nib

name	nib
description	Turns an idea or an article into original white-background, hand-drawn editorial illustrations starring a recurring avatar the user owns — one caught scene per idea, in one of several print looks. Use when the user asks to illustrate a post/article/concept with a consistent character, or invokes "nib". Not for generic draw/make-an-image requests.
version	0.1.1
argument-hint	[idea or article path/URL] | build a character | set avatar <image> | style <name> | explainer
license	MIT
metadata	{"category":"creative","requires":{"bins":["python3"]}}

Nib

Make original editorial illustrations for written content. One image explains one idea. A recurring avatar — supplied once by the user — is the subject of every scene and performs the idea (pushes, sorts, steers, builds, holds); it is never decoration. The methodology is the constant; the avatar and the look are the parameters.

This is a configurable house style, not a generic image generator. It is intentionally not a photo, not a logo, not a corporate infographic, not a UI mockup.

Setup (once)

1. A character. The user's own avatar image (a mascot, logo character, anything — save its path); a bundled one via --avatar-pack <name> (see characters/); or build a new one with the user (references/character-builder.md). The character is the reference on every generation.
2. A backend — one of:
   • Codex (free) — a logged-in Codex CLI (codex login). Generates on the user's ChatGPT / Codex subscription, no API key. Default when no key is set.
   • OpenRouter — an OPENROUTER_API_KEY (sk-or-…). Exact 16:9, and lets you pick the model with --model.

Workflow

1. Read the input.

   • A URL → fetch the page and extract the readable article text first (use your web-fetch / Defuddle capability), then treat it as an article.
   • A single idea → one illustration.
   • An article / post (or the user's own draft / notes they want illustrated) → pick the load-bearing moments (a judgment, a flow, a before/after, a trap, a loop), 4–8 of them. Not one image per paragraph — the ones that matter. Treat this set as a starting point the user shapes: show it first, let them drop moments they don't want, add their own, or ask for more candidates, then illustrate only the chosen ones. (The desktop app's Article mode exposes the same controls — per-shot select, add-a-shot, "More ideas", and a parallel "Generate".)

2. Pick a look for the piece — one of 13 (see references/styles.md): marker, riso, blueprint, woodcut, pixel, clay, gouache, chalk, diorama, enamel, felt, manila, phosphor. One look per piece.

3. For each idea, invent a fresh, concrete physical metaphor in which the avatar performs the idea (see references/methodology.md). One caught scene is the default; switch to the explainer register — a hand-drawn sketch-diagram — only when the user asks ("diagram the flow") or the idea is itself a traceable structure (references/composition.md, --register explainer).

4. Generate by calling the engine once per image:

   python3 scripts/generate.py \
     --idea "trust is built one piece of evidence at a time" \
     --style marker \
     --avatar /path/to/avatar.png \
     --out ./out/trust.png
   

   It builds the full prompt (methodology + look + idea), sends the character as the reference for consistency, and writes a 16:9 PNG. Useful flags:

   • --backend auto|openrouter|codex — auto uses the OpenRouter key if set, else the free Codex lane.
   • --model <id> — OpenRouter image model (default google/gemini-3.1-flash-image-preview).
   • --avatar-pack <name> — use a bundled character (see characters/) instead of --avatar.
   • --avatar-spec "<text>" — a written description of the character (silhouette, face, the one accent part) that locks its design. Combine with the image — it markedly improves consistency.
   • --accent "<hex|name>" — pin the one accent colour (a brand hex / site colour); see references/palettes.md.
   • --register explainer — draw a hand-built sketch-diagram instead of a single caught scene (references/composition.md).
   • --doctor — print backend readiness (which lane will run, and why) and exit.

5. Review against references/quality-bar.md (white background, one idea, avatar performing the action, short labels only, not a slide). Regenerate any that miss.

6. Report each saved path.

Example prompts

Invoke Nib from your agent like:

• Use nib to illustrate: "one fish, many dishes"
• Use nib to make illustrations for this article: https://jamesclear.com/feedback-loops
• Use nib to build a character with me, then illustrate "the information well"
• Use nib to diagram the flow of a RAG pipeline (explainer)
• Use nib to turn this post into a 5-image set in chalk: <paste a URL or text>

Notes

• Keep text in the image short; the model can misspell long labels.
• The avatar is the reference on every call — that is what keeps the character consistent across a whole article.
• For options, generate 2–3 variants of a shot (run the engine a few times with the same idea) and let the user pick.
• Cost: free on the Codex lane (uses the user's ChatGPT / Codex subscription), or a few cents per image on OpenRouter (google/gemini-3.1-flash-image-preview) — a full post is well under a dollar either way. Run --doctor to see which lane is ready.
• A written --avatar-spec (describe the character in words) markedly improves consistency over the image reference alone — use it whenever you have a description.
• Inspired by xiaohei (小黑) by helloianneo — this methodology is an adaptation of its hand-drawn editorial approach, reworded in Nib's own voice.