# Img2threejs

> Turn an object or character reference image into a quality-gated, animation-ready procedural Three.js model built in code. Use for image-to-3D reconstruction, detail-accurate object rebuilds, stylized or likeness-maximized characters, sculpt specifications, staged code generation, and multi-view visual correction.

- **Type:** Skill
- **Install:** `agentstack add skill-frabcd-codex-ai-game-studio-img2threejs`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [frabcd](https://agentstack.voostack.com/s/frabcd)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [frabcd](https://github.com/frabcd)
- **Source:** https://github.com/frabcd/codex-ai-game-studio/tree/main/plugins/ai-game-studio-img2threejs/skills/img2threejs
- **Website:** https://frabcd.github.io/codex-ai-game-studio/

## Install

```sh
agentstack add skill-frabcd-codex-ai-game-studio-img2threejs
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# img2threejs — Image to procedural Three.js

Rebuild the object visible in a reference image as a **code-only** procedural Three.js model,
gated by a staged sculpting pipeline and an AI-vision self-correction loop. This is
reconstruction-by-code, **not** photogrammetry, mesh extraction, or downloaded art packs.

Use Codex-native image reading, an already-enabled browser tool, the project preview, or a
user-supplied screenshot wherever this workflow calls for agent vision or a browser.

## Runtime and paths

Resolve `SKILL_ROOT` to the directory containing this `SKILL.md`. When Codex exposes
`PLUGIN_ROOT`, this skill is at `${PLUGIN_ROOT}/skills/img2threejs`; otherwise use the skill's
discovered absolute path. Run every bundled script by absolute path from `SKILL_ROOT` and keep
generated specifications, renders, and models in the user's project or an approved output
directory, never in the installed plugin.

Use the active Python 3.10+ executable: `py -3` or `python` on Windows, and `python3` or the
configured environment's interpreter on macOS. Pass arguments as an array and do not build a shell
command. In command examples, `` means that selected executable and `` means
the resolved absolute `SKILL_ROOT`. The harness itself uses only the standard library.

The local specification index never writes into the installed plugin. Use
`--cache-root ` or `AI_GAME_STUDIO_CACHE_DIR`; otherwise a
bounded OS user-cache directory is used. Generated specifications, code,
renders, and evidence still belong in the approved project/output directory.

The built-in pixel tools decode compatible PNG directly. Common JPEG/TIFF/BMP
inputs may be converted through the fixed native Windows System.Drawing or
macOS `/usr/bin/sips` route. A non-native ImageMagick fallback is allowed only
when `AI_GAME_STUDIO_IMAGEMAGICK_MANIFEST` points to a confirmed JSON record
containing its absolute executable, SHA-256, version, reviewed license, HTTPS
source URL, and plan digest. Unsupported formats must stop with a conversion
request; never run an executable selected only from `PATH`.

This plugin vendors upstream v1.4.3 under Apache-2.0. Read
[UPSTREAM.json](../../UPSTREAM.json) and [LICENSE](../../LICENSE) before redistributing changes.

## Core Promise

Sculpt from a photo, in order — never one-shot a mesh:
1. **Run ` "/forge/next.py" ` first.** It reports the current unlocked pass, exact next command, and unmet acceptance criteria.
2. **Validate** the image is a suitable 3D target (`grimoire/intake/validation_rubric.md`).
3. **Assess** object class + complexity, then write a `qualityContract` before any code.
3. **Spec** it: component hierarchy, materials, lighting, pivots, sockets, action anchors.
4. **Build pass-by-pass** from blockout → structure → form → material → lighting → interaction → optimization.
5. **Verify** each pass with a screenshot compared against the reference; fail a pass if an identity-defining feature is wrong even when the global score looks fine.

State explicitly when output is approximate/stylized/low-poly. A single image cannot reveal
hidden sides or guarantee exact geometry — say so instead of faking confidence.

## Transparency and Process Debugging (Critical — from Bowie Knife reconstruction)

**The problem:** When the user cannot tell what was done or where something went wrong, they cannot debug the process. Over-claiming (reporting success when features still don't match) destroys trust and makes iterative improvement impossible.

