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by launchdarklylaunchdarkly/agent-skills25 stars
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Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AgentControl implementation in five stages: audit the code, wrap the call, move the tools, add tracking, attach evaluators. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini, Strands) to a managed config, or stage a full hardcoded-to-LaunchDarkly migration.

Requires /configs-create /configs-targeting /tools /online-evals /built-in-metrics

Ask your Agent

Use this Skill: https://skilld.dev/gh/launchdarkly/agent-skills/migrate

This session only. Nothing lands on disk.

README.md

≈680 tokens on demand. Your agent reads this file only when SKILL.md points to it.

LaunchDarkly Config Migrate Skill

An Agent Skill for migrating an application with hardcoded LLM prompts to a full LaunchDarkly AgentControl implementation in five stages: extract, wrap, tools, tracking, evals.

Overview

This skill orchestrates the full migration journey from hardcoded openai.chat.completions.create(model="gpt-4o", ...) (or equivalent in any provider SDK) to a managed config with tools, tracking, and judges. It delegates each stage to a focused skill and covers the tracker wiring inline — since no existing skill owns tracker.track_* calls.

The five stages:

  1. Extract hardcoded model names, prompts, and parameters (read-only)
  2. Wrap the call site in completion_config / completionConfig with a safe fallback — delegates the config creation to configs-create
  3. Tools — move function-calling schemas into LaunchDarkly — delegates to tools
  4. Tracking — wire track_duration, track_tokens, track_success/track_error, optional track_feedback — inline, with a reference doc covering every SDK method in Python and Node side by side
  5. Evals — attach judges for LLM-as-a-judge scoring — delegates to online-evals

Installation (Local)

Copy skills/agentcontrol/migrate/ into your agent client's skills path.

Prerequisites

  • Remotely hosted LaunchDarkly MCP server
  • LD_SDK_KEY environment variable (server-side SDK key, starts with sdk-)
  • An application with hardcoded LLM calls (OpenAI, Anthropic, Bedrock, Gemini, LangChain, LangGraph, CrewAI, or Strands)

Usage

Migrate our chat service from hardcoded OpenAI prompts to LaunchDarkly AgentControl
Our LangGraph agent has its model and instructions baked in — walk me through wrapping it in a config
Wire up the agent tracker and attach accuracy + relevance judges to our existing config

Structure

migrate/
├── SKILL.md
├── README.md
└── references/
    ├── phase-1-analysis-checklist.md
    ├── before-after-examples.md
    ├── sdk-ai-tracker-patterns.md
    ├── agent-mode-frameworks.md
    ├── fallback-defaults-pattern.md
    └── agent-graph-reference.md

Related

License

Apache-2.0

Source: SKILL.md on GitHub

Third-party checks

No alerts2d3 checks · Risk SAFE
  • Gen Agent Trust Hub2d

    A legitimate orchestration skill for migrating hardcoded AI prompts to LaunchDarkly AgentControl. It follows a structured, human-in-the-loop workflow with clear instructions and fallback patterns.

  • Socket2d

    No alerts

  • Snyk2d

    Risk: LOW · No issues

Provenance

Signed by skilld at f1bea2f. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 days ago.

Activeupdated 2 weeks ago

Capability

compatibility
Requires the remotely hosted LaunchDarkly MCP server
metadata
{
  "author": "launchdarkly",
  "version": "0.1.0"
}

Topics

  • Python
  • TypeScript
  • launchdarkly
  • migration
  • llm
  • prompt-management
  • agent
  • openai
  • anthropic
  • bedrock

README badge

README badge for launchdarkly/agent-skills/migrate

What it does

Guides you through migrating a hardcoded LLM application to LaunchDarkly AgentControl in five stages: audit the code, wrap the call, move the tools, add tracking, attach evaluators. Targets Python and Node.js applications calling OpenAI, Anthropic, Bedrock, Gemini, or agent frameworks like LangGraph and CrewAI.

Generated from the current SKILL.md.

Frequently asked

Does this skill work with my LLM provider?
The skill has worked examples for OpenAI, Anthropic, Bedrock, Gemini, and Strands. For LangChain, LangGraph, CrewAI, and custom ReAct loops, coverage varies by language — Python has deep examples; Node.js has partial coverage with ⚠️ annotations. See the coverage table in the SKILL.md for your framework.
What if my app uses a framework not listed in the coverage table?
Apply the three framework-agnostic invariants: one `agent_config` per turn, one tracker per turn, and fire at-most-once methods once at turn end. These rules apply regardless of framework; see agent-mode-frameworks.md § Framework-agnostic invariants.
Can I skip stages or do them out of order?
Stages must be ordered — audit, wrap, tools, tracking, then evaluators. You can ship after Stage 4 (tracking) is complete; Stage 5 (evaluators) is optional. Skipping ahead produces configs without traffic or metrics without context.
Does this skill handle streaming responses?
Streaming support is delegated to the built-in-metrics skill's streaming-tracking.md reference. Use `trackStreamMetricsOf` plus manual TTFT tracking.
What environment setup is required?
You need `LD_SDK_KEY` (server-side SDK key) set, the LaunchDarkly MCP server configured, and the target application already calling an LLM provider with hardcoded prompts and model values. Check the SDK CHANGELOG before starting to ensure no breaking changes post-date this skill.

Generated from the current SKILL.md. These answers refresh after source changes.