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Minutes Tag

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silverstein
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.

Overview

Publishersilverstein
Repositoryminutes
Skill nameminutes-tag
Stars
1.5K
Forks
163
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by silverstein on GitHub. Read the source before you install it.

Installation

Install the Minutes Tag AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/silverstein/minutes.git /tmp/minutes
mkdir -p .claude/skills
cp -r /tmp/minutes/tooling/skills/goldens/claude/minutes-tag .claude/skills/minutes-tag
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Minutes Tag in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Minutes Tag on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Minutes Tag is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/minutes-tag

Lightweight outcome tagging — adds an outcome: field to a meeting's frontmatter so /minutes-mirror can correlate the user's behavior with their results over time.

The whole point of this skill is speed. Tagging should take 5 seconds, not 5 questions. Don't be precious about it — most users will never adopt tagging if it feels like data entry.

How it works

Phase 1: Identify the meeting

Three patterns the user might use. Always filter to meetings, not voice memos — voice memos can't be "won" or "lost".

1. Most recent ("tag this meeting", "mark that as a win", "tag the call I just finished"):

bash
minutes list --content-type meeting --limit 1

Use the most recent. Don't ask which one — that defeats the speed promise. The default behavior should always be "the call you just had".

2. By date ("tag yesterday's call", "tag the Tuesday call"):

bash
minutes list --content-type meeting --limit 10

Pick the meeting matching the date. If multiple meetings match the same day, ask once: "You had meetings . Which one?" with options listing titles.

3. By name ("tag my call with Sarah as a win"):

bash
minutes search "<name>" --content-type meeting --limit 5

Pick the most recent. If ambiguous, ask once.

Phase 2: Identify the tag

If the user already named the outcome in their message ("tag that as a win"), use it directly. Don't ask again — they already told you.

If they haven't, ask via AskUserQuestion with these standard options:

  • won — got the outcome you wanted (deal closed, decision made, agreement reached)
  • lost — didn't get what you wanted (deal lost, idea rejected, no decision)
  • stalled — neither — went sideways, no clear outcome, needs another meeting
  • great — high-quality conversation regardless of outcome (insight, real connection, energy, learned something)
  • noise — should have been an email; no value; time wasted
  • (custom) — let the user provide their own tag

Standard tags are the only ones /minutes-mirror will correlate. Custom tags are stored faithfully but won't appear in correlation analysis — warn the user gently if they pick a custom one: "Custom tags are saved, but mirror only correlates the standard five."

Phase 3: Capture a note only if the user gave one in their message

Do not ask an interactive note question. That's a second prompt and it breaks the speed promise.

Parse the user's original message for a "why" or note. Common patterns:

  • "tag as won, note: Sarah committed to monthly billing" → note = "Sarah committed to monthly billing"
  • "tag won — got the verbal commit on pricing" → note = "got the verbal commit on pricing"
  • "tag stalled because Alex postponed the decision" → note = "Alex postponed the decision"

If you find a note in the message, use it. If you don't, leave outcome_note out of the frontmatter entirely. Don't insert an empty field. Don't ask. Users who want a fuller record have /minutes-debrief for that.

Phase 4: Edit the frontmatter via the bundled helper script

Use the script — do not Edit the frontmatter manually. YAML frontmatter is fragile, and the script handles all the edge cases (no existing frontmatter, existing outcome that needs replacement, atomic write to prevent half-edits, preservation of all other fields).

bash
python3 "${CLAUDE_PLUGIN_ROOT}/skills/minutes-tag/scripts/tag_apply.py" \
  "<absolute-path-to-meeting-file>" \
  --outcome <won|lost|stalled|great|noise|custom> \
  [--note "the optional one-line note from Phase 3"]

Pass --note only if Phase 3 found a note in the user's message. Skip the flag entirely otherwise — the script will omit outcome_note from the frontmatter rather than inserting an empty field.

