meeting-processor

What this skill does

# Meeting Processor

Intelligent meeting transcript processor that auto-detects meeting type and applies type-specific extraction with optional interactive clarification.

## When to Use

- After syncing Fathom or Granola transcripts (`/fathom --today`, `/granola export`)
- When asked to process, analyze, or summarize a meeting transcript
- When a new meeting transcript appears in the vault root matching `YYYYMMDD-*.md`
- For coaching sessions, delegate to `coaching-session-summarizer` skill instead

## Prerequisites

```bash
pip install openai pyyaml
```

Requires `CEREBRAS_API_KEY` environment variable (uses Cerebras API with llama-3.3-70b).

## Supported Meeting Types

| Type | Description | Key Extractions |
|------|-------------|-----------------|
| **leadgen** | Sales/business development calls | Commitments, pain points, budget, timeline, decision makers, deal stage, sentiment |
| **partnership** | Collaboration/partnership exploration | Opportunity overview, value proposition, strategic alignment, technical needs, fit assessment |
| **coaching** | Coaching/mentoring sessions | Insights, decisions, action items, themes, emotional arc, techniques, session quality |
| **internal** | Internal team meetings | Coming soon |

## Usage

### Interactive Mode (default)

Run the processor, which auto-detects meeting type and asks clarifying questions:

```bash
python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --mode interactive
```

**Interactive flow:**
1. Script analyzes transcript and detects meeting type
2. Extracts structured data via LLM
3. Identifies missing/ambiguous fields
4. Returns questions as JSON (exit code 2 signals interaction needed)
5. Parse the JSON between `__INTERACTIVE_QUESTIONS__` markers
6. Use AskUserQuestion to collect answers for each question
7. Save answers to a temp JSON file and re-run with `process_with_answers.py`

**Handling interactive questions:**

When the script exits with code 2, parse the output for questions JSON. Each question has:
- `question`: The question text
- `header`: Short label (used as answer key)
- `options`: Array of `{label, description}` for AskUserQuestion

After collecting answers, create two temp files:
- `questions.json` — the original questions context (includes `partial_data`, `meeting_type`, `transcript_file`)
- `answers.json` — map of `{header_lowercase: selected_label}`

Then run:
```bash
python3 ~/.claude/skills/meeting-processor/scripts/process_with_answers.py questions.json answers.json
```

### Batch Mode

Extract only high-confidence information without user interaction:

```bash
python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --mode batch
```

### Force Meeting Type

Skip auto-detection:

```bash
python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --type leadgen
python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --type partnership
```

## Output

Analysis is appended to the transcript file as a `## Meeting Analysis` section. Frontmatter is updated with `meeting_type`, `processed_date`, and `processing_mode`.

### Leadgen Output Structure

- **Commitments & Actions** — with deadlines and owners
- **Follow-up** — next meeting date if scheduled
- **Client Context** — pain points, budget, timeline, decision makers
- **Deal Assessment** — stage (cold/warm/hot), probability (1-5), blocker, sentiment

### Partnership Output Structure

- **Opportunity** — description and value proposition for both sides
- **Commitments & Actions** — with deadlines and owners
- **Follow-up** — next meeting date if scheduled
- **Partnership Context** — strategic alignment, technical needs, resources, challenges
- **Opportunity Assessment** — fit (strong/medium/weak), readiness, success factors, sentiment

## Step 2: Auto-Link Prep Notes

After the meeting analysis is complete (Step 1), automatically link any matching meeting-prep notes to the session note. This replaces the need to manually run `/meeting-prep link`.

### How It Works

1. **Derive the meetings directory** from the processed session note's parent directory (do not hardcode paths).

2. **Extract session metadata** from the processed note:
   - `date` from frontmatter (YYYYMMDD format)
   - `participants` from frontmatter (list of names)
   - If no `participants` field, extract names from the transcript header or attendee list

3. **Search for matching prep notes**:
   ```bash
   find <MEETINGS_DIR> -name "YYYYMMDD-prep-*" -type f 2>/dev/null
   ```
   Where `YYYYMMDD` is the session date.

4. **Validate the match**: For each candidate prep note, read its frontmatter and confirm:
   - The `date` field matches the session date
   - The `participant` field matches one of the session's participants (fuzzy: check both full name and first name, case-insensitive)
   - The `session_note` field is empty (`""`) — skip already-linked prep notes

5. **Update both files** when a match is found:

   **In the prep note:**
   - Set `session_note: "[[session-note-filename]]"` (without `.md` extension)
   - Set `status: done`

   **In the session note:**
   - If a `## See also` section exists, add `- [[YYYYMMDD-prep-participant-slug]]` to it
   - Otherwise, append a new section at the end:
     ```markdown
     ## Prep Note
     - [[YYYYMMDD-prep-participant-slug]]
     ```
   - Never create duplicate links — check if the link already exists before adding

6. **Report** in the processing output which prep notes were linked, skipped, or not found.

### Rules

- Derive `MEETINGS_DIR` from the session note path, not from hardcoded values
- If the meeting-prep `config.yaml` is available, read `prep_notes.prefix` (default: `prep`) and `prep_notes.type_tag` (default: `meeting-prep`)
- This step is non-blocking: if it fails or finds no prep notes, processing still succeeds