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Poster generator

Skill nanoodlecom/noodle-skills/skills/poster-generator

Ready-to-install agent skills, each wrapping a nanoodle visual AI workflow — poster, photo restyle, jingle, narrated poem, video teaser

Install
npx -y skills add nanoodlecom/noodle-skills --skill poster-generator

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What its author says it does

Copied from the file, not written here

Generate a poster image from a short idea — an LLM expands the idea into a detailed image prompt, then an image model renders it. Use when the user asks for a poster, illustration, or promo image from a one-line concept.

SKILL.md

2.4 KB, as published. Nobody here has run it

Poster generator

Runs the bundled nanoodle workflow workflows/poster.noodle-graph.json against the NanoGPT API: a text input (Idea) feeds an LLM that writes a vivid image prompt, which feeds an image model that renders the poster (Poster). Requires Node.js >= 20 and the nanoodle npm package (npx nanoodle fetches it).

API key

The run needs a NanoGPT API key. Use whichever is available:

  • NANOGPT_API_KEY already set in the environment — prefer this; no extra flags.
  • A .env file containing NANOGPT_API_KEY=... — pass --env-file <path> only when the key is not already in the environment. (With this CLI, --env-file overrides ambient NANOGPT_API_KEY.)

Never print the key.

Run

From this skill's directory (or prefix paths if running from elsewhere). Replace the Idea value with the user's concept:

npx nanoodle run workflows/poster.noodle-graph.json \
  --input "Idea=a cozy ramen shop on a rainy night" \
  --out ./poster-out

Add --env-file .env only when the key is not already exported. For a machine-readable payload (paths, cost, balance), add --json.

Optional style override (the workflow also exposes this input):

--input "System prompt=<custom image-prompt writer instructions>"

Inspect the interface anytime with:

npx nanoodle inspect workflows/poster.noodle-graph.json

Inputs

KeyRequiredWhat to pass
IdeayesThe poster concept, one line is enough
System promptnoCustom instructions for the prompt-writing LLM

Outputs

  • Media is saved under --out as Poster.<ext> where <ext> follows the image MIME (often jpg or png). Use the path the CLI prints on the Poster: line — do not hard-code .png.
  • With --json, the path is in outputs.Poster (and cost/balance in the same object).
  • The CLI also prints total cost (and remaining balance when the API reports it) to stderr.

Cost

Each run costs about $0.04 in NanoGPT credit (the image step, nano-banana-2-lite, dominates; the LLM prompt-writing step is a fraction of a cent). Runs spend real credit — tell the user before running repeatedly.

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