Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add devkindhq/ideogram-ai-toolkit --skill world-buildergit clone --depth 1 https://github.com/devkindhq/ideogram-ai-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/devkindhq/ideogram-ai-toolkit/world-builder)<a href="https://agentmods.dev/skills/devkindhq/ideogram-ai-toolkit/world-builder"><img src="https://agentmods.dev/badge/skills/devkindhq/ideogram-ai-toolkit/world-builder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/devkindhq/ideogram-ai-toolkit/world-builder"><img src="https://agentmods.dev/badge/skills/devkindhq/ideogram-ai-toolkit/world-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00298 | $0.02729 |
| Opus 5.5 | $0.00119 | $0.01092 |
| Sonnet 5.5 | $0.00060 | $0.00546 |
| Haiku 4.5 | $0.00030 | $0.00273 |
Grade A, and why
world-builder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
World Builder
A trained custom model locks one character's likeness. It does not, by itself, tell you what that character's world looks like — the palette its packaging uses, whether it has a village, what its landmarks are, whether "family" scenes read as a community or as a crowd of clones. This skill is the staged pipeline for answering that, developed over several real sessions building out the world around PorchPing's mascot ("Ding-Bot") and generalized here so it applies to any already-trained character.
The whole pipeline is sequenced around one constraint: every image that uses the
custom model spends training-specific budget and risks compounding whatever quirks the
model has (see references/character-batch-discipline.md), so cheaper no-model passes
come first, cheaper batches come before expensive ones, and nothing gets called "done"
without a human visually reviewing it.
Prerequisite — do not start without this
A custom model must already exist for the character, trained via the
custom-model-training skill (dataset → train_model → get_model polling →
custom_model_uri). If the user hasn't mentioned a custom_model_uri, ask for it or
run mcp__ideogram__list_models to find it before doing anything else. Don't start
generating "world" images against a character description alone — the entire point of
this pipeline is testing how a trained model behaves across many contexts, not
describing the character freshly in each prompt (which character-model-sheet already
does, and which drifts exactly the way custom-model-training's existence is meant to
prevent).
Cross-cutting discipline — applies to every step below, no exceptions
Read these two reference files once, before step 1, and re-apply them on every single prompt for the rest of the pipeline:
references/palette-lock.md— the locked palette (paper/dominant, primary, secondary, ink/trim, and at most one reserved accent used in exactly one place) must be defined and quoted, verbatim, in every prompt from step 1 onward. A world built on a drifting palette isn't a world, it's six unrelated images.references/anti-slop-discipline.md— the reusable, brand-agnostic ban list (glowing orbs, neural-network nodes, circuit-board textures, gradient washes, stock-photo people, glossy mirror-shine, plastic-toy uncanny valley, and the rest). Run the pre-generation gate in this file before everygenerate_image/generate_images_bulkcall, the same disciplinecharacter-model-sheetandbrand-identity-sheetalready apply to their own single-image gates, scaled up to a multi-batch pipeline.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 185 lines · 298 tokens per session scan A 7187d0e3a34b
world-builder is a skill published in the GitHub repository devkindhq/ideogram-ai-toolkit (2 stars, last pushed 6d ago), licensed Apache-2.0. It adds 298 tokens to every session and 2,729 once invoked, about $0.0012 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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