source-part-segmentation

source-part-segmentation is a skill for Claude Code from CheshireJCat/blender. It costs 58 tokens per session (734 once invoked), scanned A, a copy of source-part-segmentation, MIT.

A process for separating touching or overlapping visual parts into individual masks before rebuilding a mascot, logo, or template in 3D.

In plain words
What is it for?
Creating named masks for structural, decorative, facial, background, and validation parts from images, wireframes, texture sheets, or decals.
Why use it?
It prevents nearby parts from being merged or split incorrectly, which can make later geometry reconstruction inaccurate.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Creating named masks for structural, decorative, facial, background, and validation parts from images, wireframes, texture sheets, or decals.

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Install with agentmods
npx agentmods add skills/cheshirejcat/blender/source-part-segmentation
Install

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.

Any agent
npx skills add CheshireJCat/blender --skill source-part-segmentation
Clone the repo
git clone --depth 1 https://github.com/CheshireJCat/blender

Made for: Claude Code.

Wrote 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.

agentmods badge for source-part-segmentation

README.md
[![agentmods](https://agentmods.dev/badge/skills/cheshirejcat/blender/source-part-segmentation.svg)](https://agentmods.dev/skills/cheshirejcat/blender/source-part-segmentation)
Your own site
<a href="https://agentmods.dev/skills/cheshirejcat/blender/source-part-segmentation"><img src="https://agentmods.dev/badge/skills/cheshirejcat/blender/source-part-segmentation.svg" alt="Measured on agentmods" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 734 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00058 $0.00734
Opus 5 $0.00029 $0.00367
Sonnet 5 $0.00012 $0.00147
Haiku 4.5 $0.00006 $0.00073

Measured 7d ago against content hash 190f635882ed, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

source-part-segmentation 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 7d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/seeded_part_masks.py, scripts/segment_source_parts.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

This is a copy

100% identical to source-part-segmentation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/create-3d-model/references/modules/source-part-segmentation/SKILL.md · 64 lines

How it starts

The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Source Part Segmentation

Use this before contour-to-mesh when a source image contains overlapping or touching designed parts. The output is not “nice masks”; it is a source-of-truth part inventory that downstream geometry must obey.

Inputs

  • source image, wireframe, decal, or texture atlas;
  • optional manual seed manifest with named parts, polygons, seed points, rough rectangles, or HSV/color ranges;
  • source manifest with structural/decorative/context classification and expected part count.

Workflow

  1. Choose the cleanest modality: alpha, edge, dark-line, bright-on-dark, color-band, or atlas region.
  2. Extract contours and hierarchy to identify candidate objects, holes, nested details, and strokes.
  3. If components touch, run distance-transform marker watershed first.
  4. If watershed over/under-splits, switch to seeded segmentation:
    • create named part seeds (bbox, polygon, or seed_point + optional flood/HSV tolerance);
    • save one mask per named structural part;
    • mark ambiguous overlaps explicitly instead of merging them.
  5. Classify masks as structural, decorative, face_feature, aura_context, or validation_only.
  6. Pass structural masks to contour-to-mesh; pass feature masks/landmarks to landmark-fit-repair; pass atlas regions to atlas-uv-fitting.

Hard rules

  • Do not infer repeated parts from symmetry; segment what the source shows.
  • Do not merge overlapping components if the manifest expects separate structural meshes.
  • Do not proceed to final modeling when part count differs between source images; write a conflict report or canonical policy.
  • If automatic segmentation is ambiguous, write an ambiguity report and require or create manual seed rectangles/points.
  • Keep stroke/line masks separate from filled-part masks; wireframe strokes are guides unless explicitly used as the contour boundary.

Seed manifest schema

{
  "schema": "source_part_seed_manifest.v1",
  "image": "path/to/source.png",
  "parts": [
    {"name":"leaf_top", "class":"structural", "bbox":[x,y,w,h], "mode":"non_background"},
    {"name":"face_shell", "class":"structural", "polygon":[[x,y],[x,y],...], "mode":"polygon"}
  ]
}

Read the full file on GitHub · 64 lines

Files

What ships with it

2 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.

Changes

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.

  1. 7d ago First seen · 64 lines · 58 tokens per session scan A 190f635882ed

Subscribe to this mod's changes

source-part-segmentation is a skill published in the GitHub repository CheshireJCat/blender (24 stars, last pushed 18d ago), licensed MIT. It adds 58 tokens to every session and 734 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to source-part-segmentation, differing in 0 lines, and is treated as a copy.

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