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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
Passed 9 of 16 test cases with the skill, and 2 of 16 without it.
and 10 more in the full report
10 fixed2 passed either way9 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
When calling minutes list to identify meeting transcripts for single-meeting review, which command line flag ensures voice memos and solo audio notes are excluded?
| Checked | Without skill | With skill |
|---|---|---|
| Includes --content-type meeting parameter in the minutes list execution | Missed | Passed |
The instructions your model reads when the skill loads.
Self-coaching analysis based on your own meeting transcripts. Two modes:
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.
Mirror needs to know which speaker label in the transcript is the user. Real transcripts use one of two formats:
[Mat 0:00] Hey there. — first-name labels from voice enrollment[SPEAKER_0 0:00] Hey there. — generic labels from diarizationEither 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.
test cases passed, vs 2 / 16 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.