**Rule:** Be transparent + don't over-claim. State exactly what changed each pass, with evidence, and name what still doesn't match:
- After each pass, explicitly list what changed: "Updated guard shape to extend left edge from -0.56 to -0.48 for handle overlap"
- Provide evidence: reference the specific values, coordinates, or parameters that changed
- Name what still doesn't match: "Handle silhouette traced but still flat plane (no Z palm-swell), procedural crosshatch not reference's exact dot-grid knurl"
- Explain why a change was made: "Extended guard left edge because handle ends at X=-0.42 and guard ended at X=-0.20, causing visual gap"
- Never claim a feature is "done" when it's only "improved" — use precise language
- When a gate passes but visual inspection shows issues, explain the limitation: "2D gate passed (fidelity 0.83) but three-quarter render shows blade reads as toy (no grind wedge) — 2D gates are blind to 3D realism"

**The user needs to be able to debug the process, not just the output.** If something is wrong, they should be able to trace which decision led to the error and correct it. Opaque processes force restarts; transparent processes enable refinement.

## Required Inputs

- one image path / screenshot / URL / attached image (if missing or unreadable, ask)
- intended use: prop, game object, hero render, playable/destructible object, animation rig
  (default: real-time browser prop with interactive performance)
- for a CS2 request, an authoritative classification record (family/subtype and evidence refs) or
  an explicit request for the user/vision provider to supply one; heuristic detection alone is not
  enough to select a geometry adapter

Download a URL to an approved local input path only through a separately
reviewed network step. The forge accepts local image files; it does not treat an
arbitrary URL as permission to fetch or upload content.

## The Loop (scripts do enforcement; agent vision does judgment)

Run scripts through their absolute paths under `/forge/...`. Pure Python 3.10+ stdlib,
no pip installs.
Full flags: `grimoire/scripts.md`. Never let a script *score* visuals — that is the agent's job.

1. **Analyze the image first** (agent vision, before any script): work the layered observation
   protocol in `grimoire/intake/image_analysis.md` — identify/classify, decompose macro→meso→micro,
   map part relationships, name materials in PBR terms, list identity-defining features, and flag
   what the single view hides. Observation before inference; controlled 3D vocabulary; 3D
   object-space not 2D image-space. This is generic for any subject and feeds every field below.
   Then probe local images: `forge/stage1_intake/probe_image.py ` (metadata only, not a visual check).
1a. **Local Spec Search** — after image analysis and before writing or refining a spec, local
    evidence is a pipeline stage, not an optional memory lookup, whenever the request needs
    domain-specific anatomy, PBR, wear, geometry, runtime, or physics specifications. The pre-spec
    command automatically runs BM25, chooses `cs2` for CS2 targets and `core_3d` otherwise, and
    writes a `localSpecSearch` evidence bundle into the assessment:
    ` "/forge/stage2_spec/new_pre_spec_assessment.py" "Name" --image  --out assessment.json`.
    Add observed terms with repeatable `--spec-query ""`; use `--collection ` only
    when the automatic collection choice is insufficient. `new_sculpt_spec.py --assessment` carries
    that bundle into the final spec, including snippets, `source_refs`, and `evidence_refs`.
    For extra focused retrieval, the direct CLI remains available:
    ` "/forge/stage1_intake/search_specs.py" "" --collection  --limit 3 --snippet-chars 250 --json`.
    For CS2, include English/Vietnamese variants, for example `--spec-query "safety ring vòng ngón"`
    or `search_specs.py "roughness độ nhám" --collection cs2`. Expand queries with object names,
    component names, material/finish terms, behavior terms, and bilingual aliases; retry focused
    alternatives when the first result is incomplete. Build the spec from returned evidence and do
    not invent domain specs when local evidence exists. Search caches are local/generated only;
    preserve JSONL records and source provenance rather than replacing them with cache output.
