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Chaitrali Kakde
Chaitrali Kakde

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How I Built an AI Agent with Claude Code That Posts My Daily Standup to Slack at 10 AM

#ai #automation #development #productivity

For the last few months, I was doing the same thing every single day.

First, I would update all my tasks in ClickUp.

Then, before the team standup, I would open Slack and manually write a summary of everything I worked on the previous day.

The funny part was that all the information already existed in ClickUp. I was basically maintaining the same data in two different places.

Some days it took only a few minutes. On busy days, it felt like another task added to my list.

After repeating this process for weeks, I finally decided to fix it.

Instead of manually writing standup updates every morning, I connected ClickUp to Slack and built a simple workflow that automatically generates and posts a summary of yesterday's work at 10 AM.

Now the standup update appears in Slack automatically, and I don't have to think about it anymore.

Since this saved me time every day, I thought I'd share exactly how I built it.

What the Workflow Does

Every day at 10 AM:

  • The workflow fetches tasks that were updated in ClickUp yesterday.
  • It collects the latest task statuses and activity.
  • AI generates a clean and readable standup summary.
  • The summary is automatically posted to a Slack channel.

slack automation

Why I Built This

The biggest problem wasn't writing the update.

The problem was context switching.

I was already keeping ClickUp updated throughout the day. Having to open another tool and rewrite the same information felt unnecessary.

I wanted a system where updating ClickUp automatically became my standup update.

Now it does.

How I Built It

Prerequisites

Step 1: Install Swytchcode CLI 

npm install -g swytchcode
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Log in to your account:

swytchcode login
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Step 2 :Initialize a project 

mkdir standup-bot && cd standup-bot
npm init -y
npm install swytchcode-runtime dotenv
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Initialize Swytchcode in the project: 

swytchcode init
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swytchcode command

Selecting claude means Swytchcode generates an MCP config and a CLAUDE.md contract file in your project — Claude Code can then read it and know exactly which integrations are available and how to call them. Selecting sandbox means all API calls run in test mode so you don't accidentally hit production APIs while building.

swytchcode-command

Step 3: Search and fetch the integrations

Find the ClickUp and Slack integration bundles:

swytchcode search clickup
swytchcode search slack
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Fetch them into your project:

swytchcode get clickup
swytchcode get slack
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Step 4 : Enable the methods you need

You need two methods: one to fetch tasks from ClickUp, one to post a message to Slack.

swytchcode add method list.task.get
swytchcode add method chat.postmessage.chat.postmessage.create
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Verify they're enabled:

swytchcode list tooling
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Step 5 : Inspect the contracts

Before writing any code, check the exact input/output schema for each method:

swytchcode info list.task.get
swytchcode info chat.postmessage.chat.postmessage.create
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This tells you which fields are required, where they go (path, query, body, header), and what the response shape looks like. No guessing.

Step 6 : Set up your .env 

CLICKUP_API_KEY=pk_your_clickup_api_key
CLICKUP_LIST_ID=your_list_id
SLACK_BOT_TOKEN=xoxb-your-slack-bot-token
SLACK_CHANNEL=C0XXXXXXXXX
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  • 1. To find your CLICKUP_LIST_ID: open a list in ClickUp and copy the ID from the URL — it's the number after /l/.
  • 2. To find your SLACK_CHANNEL ID: right-click the channel in Slack → Copy link — the ID is the last segment.

Step 7 : Ask Claude to write the script

This is where Swytchcode + Claude really shines. Because you ran swytchcode init with Claude selected, Claude Code already knows your project's integrations, available methods, and their exact input/output contracts via the CLAUDE.md file Swytchcode generated.

