aiconfig-update

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• Fetch Before Changing: Always check the current state before modifying

• Verify After Changing: Fetch the config again to confirm updates were applied

• 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

Related Skills

• aiconfig-variations -- Create variations to test changes side-by-side

• aiconfig-tools -- Update tool attachments