mcpbeat

Meeting Transcript To Action Items

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.

127k tokens
context cost
the whole folder, loaded on every use
5
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
208
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/skrun-dev/skrun --skill meeting-transcript-to-action-items

The instruction itself

5 sections, as written by the author

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:

{
  "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

  • 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.
  • 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.
  • 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.
  • 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)
  • 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>
  • Write both files via write_artifact (actions.csv then recap.md).
  • 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:

{
  "_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.

How to use it

Copy the folder

Take skrun-dev/meeting-transcript-to-action-items from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.