bootstrap check (first run)

Before doing any work, verify the environment is ready. Skip any check that already passes — only prompt the user when something is actually missing.

0a. Resolve the vault root

vault=""
if [ -n "$VAULT_ROOT" ]; then
  vault="$VAULT_ROOT"
else
  dir="$PWD"
  while [ "$dir" != "/" ]; do
    if [ -d "$dir/.obsidian" ]; then vault="$dir"; break; fi
    dir="$(dirname "$dir")"
  done
fi
echo "Vault: ${vault:-NOT FOUND}"

If no vault is found, ask the user:

What's the absolute path to your Obsidian vault? Recommended: use a new, dedicated Obsidian vault for this skill — not your existing personal vault. The skill creates and modifies many notes and folders, and a clean vault avoids polluting your existing notes. If you don't have one yet, create an empty folder, open it in Obsidian (File → Open vault as folder), and paste that path here.

After they answer, validate that <answer>/.obsidian/ exists before using it — if not, warn that the path doesn't look like an Obsidian vault (they may need to open it in Obsidian first) and ask them to confirm or re-enter. Use the validated answer as $VAULT_ROOT for the session (and suggest they set it permanently in their shell profile).

0b. Check required folders

for d in "$MEETINGS_DIR" "$PEOPLE_DIR" "$DAILY_DIR" "$TEMPLATES_DIR"; do
  [ -d "$VAULT_ROOT/$d" ] || echo "MISSING: $d"
done

For each missing folder, ask the user: "Create <folder> in your vault? [y/N]" — if yes, mkdir -p "$VAULT_ROOT/<folder>".

0c. Check required CLI tools

command -v ffmpeg >/dev/null 2>&1 || echo "MISSING: ffmpeg"

If ffmpeg is missing, ask the user before installing. Install command depends on the platform:

• macOS: brew install ffmpeg
• Linux (Debian/Ubuntu): sudo apt install ffmpeg
• Linux (Fedora/RHEL): sudo dnf install ffmpeg
• Windows: download from https://ffmpeg.org/download.html

0d. Install the person template if missing

If $VAULT_ROOT/$TEMPLATES_DIR/new person template.md does not exist, ask the user which version to install:

Install person template — which version?

1. Minimal (default, works in any vault)
2. Full (requires Dataview plugin + Obsidian Bases)

Copy the chosen template from the repo's shared templates/ directory (sibling of this skill dir, i.e. ../templates/) into $VAULT_ROOT/$TEMPLATES_DIR/new person template.md. If the file already exists, leave it alone — the user may have customized it.

Once Step 0 passes, proceed to Step 0.5.

Step 0.5: Determine depth mode

Before transcribing, establish which depth the user wants:

1. Scan the invocation first. If the user's request already specifies a mode, use it and skip the prompt:
   • Words like minimal, fast, quick, --minimal, -m → minimal mode
   • Words like detailed, deep, full, --detailed, -d → detailed mode
2. Otherwise, prompt. No default — if unspecified, ask every time:

Depth?

1. Detailed (best results) — person notes for every person mentioned, including third parties name-dropped mid-call (celebrities, YouTubers, mutual friends). Public figures get researched biographies.
2. Minimal (fast) — person notes for call participants only. Name-drops mid-call stay as dangling wikilinks.

This keeps interactive runs explicit while letting scheduled tasks / cron / /loop pass the mode in the invocation (e.g. /summarize-call ~/call.mp4 minimal) without blocking on input.

The chosen mode determines how Step 6 runs.

Step 1: Choose transcription method

Ask the user:

Transcription method?

1. Local (mlx_whisper + pyannote — free, private, slower, requires setup)
2. ElevenLabs Scribe (paid, faster, handles transcription + diarization in one call)

Option A: Local — walkthrough if not set up

If the user picks local, verify each component and walk them through any missing piece:

1. mlx_whisper

command -v mlx_whisper >/dev/null 2>&1 || echo "MISSING: mlx_whisper"

If missing, ask before installing: pip install mlx-whisper

2. pyannote.audio environment

Check for the venv at ~/.local/share/summarize-call/pyannote-env (persists across reboots, XDG-compliant):

PYANNOTE_ENV="${XDG_DATA_HOME:-$HOME/.local/share}/summarize-call/pyannote-env"
[ -d "$PYANNOTE_ENV" ] || echo "MISSING: pyannote venv"

If missing, walk through setup:

mkdir -p "$(dirname "$PYANNOTE_ENV")"
# Create venv with uv (or python3 -m venv if uv not installed)
uv venv "$PYANNOTE_ENV"
source "$PYANNOTE_ENV/bin/activate"
uv pip install pyannote.audio torch torchaudio

3. HuggingFace token

Check for HF_TOKEN:

[ -n "$HF_TOKEN" ] || echo "MISSING: HF_TOKEN"

If missing, tell the user:

You need a HuggingFace token with access to the pyannote gated repos.

