Post GitHub Digest to Slack
Pulls daily pull requests, issues, and commits from GitHub, generates a structured engineering digest using AI, and posts it to a Slack channel every morning.
What This Workflow Does
This workflow automatically generates a daily summary of engineering activity from your GitHub repository and delivers it to your team via Slack. It pulls pull requests, issues, and commits, runs them through an AI agent to create a clear and structured report, then posts it directly to a Slack channel of your choice.
The goal is simple: keep your team informed about what's happening in the codebase without anyone having to manually check GitHub or sit through lengthy standup meetings.
How It Works
The workflow runs on a schedule — every day at 9:00 AM UTC. Once triggered, it kicks off three parallel requests to GitHub to gather recent activity. Those results are merged together, analyzed by an AI agent, and the final digest is sent to Slack.
Here's what each step does:
| Step | Node | Description |
|---|---|---|
| 1 | Scheduled Trigger | Fires automatically every day at 9:00 AM UTC. No manual action needed once the workflow is published. |
| 2 | List Pull Requests | Fetches up to 30 recent pull requests sorted by last updated, including both open and closed PRs. |
| 3 | List Issues | Fetches up to 30 recent issues ordered by last updated, capturing bugs, feature requests, and ongoing discussions. |
| 4 | List Commits | Pulls the 30 most recent commits, giving visibility into who's pushing code and what changes are being made. |
| 5 | Merge | Combines output from all three GitHub nodes into a single dataset for the AI agent. |
| 6 | AI Agent | Analyzes the merged GitHub data and generates a well-structured daily digest formatted for Slack. |
| 7 | Slack Send Message | Posts the AI-generated digest to your designated Slack channel with clean markdown formatting. |
What the Digest Looks Like
When the workflow runs, your Slack channel receives a message structured like this:
- A header with the date and repository name
- A section for completed work showing merged pull requests
- A section for work in progress showing open pull requests
- A section for issues and bugs highlighting anything critical
- A section listing recent commits with author names and messages
- A brief team activity summary noting patterns and overall momentum
If there's no data for a particular section, the AI notes that clearly instead of leaving gaps.
What You Need
To use this workflow, you need three things:
- A GitHub account with access to the repository you want to track. The workflow connects via a GitHub connector that you authenticate once during setup.
- A Slack workspace with a channel where you want the digest posted. You authenticate a Slack connector and specify the channel name.
- A Needle account to build, test, and publish the workflow.
Who This Is For
This workflow is built for anyone who wants better visibility into engineering activity without the overhead of manual tracking.
- Engineering teams — Instead of checking GitHub throughout the day, the whole team gets a single summary every morning that shows exactly what happened, what's in progress, and what needs attention.
- Tech leads and engineering managers — Stay on top of your team's output without micromanaging. The digest gives a quick pulse check on velocity, blockers, and who's working on what.
- Startup founders — If you're hands-on with your engineering team, one Slack message in the morning tells you everything you need to know.
- Remote and async teams — When people work across different time zones, a daily digest keeps everyone aligned without requiring synchronous standups. People can read it when their day starts and immediately know the state of things.
Notes
- The workflow currently tracks a single repository. If you need to monitor multiple repos, you can duplicate the workflow and point each one at a different repository.
- The schedule is set to 9:00 AM UTC by default. You can change this to whatever time works best for your team by adjusting the cron expression in the trigger node.
- You can swap the AI model used in the AI Agent node for a different one depending on your preference.
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