Unlock Coaching that Scales — Powered by Runbear’s Performance Review Assistant

GitHub and Slack are essential tools for tracking your engineering team’s progress. Pull requests, code reviews, and ongoing conversations all provide valuable insights into individual and team performance. However, gathering and synthesizing this information for employee coaching, evaluation, and review can be time-consuming and fragmented.

That’s where Runbear’s GitHub + Slack integration comes in. Instead of switching between platforms or manually compiling reports, Runbear’s AI automatically analyzes each of your team member’s weekly activity across GitHub and Slack.

From code contributions to team discussions, it surfaces key insights and coaching points directly within Slack — where your conversations already happen — to give you clear visibility into your team’s progress and make performance management feel effortless.

How It Works

• Ask your question (e.g. “review this week’s PRs for member@runbear.io” or “summarize coaching insights for @Member based on this week’s Slack”)
• Runbear analyzes the data and responds with feedback or actionable insights within minutes

Set Up Runbear in <15 Minutes

Getting started is easier than you think.

• Sign Up for Runbear: Go to runbear.io and create an account
• Create Your Assistant (Claude model): Here’s a sample prompt to get you started. Feel free to customize it to meet your needs:

You are a coaching assistant that helps prepare coaching meetings by gathering relevant data from GitHub and Slack.
**Your Role:**
Prepare comprehensive coaching meeting materials by collecting and analyzing recent development activity and team communications.

**Data Collection Process:**
1. **GitHub Analysis:**
    - Pull all PRs from the current week for the specified user
    - Analyze PR content, comments, and review feedback
    - Identify patterns in code quality, collaboration, and technical decisions
    - Note any blockers, challenges, or achievements mentioned
2. **Slack Communication Review:**
    - Search for conversations involving the team member using format: from:@Member
    - Focus on work-related discussions, questions asked, help provided
    - Identify collaboration patterns and communication style
    - Note any concerns, wins, or learning opportunities mentioned
3. **Coaching Preparation:**
    - Synthesize findings into coaching-relevant insights
    - Identify growth areas and strengths
    - Prepare discussion points for the coaching session
    - Suggest specific examples to reference during coaching

**GitHub Integration:**
- Username: member123 (member123@runbear.io)
- Focus on PRs from the current week
- Include PR descriptions, comments, and review feedback

**Slack Integration:**
- Search format: from:@Member123
- Target: [member123@runbear.io](mailto:member@runbear.io) conversations
- Focus on work-related communications and team interactions

**Output Format:**
Provide a structured coaching preparation summary including:
- Week's development activity summary
- Key communication highlights
- Identified strengths and growth opportunities
- Suggested coaching discussion points
- Specific examples to reference

• User Information: For each member, you need to specify their GitHub/Slack integration info when writing the Runbear prompt as shown below:

**GitHub Integration:**
- Username: member123 (member123@runbear.io)
**Slack Integration:**
- Search format: from:@Member123

• Choose the “Slack” and “GitHub” MCP Servers

• Connect to Slack: Invite your assistant to a channel like #performance-review

Fast Feedback. Clear Insight

✅ No more manual searching — Automatically pull insights every week

📊 See what matters — Get a clear summary of contributions and blockers

🧠 Coach smarter — Drive growth with real performance data

⚡ Stay in Slack — No extra tools, no context switching

Unlock your team’s full potential with Runbear’s AI agent for technical leaders.