Agent skill

Image Generation

Generate images from text prompts (and optionally edit/remix input images).

Apache-2.0Auto-check passedMedia & Creative

Install Image Generation

skills CLI

$ npx skills add letta-ai/letta-code --skill image-generation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install letta-ai/letta-code image-generation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/letta-ai/letta-code.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src/skills/builtin/image-generation .claude/skills/image-generation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "image-generation" agent skill from https://github.com/letta-ai/letta-code/tree/main/src/skills/builtin/image-generation into .claude/skills/image-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "image-generation", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/letta-ai/letta-code/tree/main/src/skills/builtin/image-generation

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add letta-ai/letta-code --skill image-generation -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install letta-ai/letta-code image-generation --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/letta-ai/letta-code.git skills-src && mkdir -p .agents/skills && cp -r skills-src/src/skills/builtin/image-generation .agents/skills/image-generation && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "image-generation" agent skill from https://github.com/letta-ai/letta-code/tree/main/src/skills/builtin/image-generation into .agents/skills/image-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "image-generation", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add letta-ai/letta-code --skill image-generation -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install letta-ai/letta-code image-generation --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/letta-ai/letta-code.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/src/skills/builtin/image-generation .cursor/skills/image-generation && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "image-generation" agent skill from https://github.com/letta-ai/letta-code/tree/main/src/skills/builtin/image-generation into .cursor/skills/image-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "image-generation", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/letta-ai/letta-code.git --path src/skills/builtin/image-generation

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add letta-ai/letta-code --skill image-generation -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install letta-ai/letta-code image-generation --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/letta-ai/letta-code.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/src/skills/builtin/image-generation .gemini/skills/image-generation && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "image-generation" agent skill from https://github.com/letta-ai/letta-code/tree/main/src/skills/builtin/image-generation into .gemini/skills/image-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "image-generation", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install letta-ai/letta-code image-generation

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add letta-ai/letta-code --skill image-generation -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/letta-ai/letta-code.git skills-src && mkdir -p .github/skills && cp -r skills-src/src/skills/builtin/image-generation .github/skills/image-generation && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "image-generation" agent skill from https://github.com/letta-ai/letta-code/tree/main/src/skills/builtin/image-generation into .github/skills/image-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "image-generation", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add letta-ai/letta-code --skill image-generation -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install letta-ai/letta-code image-generation --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/letta-ai/letta-code.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/src/skills/builtin/image-generation .opencode/skills/image-generation && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "image-generation" agent skill from https://github.com/letta-ai/letta-code/tree/main/src/skills/builtin/image-generation into .opencode/skills/image-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "image-generation", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: image-generation
GitHub stars: 3.6k
Token cost: ~1.3k tokens
SKILL.md length: 484 words
Files: 1
Skills in repo: 25
Repo updated: Oct 10, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Generate images from text prompts (and optionally edit/remix input images).

The user asks to create
SKILL.md covers Example, Request body, Response and Editing / remixing images, plus 1 more section
Calls curl and python3; reaches api.letta.com; needs LETTA_API_KEY
Tasks that involve Image generation

What it does

Image Generation is an agent skill from letta-ai/letta-code. 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.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Media & Creative, covering Image generation. It works with Letta and OpenAI. The repository describes itself as: Stateful agents that are like people, with memory, identity, and the ability to learn and adapt. The licence is Apache-2.0.

When your agent uses it

The user asks to create
Tasks that involve Image generation

Example prompts

“/image-generation”

Requirements

Python 3
A credential in LETTA_API_KEY

What it can do on your machine

Read from SKILL.md and the folder at commit d31b879. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- curl
- python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- api.letta.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- LETTA_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Image Generation loads about 1.3k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 484 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~53
When it runs · the whole SKILL.md, loaded when a task matches: ~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from letta-ai/letta-code at commit d31b879, republished under its Apache-2.0 licence (© letta-ai). 484 words, ~1,254 tokens.

Download SKILL.mdSave it as .claude/skills/image-generation/SKILL.md (or your agent's skills folder).

name: image-generation
description: 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.

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.

Show full SKILL.md (235 more words)Show less

Request body

Field	Type	Notes
`provider`	`"flux"` \| `"gemini"` \| `"openai"`	Required.
`prompt`	string	Required, 1–32000 chars.
`model`	string	Optional; defaults per provider (below).
`n`	int 1–4	Optional, default 1. Request variations in one call.
`size`	string	Optional, e.g. `"1024x1024"` (OpenAI).
`quality`	`low`\|`medium`\|`high`\|`auto`	Optional (OpenAI; higher = more credits).
`output_format`	`png`\|`jpeg`\|`webp`	Optional (OpenAI).
`input_images`	string[] (max 14)	Optional. Base64 data URLs for edit/remix.
`seed`	int	Optional.

