A bot for Trello automation

Hacker News

Hi, I recently started using Trello. Like many other users, I need some automation around typical actions and workflows. It's cool to use IFTTT or Zapier for those, but I decided to develop a little bot that could do some of the most common actions in Trello, from within Trello itself. The bot can perform the following actions: - Create a card in a list. - Copy or move a card into a list. - Add or remove a label from a card. - Count the labels in a board. These actions can be triggered: - Immediately. - Periodically, every day or certain days at (optionally) a given time. - When a card is moved to a board. - When a card is created, moved or copied to a list. - When a label is added or removed from card. - When an attachment is added to a card. Some examples of commands it understands: every friday at 5am, count labels Every day at 6am, create a card titled "Good Morning!" in list "Vibes" when i move a card to the "Orange Things" list, add the orange label to it when a card is moved to the "Done Things" list, remove the red "ToDo" label from it When the label "ToDo" is added to a card, copy it to the list "ToDo's". when i add an attachment to a card, add a pink label called "HasAttachment". The way to use it is simply to add the user butlerbot (https://trello.com/butlerbot) to a board. The bot will create a list called "Butler" and you can enter commands there as cards. The bot doesn't store, mine, forward or otherwise do anything evil with the cards, it just executes your commands to the best of its ability. An advantage to having the rules in a list on every board is that I can have different rules for different boards, and also that I don't need to switch to another environment to manage my workflows. There's certainly a lot more that it can be done, but I wanted to put it out there to see if this could be useful to other people. Also happy to consider open sourcing it if there's any interest.

Visit product View on Hacker NewsApr 18, 2016

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting

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best fitHighest predicted score across all platforms for this description.

TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal

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Product HuntUnlikely to reach the leaderboard · Strong signals: user, using, open · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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