minutes-mirror

name	minutes-mirror
description	Self-coaching analysis of your own behavior across meetings — talk-time ratio, filler words, hedging language, monologue length, energy patterns, and (when meetings are tagged via /minutes-tag) what your behavior in winning meetings looks like vs losing ones. Use this whenever the user says "how did I do", "review my last meeting", "mirror", "self-review", "show my patterns", "coach me", "where am I weak", "talk time", "am I improving", "what do I do in meetings I win", "feedback on me", or asks for any kind of personal feedback on their own meeting behavior. This is the rare skill that gives the user a mirror to their own habits — surface it whenever they show curiosity about their own performance, even if they don't use the word "mirror".
user_invocable	true

/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:

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:

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

→ Speaker label is Mat.

2. Cached self name(s):

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):

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):

minutes list --content-type meeting --limit 5

Require exit status 0. If the user named a specific meeting, use bounded search to identify its exact path; otherwise pick the most recent list result. Paths are hints, not retained capabilities.

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.

set -o pipefail
minutes get "<exact path>" | \
python3 "${CLAUDE_PLUGIN_ROOT}/skills/minutes-mirror/scripts/mirror_metrics.py" \
  - \
  --self "$(cat ~/.minutes/config/self.txt 2>/dev/null | paste -sd, -)"

Require both sides of the pipeline to exit successfully. The helper receives only the exact native-authorized bytes over stdin; never pass it a meeting path.

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
outcome	Supported frontmatter outcome (won, lost, stalled, great, noise), or null

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 from the last ~10 bounded list results. For each path, repeat the native minutes get to stdin pipeline above and require both commands to succeed. Never enumerate or open the meeting directory directly. Average the metrics. If you have fewer than 5 successful meetings, 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:

## 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>.)
:  per 100 words. (Average: .) 
: . 


<2–3 specific moments worth re-reading. Quote a short line from the transcript and say why it matters. Be specific — "You hedged the moment Sarah pushed on price ('I mean, I think we could maybe…')" beats "you hedged sometimes".>

Phase 2b: Pattern mode

Run across the last 30 days (or whatever window the user gives you).

Run minutes list --content-type meeting --limit 50, require exit status 0, and filter its JSON results to the requested window. Retrieve each selected meeting only through the native minutes get to stdin pipeline above.

Compute the same per-meeting metrics across every successfully authorized normal meeting in the window. Then look for patterns:

Behavioral patterns (always available):

• Trend in talk ratio over time (going up = dominating more, going down = listening more)
• Topics that correlate with high talk ratio (where do you steamroll?)
• Topics that correlate with high hedging (where do you lose authority?)
• Filler word rate by time-of-day (fatigue curve?)
• Day-of-week patterns (worse on Mondays?)
• Meeting length patterns (do your >45-min meetings degrade?)

Outcome correlations (only if meetings are tagged via /minutes-tag):

Standard outcome tags that mirror correlates: won, lost, stalled, great, noise. These mirror the set defined by /minutes-tag — if that skill ever adds new standard tags, update mirror to recognize them too. Custom (non-standard) tags are ignored for correlation analysis.

The helper includes a bounded outcome field (won, lost, stalled, great, noise, or null) from the same authorized bytes. If every result is null, skip the outcome-correlation section. Otherwise group only those returned values and compare metrics across groups:

• "In meetings you tagged won, your average talk ratio was 38%. In lost meetings, 67%."
• "In stalled meetings, your hedging rate was 2× your baseline."
• "Every meeting you tagged great had ≥12 questions from you in the first 10 minutes."

Minimum data thresholds:

• Behavioral patterns need ≥5 meetings in the window to be meaningful. Below that, single-meeting mode is more honest.
• Outcome correlations need ≥3 meetings per tag group. Below that, it's noise.
• If thresholds aren't met, surface what you can compute and tell the user explicitly: "Tag more meetings via /minutes-tag and I can show you what wins look like."

Output format:

## Mirror: 30-day patterns

**You've been in <N> meetings.** Here's what I see:

### Talk patterns
<2–3 bullets, specific>

### Where you hedge
<2–3 bullets with specific topics>

### Energy & timing
<observations about time-of-day, fatigue, day-of-week>

### Win/loss correlation
<only if ≥3 tagged meetings per outcome — otherwise skip this section entirely>

### One thing to try this week
<Exactly one. Concrete. Falsifiable.>

Phase 3: Closing ritual

End with two beats:

1. Specific experiment — Restate the "one thing to try" as a concrete test. "Try cutting your hedging in your next 3 meetings. I'll measure it when you ask me to mirror again."

2. Tag nudge (only if no meetings have an outcome: field yet) — "After your next meeting, run /minutes-tag won|lost|stalled so I can correlate behavior with outcomes over time. ~10 tagged meetings is when the patterns get sharp."

Gotchas

• Long-transcript accuracy degrades. LLMs are bad at exact token counting. For transcripts >5000 words, your filler-word and hedging counts are estimates, not measurements. Either say so in the output ("≈14 fillers, sampled from 3 segments") or sample three 1500-word segments (start, middle, end) and extrapolate. Don't pretend you exactly counted 8327 words.
• This is coaching, not roasting. Be specific, evidence-based, and kind. Quote actual lines from the transcript before making any judgment about tone or behavior. Never make claims you can't point to evidence for. The user is looking at themselves here — be the coach you'd want.
• Speaker identification can fail. If transcripts use generic labels like SPEAKER_0/SPEAKER_1 and the user hasn't enrolled their voice, the analysis can't know which speaker is them. Ask once per machine, cache forever in ~/.minutes/config/self.txt.
• Don't fake metrics. If a transcript has no speaker diarization (one big block, no speaker labels), say so and offer pattern mode across other meetings instead. Don't compute talk-time on a transcript without speakers — the number will be wrong and the user will lose trust in everything else.
• Word-count duration estimates are rough. 150 wpm is the convention. Use timestamps when present in the transcript; fall back to word count when not. Always say "≈" or "" so the user knows it's an estimate.
• Avoid corporate language. Don't say "your engagement scores" or "talk-time KPI". Talk like a coach who actually cares: "you spoke 58% of the time" not "talk-time metric: 0.58".
• Pattern mode needs at least 5 meetings. Below that, single-meeting mode is more honest. Don't surface "trends" from 2 data points.
• Outcome correlations need at least 3 per group. Below that, it's noise. Tell the user the threshold and how to reach it.
• Don't pathologize high talk time. Sometimes talking 70% is correct — it's a presentation, you're delivering bad news, you're explaining something complex to a non-expert. Compare to baseline and note context. Don't treat any number as automatically bad.
• The "one thing" must be testable. "Be more confident" is useless. "Cut hedging words from your next 3 close calls" is testable. The user will either do it or not, and the next mirror should be able to verify.
• Never compare across users. Mirror is a mirror to this user, not a benchmark vs anyone else. Don't say "the average sales rep talks 45%". Compare the user only to themselves.
• Hedging matters most around price, scope, and commitment. A general filler-word count is interesting; flagging that the user hedged the moment Sarah pushed on price is useful. Surface where the hedging happened, not just how much.