1b. **CS2 intake manifest** — for a CS2 request, create and validate `cs2-intake.json` before
    pre-spec authoring. Run admission and probing for every source view, record the heuristic signal
    as non-authoritative evidence, attach the classification record, resolve the supported family,
    and choose `route` independently from `exactnessTier`. Missing classification, insufficient
    coverage, or a contradictory high-confidence class is `request-input`; unsupported families do
    not continue into spec generation.
2. **Pre-Spec Assessment Gate** — classify + score complexity + write the quality contract:
   `forge/stage2_spec/new_pre_spec_assessment.py "Name" --image  --complexity  --out assessment.json`. Rules: `grimoire/intake/quality_contract.md`.
   Set `objectClass.primaryDomain` (`object` | `character` | `hybrid`) and fill the seeded
   `detailInventory` (its `targetMinDetails` scales with complexity). **Supported CS2 knife
   skins**: always pass `--cs2`, which defaults the complexity tier to `ultra-complex`
   (`targetMinDetails` 16) — the finish/wear/hardware is the item, so CS2 is held to the top
   fidelity bar; `targetMinDetails` never drops below the 9 floor even if downgraded by hand.
   **Author procedural GEOMETRY (blade/guard/grip profiles) but make the FINISH a de-lit
   reference-crop PROJECTION, not a procedural finish material** — projecting the photo's own
   pixels is what reaches reference fidelity for patterned skins (Doppler/Gamma/Marble/Fade), and
   is what the v1.3 baseline demos do; a procedural finish for a patterned skin reads visibly wrong
   against the reference. Take the projection path in step 2c (it generalizes from characters to
   any reference-matched surface). Procedural finish is the fallback ONLY when live view-dependent
   response matters more than matching this one reference. Finish routes + rulebook:
   `grimoire/build/cs2_finishes.md`; optional exact-texture acquisition:
   `grimoire/intake/cs2_texture_acquisition.md`.
2b. **Detail inventory** (do not skip for detailed subjects) — scan zones and enumerate every
   identity-defining small detail (gloss, bevel, fasteners, linework, contours, stains):
   `forge/stage1_intake/build_detail_inventory.py  --mode grid-3x3 --out-dir  --out di.json`.
   Each detail MUST map to a `component.localFeatures` or `material.localOverrides` entry — never
   prose only. Taxonomy + 3D-term recipes: `grimoire/intake/detail_inventory.md`.
2c. **Projection-first fidelity (characters AND reference-matched surfaces — supported CS2 knife skins, decals,
   painted patterns)** — when the goal is matching a specific reference's surface, put the photo's
   own pixels on the mesh instead of approximating them procedurally. This is the single biggest
   fidelity lever; a procedural material for a patterned surface is the #1 reconstruction failure.
   Recipe (`grimoire/character/likeness_maximization.md` — its two levers, align-mesh+camera and
   project-the-photo, generalize past characters): solve the camera
   (`stage1_intake/solve_camera_pose.py` → `referenceCamera`), **de-light** the reference so it is
   free of baked lighting (`stage1_intake/delight_albedo.py`, hard requirement — this is what makes
   projection safe, not the flat-lit icon), then project the de-lit crop onto the mesh and bake it
   into UVs (`stage3_build/bake_projected_texture.py --mesh-id `). For a CS2 skin the mesh is the
   procedural blade/guard/grip you author in the spec, and the projected de-lit crop IS the finish
   (front + back from the two views) — no procedural Doppler material. For characters, first capture
   landmarks (`stage1_intake/extract_landmarks.py --out anatomy.json`), fill `preSpecAssessment.anatomy`,
   route `grimoire/character/reconstruction.md`. A single view cannot show hidden sides — report
   per-region confidence and request more views when it matters.
3. Author the spec from the assessment:
   `forge/stage2_spec/new_sculpt_spec.py "Name" --image  --assessment assessment.json --manifest cs2-intake.json --out object-sculpt-spec.json`.
   Replace generic starter `featureReviewTargets` with the object's real identity-defining
   systems (≤5 critical, ≤3 important per pass); for characters add `anatomy-proportion`,
   `face-landmark-placement`, `pose-silhouette`, `outfit-and-palette`. Use 3D-graphics terms only
   (`grimoire/glossary/3d_vocabulary.md`), never "nice/smooth/shiny". Classify every component's
   `topologyClass`/`topologyRationale` per `grimoire/intake/surface_topology.md` before picking a
   `primitive` — this is what prevents a continuous organic form from being picked as a box.