1. Create a token at https://huggingface.co/settings/tokens (choose "Read" scope)
2. Accept the terms for all three repos while logged in:
   • https://huggingface.co/pyannote/speaker-diarization-3.1
   • https://huggingface.co/pyannote/segmentation-3.0
   • https://huggingface.co/pyannote/speaker-diarization-community-1
3. Export for this session: export HF_TOKEN="hf_..."
4. To persist, add that line to your ~/.zshrc (or ~/.bashrc)

Wait for the user to confirm before continuing.

Option B: ElevenLabs Scribe — walkthrough if not set up

If the user picks ElevenLabs, check for the API key:

[ -n "$ELEVENLABS_API_KEY" ] || echo "MISSING: ELEVENLABS_API_KEY"

If missing, tell the user:

You need an ElevenLabs API key.

1. Grab one at https://elevenlabs.io/app/settings/api-keys
2. Export for this session: export ELEVENLABS_API_KEY="..."
3. To persist, add that line to your ~/.zshrc (or ~/.bashrc)

Wait for the user to confirm before continuing.

Before calling Scribe, always print the recording duration and a pricing heads-up so the user can confirm:

This recording is <HH:MM:SS> (<minutes> min). Check ElevenLabs pricing at https://elevenlabs.io/pricing for the current per-minute rate on the Scribe model. Continue? [y/N]

Step 2: Extract audio

ffmpeg -v quiet -i "<input>" -vn -acodec pcm_s16le -ar 16000 -ac 1 /tmp/<name>.wav -y

Step 3: Transcribe + diarize

Option A: Local (mlx_whisper + pyannote)

Transcribe:

mlx_whisper --model mlx-community/whisper-large-v3-turbo --language en \
  --output-dir /tmp --output-format json \
  --condition-on-previous-text False /tmp/<name>.wav

• Use --language en for English calls
• For Japanese: --language ja (or use a kotoba-whisper model for better accuracy)
• --condition-on-previous-text False prevents whisper hallucination loops
• Start processing partial results while transcription is still running — don't block

Diarize with pyannote:

from pyannote.audio import Pipeline
import torch, os

pipeline = Pipeline.from_pretrained(
    "pyannote/speaker-diarization-3.1",
    use_auth_token=os.environ["HF_TOKEN"]
)
device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
pipeline.to(torch.device(device))  # GPU if available, CPU otherwise

output = pipeline("/tmp/<name>.wav", num_speakers=<N>)
annotation = output.speaker_diarization
for turn, _, speaker in annotation.itertracks(yield_label=True):
    # save turn.start, turn.end, speaker

Note: use output.speaker_diarization.itertracks(yield_label=True) (not output.itertracks).

Merge transcript + diarization:

• Map whisper segments to speaker turns by matching timestamps
• Merge consecutive same-speaker segments into paragraphs
• Format: [H:MM:SS] **Speaker**: text

Option B: ElevenLabs Scribe

import requests, os

url = "https://api.elevenlabs.io/v1/speech-to-text"
headers = {"xi-api-key": os.environ["ELEVENLABS_API_KEY"]}

with open("/tmp/<name>.wav", "rb") as f:
    response = requests.post(
        url,
        headers=headers,
        files={"file": f},
        data={
            "model_id": "scribe_v1",
            "language_code": "<lang>",  # e.g. "eng", "jpn"
            "diarize": "true",
            "timestamps_granularity": "word",
            "num_speakers": <N>
        }
    )

result = response.json()

Scribe handles both transcription AND diarization in one call — no pyannote needed. Format the result the same way: [H:MM:SS] **Speaker**: text.