You don't need to write the script yourself. Just open Claude Code in your project and paste this prompt:

I want to build a daily standup automation. Here's what it should do:

1. Fetch all tasks from my ClickUp list using the list.task.get method
2. Filter to tasks updated in the last 48 hours (fall back to all tasks if none match)
3. Group the tasks by their status (done, in progress, blocked, to do, etc.)
4. Build a formatted Slack message with emoji per status group, task names as 
   clickable links, and assignee names
5. Post the message to my Slack standup channel using the Slack API

Use swytchcode-runtime exec() for the ClickUp fetch. Read credentials from .env 
(CLICKUP_LIST_ID, CLICKUP_API_KEY, SLACK_BOT_TOKEN, SLACK_CHANNEL). 
Write the result to standup.js.
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Step 8 — Run it 

node standup.js
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slack-clickup-atomation

slack - clickup - automation

A small automation, but it removes a repetitive task that used to happen every single day.

If you're already using ClickUp and Slack, this is probably one of the easiest productivity automations you can build.

I'm sharing the workflow because I think many teams have the exact same problem and don't realize how easy it is to automate using swytchcode

Resources

Top comments (4)

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harjjotsinghh profile image
Harjot Singh •

Automating standups is a perfect first agent project - the input already exists (your commits, PRs, ticket activity), the output is low-stakes, and you do it every single day, so the leverage is real and the blast radius if it's wrong is "a slightly off Slack message," not a production incident. That low-risk-high-frequency profile is exactly where agents earn their keep. Pulling the summary from your actual git/PR activity rather than asking you to recall your day is the smart move - the agent reports what you did, not what you remember doing.

The one upgrade I'd suggest: keep a quick human glance before it posts, at least early on, because a standup summary that confidently misreports what you worked on erodes teammates' trust faster than just writing it yourself. Propose-then-approve beats fully-autonomous for anything teammates read. That propose-and-gate instinct is the core of how I build Moonshift, the thing I work on - a multi-agent pipeline that takes a prompt to a deployed SaaS, where agents draft and a verify/approve step gates before anything goes out. Same pattern as a standup bot you trust. Multi-model routing keeps a build ~$3 flat, first run free no card. Nice practical build. Does it post automatically, or draft for you to confirm? And is it pulling from commits/PRs or something richer? The source data is what decides whether the summary is actually accurate.

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chaitrali_kakde_27694f6f9 profile image
Chaitrali Kakde •

Hey, yeah, that's totally possible. I actually agree with you on the human-in-the-loop part having it draft first and then giving it a quick review before posting feels like the right balance.

Right now I've only built the basic version. It pulls tasks/activity from GitHub and generates a standup from that. I'm planning to make it richer by pulling things like commits, PRs, reviews, issues, and other signals through GitHub APIs so the updates are more accurate.

One nice thing is that I'm building it with Swytchcode, which gives access to 1000+ integrations out of the box, so connecting tools like Jira, Linear, Slack, Notion, etc. is pretty straightforward.

For now it's more of a draft generator than a fully autonomous poster, but adding an approval step before it posts would be easy and honestly is probably the better experience anyway. The quality of the standup really comes down to how much context you can pull from the source systems.

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chilarai profile image
Chilarai •

This is really awesome

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kenielzep97 profile image
Self-Correcting Systems •

This is a really good example of where AI automation makes sense: repeated task, existing
source data, low blast radius, clear daily value.

The part I’d watch as this grows is source authority.

If the bot is pulling from ClickUp, commits, PRs, Slack threads, etc., those sources may
disagree. A task might be updated in ClickUp but the PR says something different, or a
ticket may be stale while the actual work moved forward elsewhere.

So I’d think about adding a simple source hierarchy:

  1. What source is allowed to govern “done”?
  2. What source is allowed to govern “blocked”?
  3. What source is only context?
  4. When should the bot draft instead of post automatically?

That keeps the automation useful without letting stale task data confidently speak for
you.

For a standup bot, I think the ideal pattern is:

  • gather from tools,
  • generate the summary,
  • show the source links,
  • let the human approve or edit,
  • then post.

Small automation, but it teaches the right agent pattern: assist first, act after
approval.