Skip to main content

What You'll Build

A fully automated workflow that uses Continue CLI with the PostHog MCP to fetch analytics data, analyze user experience issues with AI, and automatically create GitHub issues with the GitHub CLI.

​
What You’ll Learn

This cookbook teaches you to:
  • Use PostHog MCP to query analytics, errors, and feature flags
  • Analyze user behavior patterns with AI
  • Automatically create GitHub issues using GitHub CLI
  • Set up continuous monitoring with GitHub Actions

​
Prerequisites

Before starting, ensure you have:
1

Install Continue CLI

npm i -g @continuedev/cli
2

Set up Continue CLI Account & API Key

  1. Visit Continue Organizations
  2. Sign up or log in to your Continue account
  3. Navigate to your organization settings
  4. Click “API Keys” and then ”+ New API Key”
  5. Copy the API key immediately (you won’t see it again!)
  6. Login to the CLI: cn login
3

Add Required Secrets to Continue CLI

Continue CLI will securely store your API keys as secrets that can be referenced in prompts.
Continue CLI handles the complex API interactions - you just need to provide the right prompts!

​
Step 1: Set Up Your Credentials

First, you’ll need to gather your PostHog and GitHub API credentials and add them as secrets in Continue CLI.
  • PostHog API Credentials
  • Set Up GitHub CLI Authentication
  • Configure API Access in Continue CLI
You’ll need a Personal API Key (not a Project API key) to access session recordings:
  1. Go to Personal API Keys in PostHog
  2. Click + Create a personal API Key
  3. Name it “Continue CLI Session Analysis”
  4. Select these scopes:
    • session_recording:read - Required for accessing session data
    • feature_flag:read - Required for feature flag auditing
    • insight:read
    • query:read
    • session_recording_playlist:read
  5. Copy the key immediately (you won’t see it again!)
  6. Note your Project ID from your PostHog project settings
  7. Note your PostHog host URL (e.g., https://us.posthog.com or your custom domain)
  8. You’ll also need your POSTHOG_AUTH_HEADER value, which is simply Bearer YOUR_API_KEY
Continue Secrets: The POSTHOG_AUTH_HEADER secret should be stored in Continue’s secure secrets storage. This keeps your API key safe and the MCP automatically connects to your default PostHog project.

​
PostHog GitHub Continuous AI Workflow Options

🚀 Fastest Path to Success

Skip the manual setup and use our pre-built PostHog GitHub agent that includes optimized prompts, rules, and the PostHog MCP for more consistent results.
How PostHog MCP Works:
  • Your API key is tied to your PostHog account and organization
  • It automatically uses your default project (no project ID needed)
  • If you have multiple projects, use mcp__posthog__switch-project to change
  • The MCP connects via https://mcp.posthog.com/sse using your account context.
Why GitHub CLI over GitHub MCP: While GitHub MCP is available, it can be token-expensive to run. The gh CLI is more efficient, requires no API tokens (authenticated via gh auth login), and provides a cleaner command-line experience. GitHub MCP remains an option if you prefer full MCP integration.
To use the pre-built agent, you need either:
  • Continue CLI Pro Plan with the models add-on, OR
  • Your own API keys added to Continue Hub secrets (same as Step 1 below) The agent will automatically detect and use your configuration.
Repository Labels Required: Make sure your GitHub repository has these labels:
  • bug, enhancement, technical-debt
  • high-priority, medium-priority, low-priority
  • user-experience, automated, feature-flag, cleanup
Create missing labels in your repo at: Settings → Labels → New label
What Continue CLI Does:
  • Parses your analysis results automatically
  • Makes authenticated GitHub API calls using your stored token
  • Creates properly formatted issues with appropriate labels
  • Checks for duplicate issues to avoid spam
  • Provides confirmation with issue URLs

​
What You’ve Built

After completing this guide, you have a complete Continuous AI system that:
  • Monitors user experience - Automatically fetches and analyzes PostHog session data
  • Identifies problems intelligently - Uses AI to spot patterns and technical issues
  • Creates actionable tasks - Generates GitHub issues with specific recommendations
  • Runs autonomously - Operates daily without manual intervention using GitHub Actions
  • Scales with your team - Handles growing amounts of session data automatically

Continuous AI

Your system now operates at Level 2 Continuous AI - AI handles routine analysis tasks with human oversight through GitHub issue review and prioritization.

​
Security Best Practices

Protect Your API Keys:
  • Store all credentials as GitHub Secrets, never in code
  • Use Continue CLI’s secure secret storage
  • Limit token scopes to minimum required permissions
  • Rotate API keys regularly (every 90 days recommended)
  • Monitor token usage for unusual activity

​
Example Use Cases

Here are practical examples of what you can build with PostHog MCP and Continue CLI:

​
Session Recording Analysis (Current Implementation)

The main workflow above focuses on analyzing session recordings to identify UX issues and create GitHub issues automatically.

​
Feature Flag Audit and Cleanup

🏁 Feature Flag Management

Automatically audit your feature flags to identify unused, outdated, or problematic flags that need attention.
What this workflow does:
  • Fetches all feature flags from your PostHog project
  • Analyzes flag usage, rollout status, and configuration
  • Identifies flags that may be candidates for removal or updates
  • Creates GitHub issues for flag cleanup tasks
Example Continue CLI prompts: 
# Get all feature flags and analyze them
cn "Use PostHog MCP to fetch all feature flags with mcp__posthog__feature-flag-get-all. Then analyze each flag to identify: 1) Flags that are 100% rolled out and could be removed, 2) Flags that haven't been updated in 90+ days, 3) Flags with complex targeting that might need simplification, 4) Experimental flags that should be cleaned up."

# Create cleanup issues for identified flags
cn "For each problematic feature flag identified, create a GitHub issue using gh CLI:
- Title: '🏁 Feature Flag Cleanup: [flag_name]'
- Include flag details: rollout percentage, last modified date, targeting rules
- Add labels: 'technical-debt', 'feature-flag', 'cleanup'
- Set priority based on risk level (high for 100% rollouts, medium for stale flags)
- Include specific recommendations for each flag"

# Audit flag performance impact
cn "Cross-reference feature flags with PostHog performance metrics to identify flags that may be impacting user experience or site performance. Create performance-focused GitHub issues for flags showing negative impact."
Required PostHog MCP Tools:
  • feature-flag-get-all - Retrieve all feature flags
  • feature-flag-get-definition - Get detailed flag configuration
  • query-run - Run analytics queries to check flag usage
  • insights-get-all - Get insights related to flag performance
Sample Output: This workflow creates GitHub issues like:
  • ”🏁 Feature Flag Cleanup: dark-mode-toggle” (100% rollout, safe to remove)
  • ”🏁 Feature Flag Review: experimental-checkout” (unused for 120 days)
  • ”🏁 Feature Flag Simplify: complex-user-targeting” (overly complex rules)

​
Advanced Prompts

Consider enhancing your workflow with these advanced Continue CLI prompts:

Performance Analysis

“Analyze PostHog performance metrics alongside session recordings to identify slow page loads affecting user experience”

Error Correlation

“Cross-reference JavaScript console errors with user actions to identify the root cause of UX issues”

Feature Flag Performance Impact

“Use PostHog MCP to correlate feature flag rollouts with performance metrics and user behavior changes to identify flags causing issues”

Custom Slack Alerts

“Create Slack alerts when critical UX issues are detected in PostHog sessions or when feature flags need attention”

​
Next Steps

​
Resources