Step 4: Summarize

• Model choice depends on depth mode:
  • Detailed: use the highest-quality model available (Opus if the user has access, otherwise Sonnet)
  • Minimal: Sonnet — good enough for conversational content at ~5x lower cost
• For long transcripts (>3000 words), split into chunks and summarize each in parallel, then combine
• Extract: key topics, decisions, action items, notable quotes (with speaker attribution)

Step 5: Create vault notes

Transcript file

• Location: $MEETINGS_DIR/<MM-DD-YY Day Participant1 x Participant2> Transcript.md
• Content: the merged, speaker-labeled transcript with timestamps
• Frontmatter:

  ---
  date: YYYY-MM-DD
  duration: <seconds>
  meeting: "[[<Call Note Title>]]"
  unread: true
  ---
  

Call note

• Location: $MEETINGS_DIR/<MM-DD-YY Day Participant1 x Participant2>.md
• Frontmatter:

  ---
  created: YYYY-MM-DDTHH:MM
  updated: YYYY-MM-DDTHH:MM
  tags: [call]
  date: YYYY-MM-DD
  start: HH:MM
  end: HH:MM
  duration: <seconds>
  people: ["[[Participant 1]]", "[[Participant 2]]"]
  summary: "1-line description of call topics"
  transcript: "[[<Call Note Title> Transcript]]"
  unread: true
  ---
  

• No # Title heading — filename is the title
• Body structure:

  > [!tldr]
  > [2-3 sentence overview]
  
  ## Key Topics
  - ...
  
  ## Decisions
  - ...
  
  ## Action Items
  - [ ] ...
  
  ## Notable Quotes
  > [!quote] [[Participant 1]]
  > "..."
  
  ## People Mentioned
  - [[Person Name]] — brief context
  

• summary frontmatter field is mandatory — never omit it
• Wikilink everything — people, companies, products, concepts, places

Person notes

• For each participant: create at $PEOPLE_DIR/<Full Name>.md using the template at $VAULT_ROOT/$TEMPLATES_DIR/new person template.md
• Extract ALL biographical details mentioned in the call (location, career, family, background)
• For mid-call name-drops: behavior depends on depth mode (see Step 6)

Daily note

• Update $VAULT_ROOT/$DAILY_DIR/YYYY/MM/MM-DD-YY ddd.md (create YYYY/MM/ if missing)
• No # Title heading — filename is the title
• Set unread: true in frontmatter
• Add under a ## calls/meetings section:

  - [[<Call Note Title>]] — brief description
  

Step 6: Handle mid-call name-drops

Detailed mode

For every person, company, product, or concept wikilinked in the call note (that isn't already a note), create a reference or person note:

• People: research public figures (birthday, career, links); private individuals get minimal notes based only on what was said
• Concepts / companies / products: create in 07 References/ (or $REFERENCES_DIR if you have the /summarize skill installed) with a 2-4 sentence explanation
• For large numbers of notes (>10 missing), dispatch parallel subagents (highest available model) in batches of ~20

After all notes are created, audit for dangling links. The regex excludes | (alias), # (heading ref), and ^ (block ref) so [[Target|Alias]], [[Page#Heading]], and [[Page^block]] all resolve to the canonical note name Target / Page:

grep -oE '\[\[[^]|#^]+' "<call_note_path>" | sed 's/\[\[//' | sort -u
for term in <each>; do
  found=$(find "$VAULT_ROOT" -name "$term.md" -not -path "*/.Trash/*" 2>/dev/null | head -1)
  [ -z "$found" ] && echo "MISSING: $term"
done

Re-create any missed notes. The call is not done until zero dangling links remain.

Minimal mode

Create person notes only for call participants (those in the people frontmatter). All other wikilinks — mid-call name-drops, concepts, companies — stay dangling. Skip the audit.

Key rules

1. Wikilink everything in the call note — every person, company, concept, place
2. No # Title headings — Obsidian shows filename as title
3. Never repeat frontmatter in body
4. summary frontmatter is mandatory on call notes
5. unread: true on every note created
6. Model choice: detailed uses the highest available model (Opus if accessible, else Sonnet); minimal uses Sonnet. Never Haiku.
7. Always --condition-on-previous-text False on mlx_whisper to prevent hallucination loops
8. Auto-detect device for pyannote (CUDA → MPS → CPU) so it works on any platform
9. Person note ## updates links to the call note, never the daily note