Practical Slack bots for Product Managers

Yo ✌

A lot of product managers can be code illiterate but you don’t have to be a developer to make use of some handy features in Slack. As long as you’ve used a computer before you should be able to follow along and impress your team with a bot that does something useful (or not).

I’m not a fan of Slack anymore and prefer Discord but a lot of organisations are using it. Anyway, the process to make a bot for Discord or Microsoft Teams is similar.

Since sometime in 2018 your Slack admins can download private conversations via “compliance exports”. Private messages and channels aren’t actually private. Not that anyone should be using the internets to communicate anything they want to keep a secret.

What you’ll need to make a bot

  • Slack account with privileges
  • Some kind of code editor, like Visual Studio Code or Jupyter for using Python
  • Courage, I’ll explain all the steps and keep the code simple so don’t worry if you aren’t a developer

Writing a bot

Set up a Python environment if you don’t have one :

We’ll make a bot that looks at a public Google sheet and notifies some Slack channel each time it is updated.

Make a Google Sheet and click share to make it viewable by anyone who has the link.

Making a google sheet public
Getting the link to a public Google Sheet

A trick to reading any Google sheet in Pandas is to append ‘/export?format=csv&gid=’ after the / following your unique URL ID. This will avoid a bunch of other hassles such as using the Google API.

import pandas as pd
#Replace g_sheet with your own Google Sheet URL
g_sheet = ''
g_csv = g_sheet.replace('/edit#gid=', '/export?format=csv&gid=')
df = pd.read_csv(g_csv)

Sweet, now the DataFrame should update with new values each time you run that cell.

Google sheet with one row
Google Sheet with task of drinking beer and maximum priority of 5
Running the code to read Google Sheetin Python
DataFrame correctly loading Google Sheet data

We can make a simple function that returns the last row of the DataFrame as a string later.

Get a Slack Webhook URL

Go to this URL:

Click the create your Slack app button there. It should take you to this page.

Creating a webhook in Slack

Select the App name and workspace

Naming the bot and picking a channel for the bot in Slack

The next page should have a bunch of features and functionality. We want to add an incoming Webhook.

Creating an incoming Webhook
Click incoming Webhooks on this page
Activating the webhook
Flip that stupid switch on to enable incoming Webhooks

After switching on Webhooks a new option should appear at the bottom. Click to add a new Webhook and pick the appropriate channel.

Adding a new Webhook in slack
Picking the channel to post in for the Slack bot
The Webhook URL which should be kept a secret

Now it should take you back and give you the URL which is a huge secret. So try not to share it unless you want “hackers” ? to post into your Slack. If Slack finds the URL somewhere public they block its use.

Back to writing the Bot

Copy and paste the URL into another cell

#Replace it with your own URL

Import requests and test if everything is working.

import requests
pd.set_option("display.max_colwidth", None)

message = df.iloc[-1].to_string()

payload = {'text':message}, json=payload)
Example of bot posting in Slack

We can clean it all up and put it into a small .PY file.

import pandas as pd
import requests

pd.set_option("display.max_colwidth", None)

g_sheet = ''
g_csv = g_sheet.replace('/edit#gid=', '/export?format=csv&gid=')


def create_message():
    df = pd.read_csv(g_csv)
    message  = df.iloc[-1].to_string()
    return message

def push_message(message):
    payload = {'text':message}, json=payload)


Add another row to the spreadsheet and test it again.

Example of the function working correctly which makes the bot post in Slack


What you learned

  • Writing a neat script
  • Interfacing with Slack via Webhook
  • Reading data from Google Sheets easily
  • It’s not perfect but now that you understand the process to go forth and experiment

Next steps would be having it run in the cloud in some sort of loop or cron job which I’ll write about in the following article: