# World Builder > Builds out a fictional "world" around an already-trained custom character model — a staged, multi-batch pipeline (no-mascot moodboard, paired with/without-model scene tests, society & culture batch, family/village/society deep pass, landmarks/maps/art pass, optional contamination-check redo) that goes from "I have a trained mascot" to "I have a whole visual world this character lives in," using `generate_image`, `generate_images_bulk`, `remix_image`, and `describe_image`. Use whenever the user wants to expand a locked mascot or character (already produced by `character-model-sheet` and trained by `custom-model-training`) into a broader brand world — community scenes, rituals, hierarchy, family/village life, landmarks, maps, art objects — or asks for "world-building," "brand world," "the world Ding-Bot lives in," "expand the universe around this character," or "society/culture pass." Distinct from `character-model-sheet` (locks the one character's own design) and `custom-model-training` (turns that locked desi - Skill: `devkindhq/world-builder` (Agent Skill, multi-file: 8 files) - Install (CLI): `npx skillmds@latest add devkindhq/world-builder` - Raw SKILL.md: https://api.skillmd.com/api/skills/devkindhq/world-builder/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: devkindhq (https://skillmd.com/u/devkindhq) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/devkindhq/world-builder --- # 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 every `generate_image` / `generate_images_bulk` call, the same discipline `character-model-sheet` and `brand-identity-sheet` already apply to their own single-image gates, scaled up to a multi-batch pipeline. And read `references/character-batch-discipline.md` before step 2 (the first step that puts the custom model in front of more than one character) — it covers the character-count clause, the no-characters clause, and the known family-resemblance limitation that governs every batch from here on. ## Workflow ### 1. Moodboard pass — no mascot, establish the visual language Before spending any custom-model budget, generate a pure brand-world moodboard with the character **absent**. Do not pass `custom_model_uri` on this call. Prompt for palette, material, texture, and mood only — no characters, no mascots, no people. This is the step that locks the palette (per `references/palette-lock.md`) and the material/texture language everything downstream has to agree with, before the character is anywhere in frame. Use `mcp__ideogram__generate_image`, `style_type: "DESIGN"`, no custom model. If the project already has a `moodboard-generator` board, this step can extend that board's palette rather than re-deriving it from scratch — but the character-absence rule still applies even when reusing an existing palette. ### 2. Paired with/without family — does the mascot integrate into real usage? For a set of product/UI-style scenes (packaging shot, app screen, storefront signage, whatever the brand's real usage contexts are), generate each scene **twice**, same composition/palette/lighting held identical between the pair: - **With family**: `custom_model_uri` set, mascot present, exact character count stated (see `references/character-batch-discipline.md`). - **Without family**: no `custom_model_uri`, mascot and all characters absent, explicit "no characters, mascots, or people anywhere in frame" clause. The point of the pair is diagnostic, not decorative: it tests whether the mascot reads as belonging in the scene or as pasted on top of it. Review both halves of each pair side by side, not independently — a mascot that looks fine alone but breaks the composition once the "without" half is generated for comparison is a real finding, not a false alarm. ### 3. Society & culture batch — paired with no-model "world artifacts" Two batches, run as a pair the same way step 2 was paired, but now testing civic/cultural depth instead of product usage: - **Character-driven batch** (`custom_model_uri` set): community roles, rituals (graduation, festival), hierarchy/rank lineups. State the exact character count in every prompt — this is the step where crowd-runaway risk is highest, since "hierarchy lineup" and "festival" both invite the model to keep adding figures unless capped explicitly. - **World-artifacts batch** (no `custom_model_uri`, no characters): maps, a landmark, heraldry/crest, currency, textile pattern, a public space. Explicit "no characters or mascots or people anywhere in frame" clause on every prompt in this batch. Submit each batch via `mcp__ideogram__generate_images_bulk` if it's more than a couple of prompts (see `bulk-image-generation-workflow` for the batch-submission pattern this reuses) — but keep the character-driven and world-artifacts batches as two