What the script guarantees:

  • The new fields (outcome, outcome_note if a note was passed, tagged_at) are inserted just before the closing --- of the frontmatter, after every other existing field.
  • All other frontmatter fields are preserved byte-for-byte — no reordering, no reformatting, no whitespace changes.
  • Re-tagging is fully idempotent: if outcome: already exists, the script removes the old outcome lines and re-inserts fresh ones at the end. Old outcome_note: is dropped if no new note is passed.
  • The body of the meeting file is never touched — only the frontmatter block.
  • Writes are atomic (temp file + rename) so an interrupted run can never leave a half-written meeting.

The script prints {"status": "ok", ...} to stdout on success, or {"error": "..."} to stderr with non-zero exit on failure. Surface any error to the user.

Fallback if Python isn't available (extremely rare on macOS): use the Edit tool with surgical precision. Find the closing --- of the frontmatter, anchor on a small unique block ending in it, and insert your new fields right before. This is brittle on unusual frontmatter — only do it if the script fails.

Phase 5: Confirm and nudge

Confirm in one line: "Tagged as ."

Then verify the file is still parseable by Minutes after the edit. The slug is the filename minus .md (e.g., 2026-03-18-product-roadmap-with-case):

bash
minutes get "<filename-without-.md>" 2>&1 | head -3

If the output contains an error or warning about malformed frontmatter, surface it gently: "Note: this meeting's frontmatter has a pre-existing schema issue. The tag was saved, but /minutes-mirror may skip this meeting until it's fixed." Don't try to fix the unrelated schema issue — that's not tag's job.

One-time lifetime nudge (idempotent — never repeats):

bash
ls ~/.minutes/tag-nudge-shown 2>/dev/null

If that marker file doesn't exist, this is the first time tag has run on this machine. Show the nudge once, then create the marker:

"First tag — nice. When you've tagged ~10 meetings, run /minutes-mirror trends and I'll show you what your winning meetings have in common."

bash
mkdir -p ~/.minutes && touch ~/.minutes/tag-nudge-shown

The marker file is the state. No counting, no edge cases, no risk of repeated nudges from re-tagging the same meeting.

Gotchas

  • Speed is the entire feature. If tagging takes more than two questions (the tag, optionally the note), you've broken it. Default to "most recent". Skip the optional note unless the user clearly wants to add one.
  • Standard tags only correlate. Mirror's correlation analysis only works on the five standard tags: won, lost, stalled, great, noise. Custom tags are saved but won't be analyzed. Warn the user once if they pick a custom tag — don't lecture them, just let them know.
  • Don't touch the meeting body. Only edit the YAML frontmatter block between the first two --- markers. Use Edit with surgical precision.
  • Re-tagging is intentional. If the user tags a meeting that's already tagged, overwrite it cleanly. They're either correcting themselves or seeing it differently after the fact. Both are valid.
  • Preserve existing frontmatter exactly. Some meetings have action_items, decisions, intents, entities, people, calendar_event, captured_at, device, recorded_by, etc. Don't reformat or reorder anything — only insert/update the three outcome fields.
  • Tag freshness matters. Tags are most valuable within ~24 hours, while the outcome is fresh in the user's head. Tagging two weeks later is fine but worth less. Don't enforce this — just don't make tagging feel like a chore that the user puts off.
  • Don't try to infer the tag from the transcript. If the user says "tag this meeting" without saying which outcome, ask. Don't guess from the transcript — your guess will be wrong in the cases that matter most (a meeting that looks like a win on paper but actually wasn't, or vice versa).
  • The note is optional for a reason. Most users will skip it. That's fine — the tag itself is the load-bearing data. Don't make the user feel like they're underperforming if they skip the note.

Frequently asked questions

What does the Minutes Tag AI skill do?

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 was...

Why use Minutes Tag on TypingMind?

Because you install it once and use it with any model. Minutes Tag is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Minutes Tag in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/silverstein/minutes/tree/main/tooling/skills/goldens/claude/minutes-tag. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Minutes Tag?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Minutes Tag?

As many as you like. As long as a model supports skills, you can use Minutes Tag with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Minutes Tag AI skill free?

Yes. It is published on GitHub by silverstein under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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