GitHub Analytics

Overview

Integrate GitHub Analytics to measure and track engineering performance metrics directly from CORE. Access comprehensive DORA metrics (Deployment Frequency, Lead Time, Change Failure Rate) along with additional delivery, stability, and code quality metrics to gain insights into your team’s productivity and engineering excellence.

Setup Guide

Authentication Type : OAuth 2.0

How to Connect

Navigate to CORE Dashboard → Integrations
Click on the GitHub Analytics card
Click Connect to authorize CORE
You’ll be redirected to GitHub to authorize the application
Grant the required permissions (read access to repositories and their data)
You’ll be redirected back to CORE once connected

Use Cases

Track DORA Metrics

Scenario: Get all four key DORA metrics for your repository

"Calculate all DORA metrics for the 'myorg/myapp' repository
for the last 30 days"

The agent will use all_metrics to calculate:

Deployment Frequency: releases per week
Lead Time for Changes: time from commit to production
Change Failure Rate: percentage of failed deployments
Time to Restore Service: recovery time from failures

Monitor Deployment Frequency

Scenario: Track how often your team deploys to production

"What's the deployment frequency for 'company/product'?
Show me the last 60 days and compare it to the previous 60 days"

The agent will use deployment_frequency with:

days: 60
compareWithPrevious: true

This reveals trends in deployment velocity and identifies acceleration or slowdowns.

Measure Lead Time

Scenario: Understand how long it takes from code commit to production

"Calculate the lead time for changes in 'team/api' repository.
I want to see if we're improving our time to production"

The agent will use lead_time_for_changes to measure the average time from first commit to deployment, helping identify bottlenecks in your delivery pipeline.

Analyze PR Performance

Scenario: Evaluate pull request merge times and throughput

"Show me PR metrics for 'org/web-app':
- Average time to merge a PR
- Number of PRs merged per week
- Average PR size"

The agent will use:

pr_merge_time: Average hours from PR creation to merge
pr_throughput: PRs merged per week (velocity indicator)
pr_size: Average lines changed per PR (complexity indicator)

Team Productivity Insights

Scenario: Measure overall team productivity and commit activity

"How productive is our team on the 'core' repository?
Show me commit frequency and compare with last month"

The agent will use commit_frequency to show:

Commits per week to main branch
Week-over-week trends
Team activity patterns

Engineering Metrics Dashboard

Scenario: Build a comprehensive performance dashboard

"Create a performance report for 'company/main-app':
- All DORA metrics for the last quarter
- Stability metrics (change failure rate, hotfix rate, revert rate)
- Code quality (average PR size)
- Compare current period with previous quarter"

The agent will use all_metrics with custom date ranges to generate a comprehensive engineering performance report suitable for stakeholder presentations.

Stability Assessment

Scenario: Understand your system’s stability and reliability

"How stable is our 'backend/api' repository?
Show me change failure rate, hotfix rate, and revert rate for the last month"

The agent will use:

change_failure_rate: Percentage of deployments causing issues
hotfix_rate: Percentage of emergency releases
revert_rate: Percentage of merged PRs that get reverted

These metrics together indicate system stability and code quality.

Identify Bottlenecks

Scenario: Find where delays occur in your delivery process

"Analyze delivery metrics for 'team/service':
- How often do we deploy?
- How long does it take from commit to deployment?
- How long do PRs sit before being merged?
- What's our change failure rate?"

The agent will correlate multiple metrics to identify whether delays are in:

Code development (commit frequency)
Code review (PR merge time)
Deployment process (deployment frequency, lead time)
Quality assurance (change failure rate)

Custom Date Range Analysis

Scenario: Analyze metrics for specific periods

"Calculate all metrics for 'org/app' from 2024-01-01 to 2024-03-31
(Q1 performance review)"

Using startDate and endDate parameters allows analysis of:

Quarterly performance reviews
Sprint-specific metrics
Post-incident analysis windows
Year-over-year comparisons

Incident Impact Analysis

Scenario: Understand the impact of incidents on metrics

"For 'platform/core', what's the change failure rate?
Use custom incident labels: ['critical', 'p0', 'sev1']"

The agent will calculate failure rate based on your organization’s incident classification system, enabling accurate measurement of production stability.