separate `generate_images_bulk` calls, never mixed into one, since they use different `custom_model_uri` settings and that parameter is shared across an entire batch (there is no per-prompt override). ### 4. Family/village/society deep pass — the larger character-driven batch A larger batch (roughly a dozen images, adjust to the world's actual scope) using the custom model throughout, covering: family units, village life, town hall, classroom, elder council, processions, home life, friendships, harbor, park, celebrations. Every prompt states its exact character count and quotes the locked palette. This is the deepest character-driven pass in the pipeline — expect (and explicitly review for, not silently accept) the family-resemblance limitation described in `references/character-batch-discipline.md`, since this batch has the highest character density of any step. Submit via `generate_images_bulk` given the batch size; track every `request_id` per `references/batch-tracking.md`. ### 5. Landmarks, maps & art pass — pure world-building, no characters at all No `custom_model_uri`, no characters anywhere. Landmarks (halls, towers, plazas, libraries, galleries, amphitheaters), a world map, art objects (sculptures, murals, ceramics, stained glass, monuments). Same locked palette, same anti-slop gate, explicit "no characters or mascots or people anywhere in frame" clause on every prompt. This pass exists independently of whether step 3's world-artifacts batch already touched some of the same territory (a map, a landmark) — step 5 goes deeper and wider, treating those as a first pass rather than the final one. ### 6. Contamination-check redo — optional, flag the risk explicitly Optional step, and a real risk, not a formality. If the team wants to know whether it's viable to use the trained custom model *everywhere* (including no-character world-only prompts), rerun a sample of step 1's or step 5's no-model prompts through the custom model and compare. Read `references/contamination-check.md` before doing this. The short version: a model trained on one character's likeness carries a real risk of that character's silhouette bleeding into architecture, object, or map prompts that never asked for a character at all — a tower that's subtly bell-shaped, a crest that echoes the mascot's face, a map border pattern that repeats the character's silhouette. This has to be **visually reviewed image by image**, not assumed safe because the prompt said "no characters." Never report this step as "passed" without someone actually looking at the images side-by-side against the clean no-model versions from steps 1 and 5. ## Tracking discipline — every batch, every step Every batch in every step above gets tracked per `references/batch-tracking.md`: `request_id`/`job_id` for traceability, whether `custom_model_uri` was used (and which one), and an explicit `"visual_review_status": "pending"` until a human actually looks at the results and confirms them. An API call returning 200 is not the same thing as the batch being done — don't mark anything "done" on API success alone. ## Save what you made Per the toolkit's "No Context Lost" habit, write every step's prompts, batch/request IDs, `custom_model_uri` usage, and review status to the project's world-building folder (check for an existing `logo-explorations/`, `branding/`, or `world/` folder first and match it; create `world-building/` under the project folder if none exists). Save incrementally as each step completes — this is a long, multi-session pipeline by design, and nothing generated in any step should live only in the conversation if the session ends mid-pipeline. ## Reference files - `references/palette-lock.md` — how to define and lock the paper/primary/secondary/ ink-trim/accent palette before step 1, and the discipline for quoting it verbatim in every subsequent prompt. - `references/anti-slop-discipline.md` — the reusable, brand-agnostic ban list (not brand-specific) and the pre-generation gate to run before every call in every step. - `references/character-batch-discipline.md` — the exact-character-count clause, the no-characters clause, and the known family-resemblance/near-clone limitation of single-character-trained models — what it is, why it's expected rather than a bug, and how to review for it ("does this read as a species/community, or does it read as broken"). - `references/batch-tracking.md` — the per-batch tracking record (request/job IDs, `custom_model_uri` used or not, visual-review status) to keep on every batch across every step. - `references/contamination-check.md` — the optional step-6 risk check: what "contamination" looks like when a single-character model is run against no-character world prompts, and how to review for it honestly instead of assuming safety.