4. When material fidelity matters and a source image exists, analyze each material's **finish** then
   extract reference PBR evidence, both per crop (crop the correct region — verify the crop is on the
   part you think it is):
   - `forge/stage1_intake/analyze_texture.py  --spec spec.json --material-id  --in-place`
     classifies the finish (`gem-metal | gemstone | painted-metal | worn-composite | brushed-steel |
     plastic`), extracts the gradient palette, and writes doc-grounded MeshPhysicalMaterial scalars
     (metalness/roughness/clearcoat/transmission/ior/anisotropy/envMapIntensity) onto the material.
     Recipes + Three.js texture/PBR rules (colorSpace, CanvasTexture/DataTexture, height→normal) live
     in `grimoire/build/threejs_texture_reference.md`. Rule of thumb: **solid albedo for flat paint,
     real reference crop for patterned finishes** (doppler/quartz/hydro-dip/camo).
   - `forge/stage1_intake/extract_pbr_evidence.py  --out-dir  --material-id  --target-threshold 0.7`.
   Confidence `
   `forge/stage3_build/generate_threejs_factory.py object-sculpt-spec.json --out src/createObjectModel.ts`
   (generator is pass-gated: a future `--pass-id` fails until prior passes are reviewed `continue`).
7. Render the current pass in a browser/preview, capture a screenshot at a review viewpoint.
8. Package one side-by-side sheet, then inspect it with agent vision:
   `forge/stage4_review/make_comparison_sheet.py --reference  --render  --out cmp.png --json`.
9. Record the review (overall + per-layer + per-feature scores + decision):
    `forge/stage4_review/append_review.py object-sculpt-spec.json --pass-id  --fidelity  --action  --summary "..." --render-screenshot  --comparison-image cmp.png --ai-vision-score  --layer-scores-json '{...}' --feature-reviews-json  --in-place`.
   For the CS2 knife path, also attach the versioned report with
   `--cs2-review-json cs2-review.json --review-scene-json forge/tests/fixtures/knife_review_scene.json`.
   A failed family, painted-region, projection-coverage, critical-detail, or orbit gate blocks
   `continue` even when the global score passes. See `docs/cs2/review-gates.md`.
10. Sync pipeline state after manual review edits:
    `forge/stage3_build/orchestrate_passes.py sync object-sculpt-spec.json --in-place`.

## CS2 image-matched rule

For a CS2 item, the target is observable agreement between the supplied image and the rendered
item: silhouette, proportions, edge profile, hardware layout, coating colour, pattern placement,
wear, roughness response, and camera framing. Every decision must be traceable to evidence or be
labelled as an approximation.

The initial CS2 family boundary is **knife only**. Pistol, rifle, SMG, sniper, heavy, glove, and
unknown knife subtypes must stop with `unsupported-family` or `unsupported-subtype`; they must not
receive the knife component tree as a generic fallback.

### Layer contract

Pass these records between layers. Do not copy an informal vision description into the next stage:

| Layer | Owns | Must emit | Must not decide alone |
| --- | --- | --- | --- |
| Intake | view validity and technical evidence | role, path/hash, resolution, coverage, duplicate status, admission verdict | item identity from aspect ratio or filename |
| Classification | semantic identity | family, subtype, confidence, evidence refs, provider/version, timeout state | geometry or finish parameters |
| Identity | skin/name/paint metadata | precedence, resolved values, ambiguity candidates, proven

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [frabcd](https://github.com/frabcd)
- **Source:** [frabcd/codex-ai-game-studio](https://github.com/frabcd/codex-ai-game-studio)
- **License:** MIT
- **Homepage:** https://frabcd.github.io/codex-ai-game-studio/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-frabcd-codex-ai-game-studio-img2threejs
- Seller: https://agentstack.voostack.com/s/frabcd
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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