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configs-update — Claude Code skill for LaunchDarkly configs

A skill that walks the agent through updating, archiving and deleting LaunchDarkly configs and their variations. Handy when you switch models or edit prompts.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add launchdarkly/ai-tooling --skill configs-update -a claude-code
Open on GitHub

What configs-update does

The skill sets an order for config work: read the current state, make the change, then verify. It starts with get-ai-config-health, which flags variations with no model, variations with neither instructions nor messages, tool references that point at nothing, and configs with no variations at all. Then get-ai-config returns the full picture: mode (agent or completion), variation models, current instructions, attached tools and parameters.

Config metadata goes through update-ai-config: name, description, tags, archiving. A variation is edited with update-ai-config-variation — swap the model via modelConfigKey and modelName, rewrite instructions or messages, tune parameters such as temperature and max_tokens, attach or detach tools. Deletion (delete-ai-config, delete-ai-config-variation) is irreversible and needs confirm: true.

You need the remotely hosted LaunchDarkly MCP server configured, otherwise the skill has nothing to call. When someone says remove, retire or decommission, the skill archives by default, since archived: false brings the config back. Permanent deletion waits for an explicit confirmation. It also tells the agent to change one thing at a time and not to touch a production config before testing in another variation.

What it helps with

  • switch the model on a config variation
  • rewrite instructions or messages in a prompt
  • tune temperature and max_tokens
  • retire an unused config by archiving it
  • spot variations with no model and broken tool references

Example prompts for the agent

  • check the config health and tell me what's broken
  • switch the variation to a new model and verify it
  • archive this config, we don't need it anymore

How to install configs-update

1Skills CLI (Vercel)

npx skills add launchdarkly/ai-tooling --skill configs-update -a claude-code

2Manually: put the files in place

~/.claude/skills/configs-update/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/configs-update/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about configs-update

How do I install configs-update in Claude?

Run npx skills add launchdarkly/ai-tooling --skill configs-update -a claude-code. The skill lands in ~/.claude/skills/configs-update/SKILL.md.

What does configs-update do?

It guides the agent through updating, archiving and deleting LaunchDarkly configs and their variations — metadata, models, prompts and parameters — and re-fetches the config afterwards to confirm the change.

What does the skill need to run?

A configured remotely hosted LaunchDarkly MCP server. The skill calls its tools get-ai-config-health, get-ai-config, update-ai-config and update-ai-config-variation.

Source text

SKILL.md · Apache-2.0
---
name: configs-update
description: "Update, archive, and delete LaunchDarkly configs and their variations. Use when you need to modify config properties, change model parameters, update instructions or messages, archive unused configs, or permanently remove them."
license: Apache-2.0
compatibility: Requires the remotely hosted LaunchDarkly MCP server
metadata:
  author: launchdarkly
  version: "1.0.0-experimental"
---

# Config Update & Lifecycle

You're using a skill that will guide you through updating, archiving, and deleting configs and their variations. Your job is to understand the current state of the config, make the changes, and verify the result.

## Prerequisites

This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.

**Required MCP tools:**
- `get-ai-config-health` -- assess config health before making changes (detects missing models, orphaned tools, empty configs)
- `get-ai-config` -- understand current state before making changes
- `update-ai-config` -- update config metadata (name, description, tags, archive)
- `update-ai-config-variation` -- update variation model, prompts, or parameters

**Optional MCP tools:**
- `delete-ai-config` -- permanently delete a config (irreversible)
- `delete-ai-config-variation` -- permanently delete a variation (irreversible)

## Core Principles

1. **Fetch Before Changing**: Always check the current state before modifying
2. **Verify After Changing**: Fetch the config again to confirm updates were applied
3. **Archive Before Deleting**: Archival is reversible; deletion is not

## Workflow

### Step 1: Assess Health and Understand Current State

Start with `get-ai-config-health` to get a structured health assessment. This detects:
- Variations with no model (show as "NO MODEL" in the UI)
- Variations with neither instructions nor messages
- Orphaned tool references (tools attached that don't exist in the project)
- Configs with no variations at all

The health verdict (`healthy`, `warning`, `unhealthy`) helps you prioritize what to fix.

Then use `get-ai-config` to review the full detail:
- Current mode (agent or completion)
- Existing variations and their models
- Current instructions or messages
- Attached tools and parameters

### Step 2: Make the Update

**Update config metadata** -- Use `update-ai-config`:
- Change name or description
- Add or replace tags
- Archive with `archived: true` (reversible)

**Update a variation** -- Use `update-ai-config-variation`:
- Switch model (provide new `modelConfigKey` and `modelName`)
- Change instructions or messages
- Tune parameters (temperature, max_tokens, etc.)
- Attach or detach tools via the parameters object

**Archive a config** -- Use `update-ai-config` with `archived: true`. Archiving is the **preferred** way to retire a config:
- It is reversible (unarchive with `archived: false`)
- The config is hidden from active lists but preserved
- After calling the archive, treat a successful response as confirmation and proceed to verification
- When a user says "remove", "retire", "decommission", or "no longer need", default to archiving unless they explicitly say "delete permanently"

**Delete** -- Use `delete-ai-config` or `delete-ai-config-variation` (irreversible, requires `confirm: true`). **Always suggest archiving first.** Only proceed with deletion if the user explicitly confirms they want permanent, irreversible removal.

### Step 3: Verify

Use `get-ai-config` to confirm the response shows your updated values.

**Report results:**
- Update applied successfully
- Config reflects changes
- Flag any issues or rollback if needed

## What NOT to Do

- Don't update production configs without testing in another variation first
- Don't change multiple things at once -- make incremental changes
- Don't skip verification
- Don't delete without explicit user confirmation -- always suggest archiving first
- Don't retry an update because the API response doesn't echo back the exact values you sent -- verify with `get-ai-config` instead

## More resources

To learn more about creating and managing variations, read [Create and manage config variations](https://launchdarkly.com/docs/home/agentcontrol/create-variation.md).

## Related Skills

- `configs-variations` -- Create variations to test changes side-by-side
- `tools` -- Update tool attachments

© launchdarkly · Apache-2.0 · launchdarkly/ai-tooling

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