You're using a skill that will guide you through setting up AI configuration in your application. Your job is to explore the codebase to understand the use case and stack, choose agent vs completion mode, create the config following the right path, and verify it works.
Prerequisites
LaunchDarkly API access token with
ai-configs:write
permission or MCP server
LaunchDarkly project (use
aiconfig-projects
skill if needed)
Core Principles
Understand the Use Case First
Know what you're building before choosing a mode
Choose the Right Mode
Agent mode vs completion mode depends on your framework and needs
Two-Step Creation
Create config first, then create variations (model, prompts, parameters)
Verify via API
The agent fetches the config to confirm it was created correctly
API Key Detection
Check environment variables
—
LAUNCHDARKLY_API_KEY
,
LAUNCHDARKLY_API_TOKEN
,
LD_API_KEY
Check MCP config
— Claude:
~/.claude/config.json
→
mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY
Prompt user
— Only if detection fails
Workflow
Step 1: Understand Your Use Case
Before creating, identify what you're building:
What framework?
LangGraph, LangChain, CrewAI, OpenAI SDK, Anthropic SDK, custom
What does the AI need?
Just text, or tools/function calling?
Agent or completion?
See decision below
Step 2: Choose Agent vs Completion Mode
Your Need
Mode
Persistent instructions across interactions
Agent
LangGraph, CrewAI, AutoGen
Agent
Direct OpenAI/Anthropic API calls
Completion
Full control of message structure
Completion
One-off text generation
Completion
Both modes support tools.
Agent mode: single
instructions
string. Completion mode: full
messages
array.
Step 3: Create the Config
Follow
API Quick Start
for curl examples:
Create config
—
POST /projects/{projectKey}/ai-configs
(key, name, mode)
Create variation
—
POST /projects/{projectKey}/ai-configs/{configKey}/variations
(instructions or messages, modelConfigKey, model.parameters)
Attach tools
— After creation, PATCH variation to add tools (see
aiconfig-tools
skill)
Step 4: Verify
After creation, verify the config:
Fetch via API:
curl
-X
GET
"https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs/{configKey}"
\
-H
"Authorization: {api_token}"
-H
"LD-API-Version: beta"
Confirm:
Config exists with correct mode
Variations have model names (not "NO MODEL")
modelConfigKey is set
Parameters are present
Report results:
✓ Config created with correct structure
✓ Variations have models assigned
⚠️ Flag any missing model or parameters
Provide config URL:
https://app.launchdarkly.com/projects/{projectKey}/ai-configs/{configKey}
Important Notes
modelConfigKey
must be
{Provider}.{model-id}
(e.g.,
OpenAI.gpt-4o
) for models to show in UI
Tools
must be created first (
aiconfig-tools
skill), then attached via PATCH
Tools endpoint
is
/ai-tools
, NOT
/ai-configs/tools
Edge Cases
Situation
Action
Config already exists
Ask if user wants to update instead
Variation shows "NO MODEL"
PATCH variation with modelConfigKey and model
Invalid modelConfigKey
Use values from model-configs API
What NOT to Do
Don't create configs without understanding the use case
Don't skip the two-step process (config then variation)
Don't try to attach tools during initial creation
Don't forget modelConfigKey (models won't show)