Action Items Todo logo

Action Items Todo

Organization
microsoft
action-items-todo

Use this skill whenever the user asks to monitor, capture, or track action items directed at them across Microsoft Teams chats, meeting transcripts, and Outlook mail, and record them as Microsoft To Do tasks — e.g. "set up action item tracking", "monitor my action items", "capture asks into To Do", "scan for action items now", "what did people ask me to do". Run the SETUP wizard first when no config exists; it asks for the To Do list, scan frequency, and schedule, then creates the recurring automation. Also handles the follow-up commands "scan now", "status", "pause", "resume", and "reconfigure".

Overview

Publishermicrosoft
Repositorycat-agent-skills
Skill nameaction-items-todo
Stars
95
Forks
115
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Action Items Todo 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/microsoft/cat-agent-skills/tree/main/submissions/action-items-todo
  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/microsoft/cat-agent-skills.git /tmp/cat-agent-skills
mkdir -p .claude/skills
cp -r /tmp/cat-agent-skills/submissions/action-items-todo .claude/skills/action-items-todo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Action Items Todo 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 Action Items Todo 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 Action Items Todo 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.

:: ACTION ITEMS TO MICROSOFT TO DO :: Monitors Teams chats, meeting transcripts, and Outlook mail for action items EXPLICITLY directed at the user, and captures each one as a Microsoft To Do task with a due date, importance, and owner tagging. Microsoft To Do is the only task store this skill writes to.

Nothing about the user (name, email, list name, schedule, customer and workstream names) is hardcoded. Everything lives in the config file written by SETUP.

== FILES == Both live in the user's home directory, so write them with the platform's own separator and never hardcode a Windows path:

  • CONFIG: /.scout/action-items-todo/config.json
  • STATE (dedupe + last scan): /.scout/action-items-todo/state.json Resolve at runtime (Node os.homedir(), $HOME, or %USERPROFILE% on Windows). Create the directory if missing. Write both files as UTF-8 JSON, atomically (temp file then rename). Always preserve unknown fields.

== CONFIG SCHEMA == { "version": 1, "identity": { "displayName": str, "mail": str, "upn": str, "aadId": str }, "listName": str, // the Microsoft To Do list tasks are written to "frequency": str, // natural-language interval, e.g. "every 30 minutes" "schedule": "24/7" | "weekdays-allhours" | "workhours" | str, "timeZone": str, // IANA zone name, e.g. "Europe/Berlin" "language": "en", // task titles are always written in this language "owners": { "customers": [str], "workstreams": [str] }, // optional, may be empty "excludedChats": [str], // chat/channel names to always ignore "notifyTeams": bool, // send a Teams self-message when items are captured "automationId": str|null, "setupCompleted": ISO8601|null }

== STATE SCHEMA == { "last_scan": ISO8601, "seen": [ { "key": str, "title": str, "created": ISO8601, "source": "teams"|"transcript"|"email", "url": str|null } ] } "key" is a stable hash of source + thread/message id. Keep the last 500 entries; drop older ones.

== SETUP (first run, or on demand) == Run SETUP automatically whenever CONFIG is missing or setupCompleted is null. Never run a scan before SETUP completes.

  1. Call workiq_get_my_profile and fill identity. If it fails, say so and stop; the skill cannot target asks without knowing who the user is.
  2. Read the machine time zone as an IANA name with a cross-platform runtime API: Intl.DateTimeFormat().resolvedOptions().timeZone, e.g. node -p "Intl.DateTimeFormat().resolvedOptions().timeZone". Do NOT use PowerShell Get-TimeZone: on Windows it returns a Windows zone ID such as "W. Europe Standard Time", which is not an IANA name and will not match the format the rest of the skill expects. If the lookup fails, ask the user for their IANA zone rather than guessing.
  3. Read the user's existing lists with workiq_list_task_lists, then ask QUESTION 1 with m_ask_user, free-text mode, inputHint "list name, e.g. Work": "Which Microsoft To Do list should captured action items go into?" Show the existing list names in the assistant message BEFORE the call so the user can pick one or name a new one. After the reply: if the list does not exist, confirm creation, then workiq_create_task_list.
  4. Ask QUESTION 2 with m_ask_user, multiple choice, recommended index 1: "How often should I scan for new action items?" Answers: "Every 15 minutes" / "Every 30 minutes" / "Every hour" / "Every 2 hours". Store the matching natural-language interval in frequency.
  5. Ask QUESTION 3 with m_ask_user, multiple choice, recommended index 0: "When should the scan run?" Answers:
    • "Around the clock, every day" (desc: best when you work across time zones) -> schedule "24/7"
    • "Around the clock, weekdays only" -> "weekdays-allhours"
    • "Working hours only, weekdays" (desc: 8am-6pm in your local time zone) -> "workhours"
    • "Let me describe it" -> free-text follow-up, stored verbatim.
  6. Ask nothing else. Default the remaining config: language "en", owners empty, excludedChats empty, notifyTeams true.
  7. Write CONFIG and an empty STATE with last_scan = now minus one frequency interval. Do NOT seed last_scan further back to "catch up". The first run would then backfill items that were already handled or already completed elsewhere, and create tasks for them. A short first window is correct.
  8. Create the automation with m_create_automation, then store its id in automationId:
    • name: "Action Items to To Do" // avoid > and < in the name; they get HTML-escaped in the automation list
    • schedule: derived from frequency + schedule (see SCHEDULE MAPPING)
    • teamsNotify: "auto"
    • prompt: "Load the action-items-todo skill with m_get_skill and run the SCAN procedure exactly as written. Output only the strict result format the skill defines."
  9. Confirm back in one short block: list name, frequency, schedule, automation id, and how to change any of them ("run /action-items-todo setup again").

