minutes-mirror

# /minutes-mirror

Self-coaching analysis based on your own meeting transcripts. Two modes:

- **Single-meeting mode** — review a specific meeting and surface what you did, what was unusual for you, and one concrete thing to try next time.
- **Pattern mode** — surface trends across the last 30 days, including (if meetings are tagged) what behaviors correlate with winning vs losing.

The point is not to roast you. The point is to give you a kind, evidence-based mirror to behaviors that are usually invisible to you because you're inside them.

## How it works

### Phase 0: Identify "you"

Mirror needs to know which speaker label in the transcript is the user. Real transcripts use one of two formats:

- **Enrolled users**: `[Mat 0:00] Hey there.` — first-name labels from voice enrollment
- **Non-enrolled users**: `[SPEAKER_0 0:00] Hey there.` — generic labels from diarization

Either way, mirror needs to know which label maps to the user. Check sources in order:

**1. Enrolled voice profile:**
```bash
minutes voices --json 2>/dev/null
```

Returns a JSON array of enrolled profiles. The user's profile is the one with `source: "self-enrollment"` (or the first one if there's only one). Use the `name` field as the speaker label to look for in transcripts. Example response:

```json
[{"person_slug": "mat", "name": "Mat", "source": "self-enrollment", ...}]
```

→ Speaker label is `Mat`.

**2. Cached self name(s):**
```bash
cat ~/.minutes/config/self.txt 2>/dev/null
```

The cache may contain **multiple labels**, one per line (e.g., `Mat`, `Mat S.`, `MAT_SILVERSTEIN`) — match any of them. People often appear under multiple labels across transcripts.

**3. Ask once and cache:**
If neither source returns a name, ask via AskUserQuestion: "Which speaker label in your transcripts is you? You can give multiple if you appear under different names (e.g., 'Mat, Mat S., MAT_SILVERSTEIN')."

Cache the answer (comma-separated input → one label per line):
```bash
mkdir -p ~/.minutes/config
printf '%s\n' <label1> <label2> ... > ~/.minutes/config/self.txt
```

This is a one-time setup cost. Don't ask again on future runs. If the user later mentions they have a new label, they can re-edit the file or re-run with `mirror reset-self`.

### Phase 1: Pick a mode

**Single-meeting mode** triggers on: "review my last meeting", "how did I do", "mirror that call", "feedback on the Sarah call".

**Pattern mode** triggers on: "show my patterns", "trends", "across all meetings", "coach me", "what do my winning meetings look like".

If ambiguous, default to **single-meeting mode on the most recent meeting** — it's fast, useful, and obviously what most people mean.

### Phase 2a: Single-meeting analysis

Find the target meeting (filter to meetings, not voice memos — talk-time analysis on a solo memo is meaningless):
```bash
minutes list --content-type meeting --limit 5
```
If the user named a specific meeting, use `minutes get <filename-without-.md>`. Otherwise pick the most recent.

**Compute the metrics with the bundled helper script**, not by counting in-context. LLMs are bad at exact token counting; the script does it deterministically with regex and basic string ops.

```bash
python3 "${CLAUDE_PLUGIN_ROOT}/skills/minutes-mirror/scripts/mirror_metrics.py" \
  "<path-to-meeting-file>" \
  --self "$(cat ~/.minutes/config/self.txt 2>/dev/null | paste -sd, -)"
```

The `--self` flag takes a comma-separated list of speaker labels (e.g., `Mat,Mat S.,SPEAKER_3`). Use the labels you cached in Phase 0.

The script outputs JSON to stdout with these fields:

| Field | Meaning |
|---|---|
| `total_words`, `self_words`, `other_words` | Word counts (split-on-whitespace) |
| `talk_ratio` | `self_words / total_words` as a 0–1 float |
| `self_turn_count`, `other_turn_count` | Number of speaker turns |
| `speakers` | All distinct speaker labels seen in the transcript |
| `filler_count`, `filler_per_100_words` | Filler-word hits in self speech (`um`, `uh`, `like`, `you know`, `basically`, `literally`, `kinda`, `right?`) |
| `hedging_count`, `hedging_per_100_words` | Hedging hits in self speech (`maybe`, `kind of`, `sort of`, `i think`, `i guess`, `possibly`, `somewhat`, `a little`, `perhaps`, `sorry to`). The word `just` is intentionally excluded — too many false positives. |
| `question_count`, `questions_per_5min` | Self questions (`?` count) |
| `duration_minutes` | From last timestamp if present, else word-count estimate at 150 wpm |
| `longest_monologue` | Longest uninterrupted self stretch: word count, seconds estimate, first 8 words, start time |
| `longest_listen` | Same shape, but for the longest stretch where you didn't speak |

The script exits non-zero on errors (file missing, no diarized turns, no self labels matched). On exit code 3 ("no turns matched any self label"), it tells you which speaker labels it found in the transcript — re-run with one of those, or update `~/.minutes/config/self.txt`.

**Compute your baseline by running the script on the last ~10 meetings.** Use Glob to enumerate them:

```
Glob: <output_dir>/*.md   (where <output_dir> comes from `minutes paths`)
```

Take the 10 most recent (filename starts with `YYYY-MM-DD-`), run `mirror_metrics.py` on each, average the metrics. If you have fewer than 5 prior meetings to average, say so explicitly — "Baseline computed from only N meetings, treat with caution" — instead of pretending the comparison is meaningful.

Once you have current-meeting metrics + baseline, flag anything >25% off baseline as worth noting.

**Output format:**

```markdown
## Mirror: <meeting title> · <date>

**Talk time**: You spoke <X>% of the time. (Your 30-day average: <Y>%.) <flag if abnormal>
**Longest monologue**: ~<N> seconds on "<topic>". <one-line judgment: was it earned (you were asked to explain something complex) or was it dominance?>
**Longest you listened**: ~<N> seconds during "<topic>". <one-line: what did they reveal?>
**Filler words**: <N> per 100 words. (Average: <Y>.)
**H