Extract Action Items from Text on Mac

Extracting action items from unstructured text is one of the most practical AI capabilities for anyone who attends meetings, manages projects, or processes email. The Action Items command transforms messy notes into structured, actionable task lists.

For meeting notes, capture as much raw text as possible during the meeting and extract action items afterward. Do not worry about formatting or organization while taking notes. Just get the key points, decisions, and commitments down. Then select the entire block of notes and run the Action Items command. The AI will identify tasks, assign owners based on names mentioned, and extract any deadlines or timeframes discussed.

Customize the Action Items command for your project management workflow. If your team uses a specific task format (like Jira ticket descriptions or Linear issue templates), add formatting instructions to the command prompt. You can instruct the AI to output tasks in a format that can be directly pasted into your project management tool, complete with priority levels, categories, and owner assignments.

Use Action Items on email threads to cut through lengthy discussions. Select the entire email thread, press your shortcut, and get a clean list of what needs to happen next. This is especially powerful for long threads with multiple participants where action items are buried in conversation. The AI identifies commitments, requests, and implicit tasks that you might miss when skimming.

Combine Action Items with summarization for a complete meeting processing workflow. First summarize the meeting notes to capture the key discussion points and decisions, then run Action Items on the original notes to extract tasks. This gives you both a summary for reference and a task list for execution.

Choose Anthropic Claude or OpenAI for action item extraction as they excel at understanding context and identifying implicit tasks. Google Gemini handles long meeting transcripts well thanks to its large context window. For confidential meeting content, use Ollama with a local model to keep sensitive information private.