Image Generation logo

Image Generation

OrganizationPopular
letta-ai
image-generation

Generate images from text prompts (and optionally edit/remix input images). Use when the user asks to create, generate, draw, render, or edit an image, illustration, logo, icon, diagram, or photo.

Overview

Publisherletta-ai
Repositoryletta-code
Skill nameimage-generation
Stars
3.4K
Forks
411
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by letta-ai on GitHub. Read the source before you install it.

Installation

Install the Image Generation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/letta-ai/letta-code.git /tmp/letta-code
mkdir -p .claude/skills
cp -r /tmp/letta-code/src/skills/builtin/image-generation .claude/skills/image-generation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Image Generation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Image Generation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Image Generation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Image Generation

Generate images via Letta's hosted endpoint POST /v1/images/generations. The API usually returns base64 image bytes, but some providers return signed image URLs; save either form to a local image file before replying.

Example

Generate the image, save it locally, then show it inline:

bash
base_url="${LETTA_BASE_URL%/}"

curl -sS -X POST "$base_url/v1/images/generations" \
  -H "Authorization: Bearer $LETTA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"provider":"gemini","prompt":"a friendly robot mascot waving, flat vector logo, mint green background","n":1}' \
  > image-response.json

python3 - <<'PY'
import base64, json, urllib.request

with open("image-response.json") as f:
    response = json.load(f)

image = response["images"][0]
if image.get("b64_json"):
    data = base64.b64decode(image["b64_json"])
else:
    data = urllib.request.urlopen(image["url"]).read()

with open("robot-mascot.png", "wb") as f:
    f.write(data)

print("saved robot-mascot.png")
PY

In Bash tools launched by Letta Code, use the runtime-provided LETTA_BASE_URL and LETTA_API_KEY together for Letta API calls. Build URLs relative to ${LETTA_BASE_URL%/} and send Authorization: Bearer $LETTA_API_KEY. Do not hardcode https://api.letta.com: Desktop and remote runtimes may provide a proxy base URL, and the credential may only be valid through that URL. If either variable is missing, the user needs to authenticate with Letta Cloud (or provide a Letta API key); do not ask for an OpenAI/Gemini provider key. This endpoint also does not use /connect BYOK providers — the only provider values supported here are flux, gemini, and openai.

Then show the image to the user by embedding the saved file in your reply:

markdown
Here's the mascot:

![a friendly robot mascot waving, flat vector logo](./robot-mascot.png)

The Letta Code UI renders local file paths in markdown image tags, so the image appears inline. Always display generated images this way — don't just report the path, and never paste the raw base64 / a data: URI. The markdown path must match where you saved the file. For n > 1, save each image to its own file and embed each on its own line. Keep credit amounts and billing metadata out of user-facing replies and captions unless the user asks about cost. When asked, read billing.credits_charged from the saved response.

Request body

FieldTypeNotes
provider"flux" | "gemini" | "openai"Required.
promptstringRequired, 1–32000 chars.
modelstringOptional; defaults per provider (below).
nint 1–4Optional, default 1. Request variations in one call.
sizestringOptional, e.g. "1024x1024" (OpenAI).
qualitylow|medium|high|autoOptional (OpenAI; higher = more credits).
output_formatpng|jpeg|webpOptional (OpenAI).
input_imagesstring[] (max 14)Optional. Base64 data URLs for edit/remix.
seedintOptional.
ProviderDefault modelUse for
fluxflux-2-proDefault for normal text-to-image. High-quality general image generation; commonly returns signed URLs.
geminigemini-3-pro-imageStrong prompt adherence, image editing/remix.
openaigpt-image-2Photoreal output, explicit size/quality/output_format.

Default to flux for normal text-to-image requests. Use gemini when the user provides input images or wants image editing/remix. Use openai when the user wants photoreal output or a specific size/quality.

Response

json
{
  "provider": "gemini",
  "model": "gemini-3-pro-image",
  "images": [{ "b64_json": "<base64>", "mime_type": "image/png" }],
  "billing": { "credits_charged": 12, "...": "..." }
}

Each images[] entry has either b64_json or url, plus mime_type. Gemini always returns b64_json. Flux commonly returns a signed url; download it to your local image file immediately because signed URLs expire. If OpenAI returns a url, download that URL instead of base64-decoding.

Editing / remixing images

Pass source images in input_images as base64 data URLs (data:<mime>;base64,<data>) and describe the edit in prompt. Gemini handles multi-image edits well. To build a data URL from a local file:

bash
DATA_URL="data:image/png;base64,$(base64 < input.png | tr -d '\n')"

Notes

  • Billing: every success charges credits; don't loop needlessly.
  • Errors: 402 = insufficient credits (credits_required in body); 400/500 return { "message": "..." } — surface it to the user.
  • Only flux, gemini, and openai are supported here.

Frequently asked questions

What does the Image Generation AI skill do?

Generate images from text prompts (and optionally edit/remix input images). Use when the user asks to create, generate, draw, render, or edit an image, illustration, logo, icon, diagram, or photo.

Why use Image Generation on TypingMind?

Because you install it once and use it with any model. Image Generation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Image Generation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/letta-ai/letta-code/tree/main/src/skills/builtin/image-generation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Image Generation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Image Generation?

As many as you like. As long as a model supports skills, you can use Image Generation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Image Generation AI skill free?

Yes. It is published on GitHub by letta-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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