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configs-update

launchdarkly/ai-tooling

Update, archive, and delete LaunchDarkly configs and their model variations.

What is configs-update?

Manage the lifecycle of LaunchDarkly AI configs by updating properties, model parameters, instructions, and messages. Use this skill to modify existing configs, archive them reversibly, or permanently delete them when no longer needed.

  • Update config metadata (name, description, tags)
  • Modify variation models, prompts, instructions, and parameters
  • Attach or detach tools from variations
  • Archive configs reversibly without deletion
  • Permanently delete configs or variations with explicit confirmation
  • Assess config health before making changes

How to install configs-update

npx skills add https://github.com/launchdarkly/ai-tooling --skill configs-update
Prerequisites
  • LaunchDarkly MCP server remotely hosted and configured in your environment
  • Access to the LaunchDarkly AI config API
Claude Code
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How to use configs-update

  1. 1.Run `get-ai-config-health` to assess the current state and detect issues
  2. 2.Use `get-ai-config` to review full config details before making changes
  3. 3.Call `update-ai-config` to modify metadata or archive the config
  4. 4.Call `update-ai-config-variation` to change models, prompts, or parameters
  5. 5.Run `get-ai-config` again to verify your changes were applied
  6. 6.For deletion, use `delete-ai-config` or `delete-ai-config-variation` only with explicit user confirmation

Use cases

Good for
  • Change a model parameter like temperature or max_tokens on a production variation
  • Update system instructions or user messages across a config
  • Archive an outdated config instead of deleting it
  • Switch a variation to a different model
  • Detect and fix orphaned tool references or missing models
Who it's for
  • AI config managers
  • Product teams maintaining LaunchDarkly configs
  • Developers tuning model behavior
  • Teams managing multiple config variations

configs-update FAQ

Should I delete or archive a config I no longer need?

Archive first (reversible via `update-ai-config` with `archived: false`). Only delete if the user explicitly confirms they want permanent, irreversible removal.

Can I update multiple properties at once?

No. Make incremental changes one at a time, verify each with `get-ai-config`, then proceed to the next change.

What does the health verdict tell me?

It detects variations with no model, missing instructions/messages, orphaned tool references, and configs with no variations—helping you prioritize fixes.

Do I need to test changes before applying to production?

Yes. Create a test variation first using the `configs-variations` skill, verify behavior, then apply to production.

What if the API response doesn't echo back my exact values?

Don't retry immediately. Always verify by calling `get-ai-config` to confirm the update was applied correctly.

Full instructions (SKILL.md)

Source of truth, from launchdarkly/ai-tooling.


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.

Related Skills

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