Claude-skill-registry adding-models

Guide for adding new LLM models to Letta Code. Use when the user wants to add support for a new model, needs to know valid model handles, or wants to update the model configuration. Covers models.json configuration, CI test matrix, and handle validation.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/adding-models" ~/.claude/skills/majiayu000-claude-skill-registry-adding-models && rm -rf "$T"

manifest: skills/data/adding-models/SKILL.md

Adding Models

This skill guides you through adding a new LLM model to Letta Code.

Quick Reference

Key files:

```
src/models.json
```
- Model definitions (required)
```
.github/workflows/ci.yml
```
- CI test matrix (optional)
```
src/tools/manager.ts
```
- Toolset detection logic (rarely needed)

Workflow

Step 1: Find Valid Model Handles

Query the Letta API to see available models:

curl -s https://api.letta.com/v1/models/ | jq '.[] | .handle'

Or filter by provider:

curl -s https://api.letta.com/v1/models/ | jq '.[] | select(.handle | startswith("google_ai/")) | .handle'

Common provider prefixes:

```
anthropic/
```
- Claude models
```
openai/
```
- GPT models
```
google_ai/
```
- Gemini models
```
google_vertex/
```
- Vertex AI
```
openrouter/
```
- Various providers

Step 2: Add to models.json

Add an entry to

src/models.json

{
  "id": "model-shortname",
  "handle": "provider/model-name",
  "label": "Human Readable Name",
  "description": "Brief description of the model",
  "isFeatured": true,  // Optional: shows in featured list
  "updateArgs": {
    "context_window": 180000,
    "temperature": 1.0  // Optional: provider-specific settings
  }
}

Field reference:

```
id
```
: Short identifier used with
```
--model
```
flag (e.g.,
```
gemini-3-flash
```
)
```
handle
```
: Full provider/model path from the API (e.g.,
```
google_ai/gemini-3-flash-preview
```
)
```
label
```
: Display name in model selector
```
description
```
: Brief description shown in selector
```
isFeatured
```
: If true, appears in featured models section
```
updateArgs
```
: Model-specific configuration (context window, temperature, reasoning settings, etc.)

Provider prefixes:

```
anthropic/
```
- Anthropic (Claude models)
```
openai/
```
- OpenAI (GPT models)
```
google_ai/
```
- Google AI (Gemini models)
```
google_vertex/
```
- Google Vertex AI
```
openrouter/
```
- OpenRouter (various providers)

Step 3: Test the Model

Test with headless mode:

bun run src/index.ts --new --model <model-id> -p "hi, what model are you?"

Example:

bun run src/index.ts --new --model gemini-3-flash -p "hi, what model are you?"

Step 4: Add to CI Test Matrix (Optional)

To include the model in automated testing, add it to

.github/workflows/ci.yml

# Find the headless job matrix around line 122
model: [gpt-5-minimal, gpt-4.1, sonnet-4.5, gemini-pro, your-new-model, glm-4.6, haiku]

Toolset Detection

Models are automatically assigned toolsets based on provider:

```
openai/*
```
→
```
codex
```
toolset
```
google_ai/*
```
or
```
google_vertex/*
```
→
```
gemini
```
toolset
Others →
```
default
```
toolset

This is handled by

isGeminiModel()

and

isOpenAIModel()

src/tools/manager.ts

. You typically don't need to modify this unless adding a new provider.

Common Issues

"Handle not found" error: The model handle is incorrect. Run the validation script to see valid handles.

Model works but wrong toolset: Check

src/tools/manager.ts

to ensure the provider prefix is recognized.