== SCHEDULE MAPPING == The scheduler parses a fixed set of forms. A minute interval CANNOT be combined with an hour window in natural language, so the working-hours case must use cron.

  • "24/7" -> "" e.g. "every 30 minutes"
  • "weekdays-allhours" -> " on weekdays" e.g. "every 30 minutes on weekdays"
  • "workhours" -> cron, built from the interval: every 15 minutes -> "cron: 0,15,30,45 8-17 * * 1-5" every 30 minutes -> "cron: 0,30 8-17 * * 1-5" every hour -> "cron: 0 8-17 * * 1-5" every 2 hours -> "cron: 0 8,10,12,14,16 * * 1-5"
  • custom -> pass the user's phrasing through; if m_create_automation rejects it, show the supported_forms list it returns and ask the user to pick one. All schedules run in machine-local time. Verify the automation was actually created: m_create_automation returns success:false with a supported_forms list when it cannot parse the schedule, and SETUP must not report success in that case.

== SCAN (what the automation runs) == Load CONFIG first. If it is missing, output AUTOMATION_ERROR|setup not completed and stop. Scan window: from state.last_scan to now. If last_scan is missing or older than 24h, scan the trailing 24h.

Sources, all three every run: (a) TEAMS CHATS — workiq_list_chats, then workiq_list_chat_messages on candidates. (b) MEETING TRANSCRIPTS — workiq_list_meetings for meetings that ended inside the window, then workiq_list_meeting_transcripts and workiq_get_meeting_transcript. Transcripts for a recurring series come back out of order: filter transcripts[] by createdDateTime to the target meeting date, never take transcripts[0]. (c) OUTLOOK MAIL — workiq_list_emails on the Inbox for the window.

=== TEAMS RECENCY RULE (apply before targeting) === Do NOT use a chat's lastUpdatedDateTime or the default chat-list order to judge recency. Graph frequently fails to advance lastUpdatedDateTime on 1:1 chats, so a chat with a brand-new message can look a month stale.

  • Rank and window chats by lastMessagePreview.date, which is authoritative.
  • Always open any 1:1 chat where lastMessagePreview.date is later than viewpoint.lastMessageReadDateTime, or falls inside the scan window.
  • Read the whole recent thread, not just the message naming the user. The ask often sits in a follow-up that does not repeat the name.

=== TARGETING RULE === An action item is anything asking the user to do, decide, respond, review, attend, prepare, or follow up. Soft and future-dated asks count ("when you have time, could you look at this?"). Urgency is NOT required. Capture ONLY when the ask is specifically for the user:

  • The activity names the user (display name, first name, or mail from identity) or uses a proper Teams @mention of them, OR the surrounding context clearly assigns the action to them.
  • A 1:1 Teams chat always satisfies the context test: any genuine ask there is directed at the user even without their name.
  • In a meeting transcript, capture only when someone assigns the item to the user by name, or the user is the clear owner from the immediately surrounding exchange. Do not capture the user's own statements about what they will do; this skill tracks asks TO the user.
  • Do NOT capture generic group asks, broad team requests, messages addressed to "Everyone"/"Tutti"/a channel/a whole group, status updates, FYIs, newsletters, or notifications.
  • Do NOT infer ownership from topic area, past participation, or the fact the user could help. There must be a specific ask.
  • Skip any chat or channel named in config.excludedChats.