Provider	Default model	Use for
`flux`	`flux-2-pro`	Default for normal text-to-image. High-quality general image generation; commonly returns signed URLs.
`gemini`	`gemini-3-pro-image`	Strong prompt adherence, image editing/remix.
`openai`	`gpt-image-2`	Photoreal 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.

Files

Just SKILL.md in src/skills/builtin/image-generation of letta-ai/letta-code.

Open the folder on GitHubat commit d31b879

Compare with similar skills

Image Generation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Image Generation compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Image Generation this skillletta-ai/letta-code	3.6k	—	~1.3k	Automated safety check: Pass	Apache-2.0	today
Nano Banana Pro Prompts Recommend SkillYouMind-OpenLab/nano-banana-pro-prompts-recommend-skill	1.9k	1 repos	~4.1k	Automated safety check: Pass	None	today
Yingzaoop7418/guizang-yingzao-skill	496	—	~1.1k	Automated safety check: Pass	None	1 mo ago
AI Image Creatorcentminmod/my-claude-code-setup	2.7k	—	~8.1k	Automated safety check: Notes	MIT	2 days ago
Character Refseternityspring/shuohao-skills	4.3k	—	~1.7k	Automated safety check: Warn	Apache-2.0	2 days ago
Basic Memory Repo Imagesbasicmachines-co/basic-memory	4.1k	—	~2.7k	Automated safety check: Pass	AGPL-3.0	today

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Questions about Image Generation

What does Image Generation do?

Generate images from text prompts (and optionally edit/remix input images). Image Generation is an agent skill from letta-ai/letta-code. Generate images from text prompts (and optionally edit/remix input images).

When should I use Image Generation?

Image Generation fits situations like: the user asks to create; tasks that involve Image generation.

How do I install Image Generation in Claude Code?

Run `npx skills add letta-ai/letta-code --skill image-generation -a claude-code`. Or copy the skill folder (src/skills/builtin/image-generation in letta-ai/letta-code) into .claude/skills/image-generation in your project. Claude Code loads it when a task matches its description.

How do I install Image Generation in Codex?

Run `npx skills add letta-ai/letta-code --skill image-generation -a codex`. Or copy the skill folder (src/skills/builtin/image-generation in letta-ai/letta-code) into .agents/skills/image-generation in your project. Codex loads it when a task matches its description.

Can I use Image Generation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add letta-ai/letta-code --skill image-generation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/image-generation, .gemini/skills/image-generation, .github/skills/image-generation and .opencode/skills/image-generation in your project.

What does Image Generation need to run?

Going by SKILL.md and its folder, Image Generation needs the command-line tools its instructions call (curl and python3) and credentials named LETTA_API_KEY. Our summary lists: Python 3; A credential in LETTA_API_KEY.

Does Image Generation access the network?

SKILL.md names 1 domain. In commands or code: api.letta.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Image Generation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Image Generation use?

Image Generation is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Image Generation use?

About 1.3k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Image Generation?

Skills that share tags, products or a category with Image Generation: Nano Banana Pro Prompts Recommend Skill (YouMind-OpenLab/nano-banana-pro-prompts-recommend-skill, 1.9k stars), Yingzao (op7418/guizang-yingzao-skill, 496 stars), AI Image Creator (centminmod/my-claude-code-setup, 2.7k stars) and Character Refs (eternityspring/shuohao-skills, 4.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Image Generation?

letta-ai (a GitHub organization) maintains it in letta-ai/letta-code, which has 3,562 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on October 10, 2026.

Source: letta-ai/letta-code on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Image Generation

Install Image Generation

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Image Generation

Example

Request body

Response

Editing / remixing images

Notes

Files

Compare with similar skills

Similar skills

Nano Banana Pro Prompts Recommend Skill

Yingzao

AI Image Creator

Character Refs

Basic Memory Repo Images

Chatgpt Image Ad

More from letta-ai/letta-code

Creating Skills

Generating Mod Envs

Initializing Memory

Self Configuration

Browser Use

Creating Mods

Questions about Image Generation

Install Image Generation

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Image Generation

Example

Request body

Response

Editing / remixing images

Notes

Files

Compare with similar skills

Similar skills

More from letta-ai/letta-code

Related

Questions about Image Generation