LaunchDarkly Projects Setup

You're using a skill that will guide you through setting up LaunchDarkly project management in a codebase. Your job is to explore the codebase to understand the stack and patterns, assess what approach makes sense, choose the right implementation path from the references, execute the setup, and verify it works.

Prerequisites

Choose one:

LaunchDarkly API access token with

projects:write

permission

LaunchDarkly MCP server configured in your environment

Core Principles

Understand First

Explore the codebase to understand the stack and patterns.

Choose the Right Fit

Select an approach that matches your architecture.

Follow Conventions

Respect existing code style and structure.

Verify Integration

Confirm the setup works — the agent performs checks and reports results. API Key Detection Before prompting the user for an API key, try to detect it automatically: Check environment variables — Look for LAUNCHDARKLY_API_KEY , LAUNCHDARKLY_API_TOKEN , or LD_API_KEY Check MCP config — If using Claude, read ~/.claude/config.json for mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY Prompt user — Only if detection fails, ask the user for their API key See Quick Start for API usage patterns. What Are Projects? Projects are LaunchDarkly's top-level organizational containers that hold: All your AI Configs Feature flags and segments Multiple environments (Production and Test created by default) Think of projects as separate applications, services, or teams that need their own isolated set of configurations. Project Setup Workflow Step 1: Explore the Codebase Before implementing anything, understand the existing architecture: Identify the tech stack: What language(s)? (Python, Node.js, Go, Java, etc.) What framework(s)? (FastAPI, Express, Spring Boot, etc.) Is there an existing LaunchDarkly integration? Check environment management: How are environment variables stored? (.env files, secrets manager, config files) Where is configuration loaded? (startup scripts, config modules) Are there existing LaunchDarkly SDK keys? Look for patterns: Are there existing API clients or service modules? How is external API integration typically done? Is there a CLI, scripts directory, or admin tooling? Understand the use case: Is this a new project being set up? Adding to an existing LaunchDarkly integration? Part of a multi-service architecture? Need for project cloning across regions/teams? Step 2: Assess the Situation Based on your exploration, determine the right approach: Scenario Recommended Path New project, no LaunchDarkly integration Quick Setup - Create project and save SDK keys Existing LaunchDarkly usage Add to Existing - Create new project or use existing Multiple services/microservices Multi-Project - Create projects per service Multi-region or multi-tenant Project Cloning - Clone template project Infrastructure-as-Code (IaC) setup Automated Setup - Script-based creation Need project management tooling CLI/Admin Tools - Build project management utilities Step 3: Choose Your Implementation Path Select the reference guide that matches your stack and use case: By Language/Stack: Python Implementation - For Python applications (FastAPI, Django, Flask) Node.js/TypeScript Implementation - For Node.js/Express/NestJS applications Go Implementation - For Go services Multi-Language Setup - For polyglot architectures By Use Case: Quick Start - Create first project and get SDK keys Environment Configuration - Save SDK keys to .env, secrets, or config Project Cloning - Clone projects for regions/teams IaC/Automation - Terraform, scripts, CI/CD integration Admin Tooling - Build CLI or admin utilities Step 4: Implement the Integration Follow the chosen reference guide to implement project management. Key considerations: API Authentication: Store API token securely Follow existing secrets management patterns Never commit tokens to version control Project Naming: Use consistent, descriptive names Follow existing naming conventions Project keys: lowercase, hyphens, start with letter SDK Key Management: Extract and store SDK keys for each environment Use the same pattern as other secrets in your codebase Consider separate keys for test/staging/production Error Handling: Handle existing projects gracefully (409 conflict) Provide clear error messages Don't fail silently Step 5: Verify the Setup After creating the project, verify it works: Fetch via API to confirm it exists: curl -X GET "https://app.launchdarkly.com/api/v2/projects/{projectKey}?expand=environments" \ -H "Authorization: {api_token}" Confirm the response includes the project, environments, and SDK keys. Test SDK integration: Run a quick verification to ensure the SDK key works: from ldclient import set_config , Config set_config ( Config ( "{sdk_key}" ) )

SDK initializes successfully

Report results: ✓ Project exists and has environments ✓ SDK keys are present and valid ✓ SDK can initialize (or flag any issues) Project Key Best Practices Project keys must follow these rules: ✓ Good examples: - "support-ai" - "chat-bot-v2" - "internal-tools" ✗ Bad examples: - "Support_AI" # No uppercase or underscores - "123-project" # Must start with letter - "my.project" # No dots allowed Naming Recommendations: Keep keys short but descriptive Use team/service/purpose as naming scheme Be consistent across your organization Common Organization Patterns By Team platform-ai → Platform Team AI customer-ai → Customer Success Team AI internal-ai → Internal Tools Team AI By Application/Service mobile-ai → Mobile App AI Configs web-ai → Web App AI Configs api-ai → API Service AI Configs By Region/Deployment ai-us → US Region ai-eu → Europe Region ai-apac → Asia-Pacific Region Edge Cases Situation Action Project already exists Check if it's the right one; use it or create with different key Need multiple projects Create separately for each service/region/team Shared configs across services Use same project, separate by SDK context Token lacks permissions Request projects:write or use MCP server Project name conflict Keys must be unique, names can be similar What NOT to Do Don't create projects without understanding the use case first Don't commit API tokens or SDK keys to version control Don't use production SDK keys in test/development environments Don't create duplicate projects unnecessarily Don't skip the exploration phase Next Steps After setting up projects: Create AI Configs - Use the aiconfig-create skill Set up SDK Integration - Use the aiconfig-sdk skill Configure Targeting - Use the aiconfig-targeting skill