=== LANGUAGE RULE === Task titles and bodies are ALWAYS written in config.language (default English), regardless of the source language. Translate the gist into a clear, concise imperative title. Never copy foreign-language phrasing verbatim.

=== URGENCY -> IMPORTANCE ===

  • high: incident, blocker, escalation, customer-impacting issue, explicit urgent/ASAP language, or a deadline inside the next two days.
  • normal: a standard direct request with no specific deadline. This is the default.
  • low: a soft, tentative, optional, or clearly far-future ask ("eventually", "no rush", "when you get a chance").

=== DUE DATE ===

  • Explicit or clearly implied deadline ("by Friday", "before the review") -> that date, YYYY-MM-DD.
  • Otherwise -> today's date in config.timeZone. Always set a due date; never omit it.

=== OWNER TAGGING === To Do has no custom fields, so ownership goes in the task body as a metadata block above the summary, at most ONE line: Customer: or Workstream: Rules:

  • If config.owners lists customers or workstreams, match against those exact names only, including obvious variants the thread supports.
  • If config.owners is empty, you may infer a customer or project name that is stated plainly in the thread. Only use a name that actually appears in the source.
  • Topic overlap is not ownership. Never invent a name.
  • If a task touches both a customer and a workstream, prefer Customer.
  • When the evidence is thin, leave the block off. An untagged task is easy to fix; a wrongly tagged one hides.

=== TASK BODY FORMAT === [optional owner line] <one-sentence summary of the ask, in config.language> Source: <deep link to the Teams message / meeting / email, when available>

=== WRITE PROCEDURE, per captured item ===

  1. Dedupe twice: check state.seen for the item key, and call workiq_list_tasks on config.listName for an open task with the same title. Skip if either matches.
  2. workiq_create_task with list = config.listName, the English title, due, importance, and body.
  3. If the list is missing, recreate it with workiq_create_task_list under the exact configured name and retry once.
  4. Append the item to state.seen.
  5. After all items, set state.last_scan = now and write STATE atomically. Write STATE even on a run with no items, so the window advances.

=== NOTIFICATION === If config.notifyTeams is true AND at least one task was created this run, send ONE Teams self-message listing each captured item: source, importance, owner (or "unassigned"), summary, link. Send nothing on a run that captured nothing.

=== OUTPUT FORMAT, STRICT === The final output is exactly one of these, with no preamble, narration, or progress commentary: (A) one line per captured item: ACTION_ITEM|||<owner or ->|| (B) the single token: SILENT_NO_ACTION_ITEMS (C) on a tool or permission failure: AUTOMATION_ERROR|

Presentation rule for the assistant rendering the result: if the output is SILENT_NO_ACTION_ITEMS, or contains no ACTION_ITEM and no AUTOMATION_ERROR line, render a completely empty response. No heading, no acknowledgement, no "nothing found" message.

== ON-DEMAND COMMANDS ==

  • "setup" / "reconfigure" -> re-run SETUP, keeping existing answers as the recommended defaults, then update the automation with m_update_automation instead of creating a duplicate.
  • "scan now" -> run SCAN once, immediately, and report the items in a readable list rather than the strict format.
  • "status" -> print the current config, last scan time, and the number of items captured in the last 7 days.
  • "pause" / "resume" -> m_update_automation with enabled false/true.

== PRIVACY == Everything this skill writes stays in the user's own Microsoft To Do and their own Teams self-chat. It never sends a message to a third party, never forwards source content, and never posts to a group chat or channel.

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 Action Items Todo AI skill do?

Use this skill whenever the user asks to monitor, capture, or track action items directed at them across Microsoft Teams chats, meeting transcripts, and Outlook mail, and record them as Microsoft To Do tasks — e.g. "set up action item tracking", "monitor my action items", "capture asks into To Do", "scan for action items now", "what did people ask me to do". Run the SETUP wizard first when no config exists; it asks for the To Do list, scan frequency, and schedule, then creates the recurring automation. Also handles the follow-up commands "scan now", "status", "pause", "resume", and "reconfi...

Why use Action Items Todo on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/cat-agent-skills/tree/main/submissions/action-items-todo. 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 Action Items Todo?

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 Action Items Todo?

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

Is the Action Items Todo AI skill free?

Yes. It is published on GitHub by microsoft 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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