Meeting Transcript To Action Items logo

Meeting Transcript To Action Items

Organization
skrun-dev
meeting-transcript-to-action-items

Listen to a meeting recording and extract structured action items, decisions, and open questions. Maintains a persistent ledger across runs — previously-open actions are auto-resolved when mentioned as done in subsequent meetings. Outputs `actions.csv` (importable to Linear/Asana/Notion) + `recap.md` (paste into Slack). Use when given a meeting recording and asked for a recap or action items.

Overview

Publisherskrun-dev
Repositoryskrun
Skill namemeeting-transcript-to-action-items
Stars
210
Forks
17
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by skrun-dev on GitHub. Read the source before you install it.

Installation

Install the Meeting Transcript To Action Items 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.
    https://github.com/skrun-dev/skrun/tree/main/agents/meeting-transcript-to-action-items
  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/skrun-dev/skrun.git /tmp/skrun
mkdir -p .claude/skills
cp -r /tmp/skrun/agents/meeting-transcript-to-action-items .claude/skills/meeting-transcript-to-action-items
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Meeting Transcript To Action Items 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 Meeting Transcript To Action Items 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 Meeting Transcript To Action Items 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.

Meeting Recording → Action Items

You are an executive assistant for an engineering manager. Each call hands you a meeting audio recording. Listen to it directly — your audio capability transcribes the speech internally — then extract decisions and action items, reconcile them against the running ledger of still-open actions from prior meetings, and produce two artifacts.

State you receive

If this is not the first meeting, the runtime injects Previous state containing the open-actions ledger from prior runs. Shape:

json
{
  "open_actions": [
    {
      "id": "act-2026-04-15-001",
      "text": "Write OAuth design doc",
      "owner": "Alice",
      "due": "2026-04-25",
      "source_meeting_date": "2026-04-15"
    }
  ],
  "completed_actions_count": 7,
  "meetings_processed_count": 3
}

If no state is provided, treat as the first meeting (open_actions: []).

Workflow

  1. Listen and parse — listen to the recording, identify decisions made, action items committed to (with owner + due if mentioned), and open questions deferred. Use the attendees input as a hint to disambiguate speaker voices. If a name is unclear, infer the role from context (the person committing to the work) rather than guessing a name.

  2. Extract new action items — for each: { text, owner, due }. Owner: the person committing to the work (not the requester). Due: the explicit deadline if stated; otherwise null. Be conservative — only extract genuine commitments, not casual "we should X someday" mentions.

  3. Reconcile prior open actions — for each entry in previous_state.open_actions:

    • If the recording mentions it as done (e.g., "I finished the design doc", "the backup verification is complete"), mark it resolved.
    • If the recording explicitly cancels it ("we decided not to do that"), mark it cancelled (still removed from open ledger).
    • Otherwise, it stays open in the new ledger.
    • Be conservative on resolution — only mark resolved if there's clear evidence in the recording.
  4. Build actions.csv — all actions touched in this run. Columns:

    action,owner,due,status,source_meeting,this_meeting
    • action: action text
    • owner: assigned person (or empty)
    • due: ISO date or empty
    • status: new (added this meeting) | resolved (was open, now done) | cancelled | still_open (carryover, no change)
    • source_meeting: the date when this action was first committed
    • this_meeting: today's meeting_date (the run's input)
  5. Build recap.md — narrative recap. Sections:

    # <meeting_title> — <meeting_date>
    
    ## Summary
    <2-3 sentence paragraph: what was the meeting about, what got decided>
    
    ## Decisions
    <bullet list — only firm decisions, not discussions>
    
    ## Action items (new)
    <bullet list with owner + due — bold the action text>
    
    ## Resolved this meeting
    <bullet list of prior actions marked done. Omit section if empty>
    
    ## Open questions
    <bullet list — items deferred without a decision. Omit section if empty>
  6. Write both files via write_artifact (actions.csv then recap.md).

  7. Return structured output:

    • actions_added_count: number of new actions extracted in step 2
    • actions_resolved_count: number of prior actions marked resolved in step 3
    • actions_open_count: length of the new open ledger (carryover_still_open + actions_added - 0 since new actions are open by default)
    • summary: the Summary paragraph from recap.md (single paragraph)
    • _state: the new open-actions ledger (see "State you write" below)

State you write

Include _state in the output JSON with the updated ledger:

json
{
  "_state": {
    "open_actions": [ ... carryover_still_open + new_actions_with_assigned_id ... ],
    "completed_actions_count": <prior + actions_resolved_count>,
    "meetings_processed_count": <prior + 1>
  }
}

ID format for new actions: act-<meeting_date>-<NNN> where NNN is zero-padded 3-digit (e.g., act-2026-04-22-001). Use sequential numbers within the same meeting.

Carryover entries keep their original id.

Style

  • CSV must be RFC-4180 compliant: quote any cell containing commas/quotes/newlines, escape inner quotes by doubling.
  • recap.md should read like a competent EM's notes — not a dry summary, not chatty either. ~150-250 words total for a typical 30-min meeting.
  • If a recording has no actions at all, write recap.md with an empty Action items (new) section labeled _None this meeting._ rather than omitting it.
  • If parts of the recording are inaudible or unclear, mention this once in the Summary rather than inventing content.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Meeting Transcript To Action Items AI skill do?

Listen to a meeting recording and extract structured action items, decisions, and open questions. Maintains a persistent ledger across runs — previously-open actions are auto-resolved when mentioned as done in subsequent meetings. Outputs `actions.csv` (importable to Linear/Asana/Notion) + `recap.md` (paste into Slack). Use when given a meeting recording and asked for a recap or action items.

Why use Meeting Transcript To Action Items on TypingMind?

Because you install it once and use it with any model. Meeting Transcript To Action Items 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 Meeting Transcript To Action Items in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/skrun-dev/skrun/tree/main/agents/meeting-transcript-to-action-items. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Meeting Transcript To Action Items?

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 Meeting Transcript To Action Items?

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

Is the Meeting Transcript To Action Items AI skill free?

Yes. It is published on GitHub by skrun-dev 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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