aiconfig-variations

📁 launchdarkly/agent-skills 📅 2 days ago
19
总安装量
4
周安装量
#18569
全站排名
安装命令
npx skills add https://github.com/launchdarkly/agent-skills --skill aiconfig-variations

Agent 安装分布

mcpjam 4
gemini-cli 4
claude-code 4
junie 4
windsurf 4
zencoder 4

Skill 文档

AI Config Variations

You’re using a skill that will guide you through testing and optimizing AI configurations through variations. Your job is to design experiments, create variations, and systematically find what works best.

Prerequisites

  • Existing AI Config (use aiconfig-create first)
  • LaunchDarkly API access token or MCP server
  • Clear hypothesis about what to test

Core Principles

  1. Test One Thing at a Time: Change model OR prompt OR parameters, not all at once
  2. Have a Hypothesis: Know what you’re trying to improve
  3. Measure Results: Use metrics to compare variations
  4. Verify via API: The agent fetches the config to confirm variations exist

API Key Detection

  1. Check environment variables — LAUNCHDARKLY_API_KEY, LAUNCHDARKLY_API_TOKEN, LD_API_KEY
  2. Check MCP config — If applicable
  3. Prompt user — Only if detection fails

Workflow

Step 1: Identify What to Optimize

What’s the problem? Cost, quality, speed, accuracy? How will you measure success?

Step 2: Design the Experiment

Goal What to Vary
Reduce cost Cheaper model (e.g., gpt-4o-mini)
Improve quality Better model or prompt
Reduce latency Faster model, lower max_tokens
Increase accuracy Different model (Claude vs GPT-4)

Step 3: Create Variations

Follow API Quick Start:

  • POST /projects/{projectKey}/ai-configs/{configKey}/variations
  • Include modelConfigKey (required for UI)
  • Keep everything else constant except what you’re testing

Step 4: Set Up Targeting

Use aiconfig-targeting skill to control distribution (e.g., 50/50 split for A/B test).

Step 5: Verify

  1. Fetch config:

    GET /projects/{projectKey}/ai-configs/{configKey}
    
  2. Confirm variations exist with correct model and parameters

  3. Report results:

    • ✓ Variations created
    • ✓ Models and parameters correct
    • ⚠️ Flag any issues

modelConfigKey

Required for models to show in UI. Format: {Provider}.{model-id} — e.g., OpenAI.gpt-4o, Anthropic.claude-sonnet-4-5.

What NOT to Do

  • Don’t test too many things at once
  • Don’t forget modelConfigKey
  • Don’t make decisions on small sample sizes

Related Skills

  • aiconfig-create — Create the initial config
  • aiconfig-targeting — Control who gets which variation
  • aiconfig-update — Refine based on learnings

References