Understanding the Metrics

DORA Metrics (Industry Standard)

Deployment Frequency: How often releases happen to production

Elite: On-demand
High: Weekly releases
Medium: Monthly releases
Low: Less than monthly

Lead Time for Changes: Time from code commit to production

Elite: Less than 1 hour
High: 1 hour to 1 day
Medium: 1 day to 1 week
Low: More than 1 week

Change Failure Rate: Percentage of deployments causing failures

Elite: 0-15% failure rate
High: 16-30%
Medium: 31-45%
Low: 46%+

Time to Restore Service: How quickly you recover from failures

Elite: Less than 1 hour
High: 1-24 hours
Medium: 1-7 days
Low: More than 7 days

Delivery Metrics

PR Merge Time: Average hours from PR creation to merge (indicates review efficiency)
PR Throughput: Number of PRs merged per week (indicates team velocity)
Commit Frequency: Number of commits to main branch per week (indicates development activity)

Stability Metrics

Hotfix Rate: Percentage of emergency releases (indicates production pressure)
Revert Rate: Percentage of merged PRs that get reverted (indicates code quality issues)

Integration Notes

All metrics are calculated in real-time from GitHub data
Analysis includes merged and released code only
Custom date ranges provide flexibility for any analysis window
Week-over-week comparisons help identify trends
Metrics support both organizations and personal repositories

Scopes

repo - Repository access (read)
read:org - Read organization data
read:user - Read user profile data

Available MCP Tools

The GitHub Analytics integration provides 10 tools for comprehensive performance tracking:

Delivery Speed
Stability & Reliability
Code Quality
Comprehensive Analysis

Tool Name	Description
`deployment_frequency`	Calculate deployment frequency - number of releases/deployments per week. DORA metric for delivery speed.
`lead_time_for_changes`	Calculate lead time for changes - time from first commit to production deployment (in hours/days). DORA metric for delivery speed.
`pr_merge_time`	Calculate PR merge time - average time from PR creation to merge (in hours). Delivery speed metric.
`pr_throughput`	Calculate PR throughput - number of PRs merged per week. Delivery speed metric.
`commit_frequency`	Calculate commit frequency - number of commits to main branch per week. Delivery speed metric.

Tool Name	Description
`change_failure_rate`	Calculate change failure rate - percentage of deployments that cause production failures. DORA metric for stability & reliability.
`hotfix_rate`	Calculate hotfix rate - percentage of releases that are emergency hotfixes. Stability & reliability metric.
`revert_rate`	Calculate revert rate - percentage of merged PRs that get reverted. Stability & reliability metric.

Tool Name	Description
`pr_size`	Calculate PR size - average lines changed (additions + deletions) per PR. Code quality metric.

Tool Name	Description
`all_metrics`	Calculate all GitHub analytics metrics at once - includes all DORA metrics, delivery speed, stability, and code quality metrics.

Tool Parameters

Common Parameters

Most analytics tools accept the following parameters:

Parameter	Type	Required	Description
`owner`	string	Yes	Repository owner (organization or user)
`repo`	string	Yes	Repository name
`days`	number	No	Number of days to analyze (default: 30)
`startDate`	string	No	Start date in YYYY-MM-DD format (overrides days)
`endDate`	string	No	End date in YYYY-MM-DD format (default: today)
`compareWithPrevious`	boolean	No	Compare with previous period for week-over-week analysis (default: false)

Specialized Parameters

Commit Frequency: Includes branch parameter (default: main) to analyze specific branches
Change Failure Rate: Includes incidentLabels array parameter (default: [“incident”, “production”, “outage”, “bug”]) to identify production incidents
Hotfix Rate: Includes hotfixPatterns array parameter (default: [“hotfix”, “emergency”, “patch”]) to identify emergency releases

Need Help?

Join our Discord community for support in #core-support

Overview

Communication & Collaboration

Project & Task Management

Development & Version Control

Calendar & Scheduling

Knowledge & Documentation

Data & Spreadsheets

CRM & Sales

Overview

Setup Guide

How to Connect

Use Cases

Understanding the Metrics

DORA Metrics (Industry Standard)

Delivery Metrics

Stability Metrics

Integration Notes

Scopes

Available MCP Tools

Tool Parameters

Common Parameters

Specialized Parameters

Need Help?

​Overview

​Setup Guide

​How to Connect

​Use Cases

​Understanding the Metrics

​DORA Metrics (Industry Standard)

​Delivery Metrics

​Stability Metrics

​Integration Notes

​Scopes

​Available MCP Tools

​Tool Parameters

​Common Parameters

​Specialized Parameters

​Need Help?

Overview

Setup Guide

How to Connect

Use Cases

Understanding the Metrics

DORA Metrics (Industry Standard)

Delivery Metrics

Stability Metrics

Integration Notes

Scopes

Available MCP Tools

Tool Parameters

Common Parameters

Specialized Parameters

Need Help?