minutes-mirror

Solid

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".

AI & Automation 1,239 stars 131 forks Updated 2 days ago MIT

Install

View on GitHub

Quality Score: 93/100

Stars 20%
100
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

## Skill Path Before running helper scripts or opening bundled references, set: ```bash export MINUTES_SKILLS_ROOT="$(git rev-parse --show-toplevel)/.agents/skills/minutes" export MINUTES_SKILL_ROOT="$MINUTES_SKILLS_ROOT/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 ...

Details

Author
silverstein
Repository
silverstein/minutes
Created
2 months ago
Last Updated
2 days ago
Language
Rust
License
MIT

Similar Skills

Semantically similar based on skill content — not just same category

AI & Automation Solid

minutes-tag

Lightweight outcome tagging for meetings — won, lost, stalled, great, or noise. Use whenever the user says "tag this meeting", "mark that as a win", "that one was a loss", "tag yesterday's call as stalled", "mark this great", "that meeting was noise", "label that meeting", or any time they describe a meeting outcome in passing. Tagging takes 5 seconds and unlocks /minutes-mirror correlation analysis — the more meetings get tagged, the smarter mirror gets at telling the user what behavior patterns lead to wins. Surface this skill any time the user mentions a meeting result, win, loss, or wasted time.

1,239 Updated 2 days ago
silverstein
AI & Automation Solid

meeting-insights-analyzer

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

2,987 Updated 4 days ago
davepoon
AI & Automation Listed

meeting-insights-analyzer

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

62,564 Updated 1 weeks ago
ComposioHQ
AI & Automation Listed

meeting-insights-analyzer

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

1,277 Updated 1 months ago
Prat011
AI & Automation Listed

meeting-insights-analyzer

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

2 Updated today
zartin790