tmchow/tmc-marketplace/image-sprout

image-sprout

Generate and iterate on images with consistent style and subject identity. Image Sprout turns reusable project context — reference images, derived guides, and persistent instructions — into repeatable outputs you can build on across runs.

Use this skill when:

• The user asks to generate images and wants results that stay consistent across multiple runs
• The user wants to iterate on outputs from a previous generation
• The user needs to set up or manage an image project with references or style context
• The workflow requires structured, scriptable image generation with --json output

1. Installation

Install globally from npm:

npm install -g image-sprout

Or run without installing — prefer this in Codex sandbox environments where PATH is unreliable:

npx image-sprout help

All examples below use npx image-sprout. Substitute image-sprout directly if you have a global install.

2. OpenRouter Key Setup

Image Sprout stores its OpenRouter key on disk. Set it once per machine:

npx image-sprout config set apiKey <your-openrouter-key>
npx image-sprout config show    # confirm key is set (does not reveal the raw key)

3. The Project Model

Three context layers drive every generation:

• Visual Style — consistent look and feel across outputs
• Subject Guide — consistent subject identity across outputs
• Instructions — persistent generation constraints (watermarks, framing, branding)

Two reference pools:

• Shared refs — drive both guides (default, simplest)
• Split refs — separate style and subject pools (advanced; use --role style or --role subject when adding)

Understanding this model prevents the most common agent mistake: generating without saved context and wondering why outputs are inconsistent.

4. Core CLI Workflow

# Create a project
npx image-sprout project create <name>
 
# Add references (3+ recommended; more refs = better derivation)
npx image-sprout ref add --project <name> ./ref1.png ./ref2.png ./ref3.png
 
# Optional: persistent instructions
npx image-sprout project update <name> --instructions "Watermark bottom-right: subtle."
 
# Derive guides from refs
npx image-sprout project derive <name> --target both   # or: style, subject
 
# Check readiness before generating
npx image-sprout project status <name> --json
 
# Generate (--count controls images per run: 1, 2, 4, 6; default is 4)
npx image-sprout project generate <name> --prompt "hero in neon rain"
npx image-sprout project generate <name> --prompt "hero in neon rain" --count 1
 
# Inspect results
npx image-sprout run latest --project <name> --json
 
# Delete a session and all its runs/images
npx image-sprout session delete --project <name> <session-id>

Top-level aliases:

npx image-sprout generate --project <name> --prompt "hero in neon rain"   # same as project generate
npx image-sprout analyze --project <name> --target both                    # same as project derive

5. JSON Output — the Agent Pattern

Always use --json for structured output:

npx image-sprout project show <name> --json
npx image-sprout project status <name> --json
npx image-sprout run latest --project <name> --json
npx image-sprout run list --project <name> --json --limit 5

Use --value PATH to pluck a single field:

npx image-sprout run latest --project <name> --json --value images[0].path

This is how agents hand image paths to downstream tools. Run images land in image-sprout's internal app data directory — use run latest --json --value images[0].path to get the path and leave what to do with it to the calling workflow.

6. Parallel-Safe Usage

npx image-sprout project use <name> sets a shared "current project" state on disk. In multi-agent workflows, this state can collide across concurrent processes. Always pass `--project <name>` explicitly — never rely on the current project shortcut.

7. Web UI — Agent Awareness

The web app runs over the same on-disk store as the CLI. Agents won't use it directly, but should surface it to users when interactive review is appropriate.

npx image-sprout web              # launches local app
npx image-sprout web --open       # also opens in default browser
npx image-sprout web --port 8080  # custom port (default: 4310)

Useful for:

• reviewing and comparing generated images visually
• setting up a project interactively before handing off to CLI/agent use
• iterating on outputs via the canvas interface

Security: do not expose the web UI to the public internet. The server has no authentication. Safe options are localhost only, or a private network like Tailscale. The risk is public internet exposure — LAN and tailnet access are fine.

8. Model Management

npx image-sprout model list
npx image-sprout model set-default google/gemini-3.1-flash-image-preview
npx image-sprout model add openai/gpt-5-image
npx image-sprout model restore-defaults

Default generation model is Nano Banana 2 (google/gemini-3.1-flash-image-preview). Custom models must accept image input and produce image output via OpenRouter.

Guide derivation uses a separate configurable analysis model (default: google/gemini-3.1-flash-image-preview):

# Set a persistent analysis model
npx image-sprout config set analysisModel google/gemini-2.5-flash
 
# Override per-derive
npx image-sprout project derive <name> --target both --analysis-model google/gemini-2.5-flash