# Summarize Call > Transcribe a call recording with speaker diarization, summarize it, and create Obsidian vault notes (call note, transcript, person notes for participants). Works with video or audio files. Supports local transcription (mlx_whisper + pyannote) or ElevenLabs Scribe. Use when this capability is needed. - Skill: `tomevault-io/summarize-call` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/summarize-call` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/summarize-call/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/summarize-call --- # Summarize Call Takes a call recording (video or audio), transcribes it with speaker labels, summarizes it, and writes structured notes into your Obsidian vault. ## Requirements **Vault structure** — the skill expects these folders inside your Obsidian vault. Folder names are defaults; override them in the Configuration block below if your vault uses different names. | Folder | Purpose | |---|---| | `03 Meetings/` | Where call notes and transcripts land | | `04 People/` | Person notes for participants | | `02 Daily/YYYY/MM/` | Daily notes, named `MM-DD-YY ddd.md` | | `_Templates/` | Note templates — skill installs `new person template.md` here on first run | **CLI tools** — install these before first use, or let Step 0 walk you through it: | Tool | Purpose | Install | |---|---|---| | `ffmpeg` | Extract audio from video files | macOS: `brew install ffmpeg` · Linux: `apt install ffmpeg` or `dnf install ffmpeg` · Windows: [ffmpeg.org/download.html](https://ffmpeg.org/download.html) | | `mlx_whisper` (local path) | Local transcription | `pip install mlx-whisper` | | `pyannote.audio` (local path) | Local speaker diarization | see Step 1 walkthrough | **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 ```bash 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: ${vault:-NOT FOUND}" ``` If no vault is found, ask the user: > **What's the absolute path to your Obsidian vault?** > Recommended: use a **new, dedicated Obsidian vault** for this skill — not your existing personal vault. The skill creates and modifies many notes and folders, and a clean vault avoids polluting your existing notes. If you don't have one yet, create an empty folder, open it in Obsidian (File → Open vault as folder), and paste that path here. After they answer, validate that `/.obsidian/` exists before using it — if not, warn that the path doesn't look like an Obsidian vault (they may need to open it in Obsidian first) and ask them to confirm or re-enter. Use the validated answer as `$VAULT_ROOT` for the session (and suggest they set it permanently in their shell profile). ### 0b. Check required folders ```bash for d in "$MEETINGS_DIR" "$PEOPLE_DIR" "$DAILY_DIR" "$TEMPLATES_DIR"; do [ -d "$VAULT_ROOT/$d" ] || echo "MISSING: $d" done ``` For each missing folder, ask the user: **"Create `` in your vault? [y/N]"** — if yes, `mkdir -p "$VAULT_ROOT/"`. ### 0c. Check required CLI tools ```bash 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** ```bash 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): ```bash PYANNOTE_ENV="${XDG_DATA_HOME:-$HOME/.local/share}/summarize-call/pyannote-env" [ -d "$PYANNOTE_ENV" ] || echo "MISSING: pyannote venv" ``` If missing, walk through setup: ```bash mkdir -p "$(dirname "$PYANNOTE_ENV")" # Create venv with uv (or python3 -m venv if uv not installed) uv venv "$PYANNOTE_ENV" source "$PYANNOTE_ENV/bin/activate" uv pip install pyannote.audio torch torchaudio ``` **3. HuggingFace token** Check for `HF_TOKEN`: ```bash [ -n "$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: ```bash [ -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 `` (`` 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 ```bash ffmpeg -v quiet -i "" -vn -acodec pcm_s16le -ar 16000 -ac 1 /tmp/.wav -y ``` ## Step 3: Transcribe + diarize ### Option A: Local (mlx_whisper + pyannote) **Transcribe:** ```bash mlx_whisper --model mlx-community/whisper-large-v3-turbo --language en \ --output-dir /tmp --output-format json \ --condition-on-previous-text False /tmp/.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:** ```python 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/.wav", num_speakers=) 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 ```python import requests, os url = "https://api.elevenlabs.io/v1/speech-to-text" headers = {"xi-api-key": os.environ["ELEVENLABS_API_KEY"]} with open("/tmp/.wav", "rb") as f: response = requests.post( url, headers=headers, files={"file": f}, data={ "model_id": "scribe_v1", "language_code": "", # e.g. "eng", "jpn" "diarize": "true", "timestamps_granularity": "word", "num_speakers": } ) 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/ Transcript.md` - Content: the merged, speaker-labeled transcript with timestamps - Frontmatter: ```yaml --- date: YYYY-MM-DD duration: meeting: "[[]]" unread: true --- ``` ### Call note - Location: `$MEETINGS_DIR/.md` - Frontmatter: ```yaml --- created: YYYY-MM-DDTHH:MM updated: YYYY-MM-DDTHH:MM tags: [call] date: YYYY-MM-DD start: HH:MM end: HH:MM duration: people: ["[[Participant 1]]", "[[Participant 2]]"] summary: "1-line description of call topics" transcript: "[[ Transcript]]" unread: true --- ``` - No `# Title` heading — filename is the title - Body structure: ```markdown > [!tldr] > [2-3 sentence overview] ## Key Topics - ... ## Decisions - ... ## Action Items - [ ] ... ## Notable Quotes > [!quote] [[Participant 1]] > "..." ## People Mentioned - [[Person Name]] — brief context ``` - `summary` frontmatter field is **mandatory** — never omit it - **Wikilink everything** — people, companies, products, concepts, places ### Person notes - For each participant: create at `$PEOPLE_DIR/.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: ```markdown - [[]] — 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`: ```bash grep -oE '\[\[[^]|#^]+' "" | sed 's/\[\[//' | sort -u for term in ; 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 --- > Source: [reysu/ai-life-skills](https://github.com/reysu/ai-life-skills) — distributed by [TomeVault](https://tomevault.io).