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source-part-segmentation

source-part-segmentation is a skill for Claude Code from RobLe3/cc-blender-skill. It costs 58 tokens per session (809 once invoked), scanned A, original, MIT.

A method for separating touching or overlapping parts in an image into named masks. These masks record which visual pieces are structural, decorative, facial, or background elements.

In plain words
What is it for?
Use it to separate parts in logos, mascots, wireframes, decals, and texture atlases before creating contours, fitting textures, or repairing feature positions.
Why use it?
It prevents connected parts from being merged or split incorrectly before building a 3D model. Unclear overlaps are recorded instead of hidden.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

not rated 118repo +17 changed 10d ago A scan Socket: passSnyk: pass 58 tokens original MIT

Good fit Use it to separate parts in logos, mascots, wireframes, decals, and texture atlases before creating contours, fitting textures, or repairing feature positions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/roble3/cc-blender-skill/source-part-segmentation
View source ↗ RobLe3/cc-blender-skill
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 RobLe3/cc-blender-skill --skill source-part-segmentation
Clone the repo
git clone --depth 1 https://github.com/RobLe3/cc-blender-skill

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/roble3/cc-blender-skill/source-part-segmentation/github.svg)](https://agentmods.dev/skills/roble3/cc-blender-skill/source-part-segmentation)
Your own site
<a href="https://agentmods.dev/skills/roble3/cc-blender-skill/source-part-segmentation"><img src="https://agentmods.dev/badge/skills/roble3/cc-blender-skill/source-part-segmentation/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.

agentmods 80×15 button for source-part-segmentation

Your own site · 80×15
<a href="https://agentmods.dev/skills/roble3/cc-blender-skill/source-part-segmentation"><img src="https://agentmods.dev/badge/skills/roble3/cc-blender-skill/source-part-segmentation.svg" alt="Reviewed on agentmods" width="80" 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 809 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. Third-party audits
  • Socket pass 15 Sept 2026
  • Snyk pass 15 Sept 2026
How audits are shown
Origin original No closer match found 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 $0.00809
Opus 5.5 $0.00023 $0.00324
Sonnet 5.5 $0.00012 $0.00162
Haiku 4.5 $0.00006 $0.00081

Measured 10d ago against content hash ec45b5d5723e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 10d 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

Copies of this mod

1 near-identical copy found in the catalogue:

plugin/skills/source-part-segmentation/SKILL.md · 69 lines

How it starts

The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MCP backend

Use when: Overlapping part extraction, component masks, marker/watershed segmentation, contour hierarchy analysis, atlas part isolation, manual seed fallback for source-locked Blender reconstruction.

Before the first Blender call, read ../text-to-blender/references/mcp-backends.md. Prefer official MCP for shared capabilities; preserve the selected backend for the whole task. Tool names in recipes are client/legacy examples: map them through that reference and its agent-host guide.

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.

Read the full file on GitHub · 69 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. 10d ago Changed · +5 lines ec45b5d5723e
  2. 1mo 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 RobLe3/cc-blender-skill (118 stars, last pushed 10d ago), licensed MIT. It adds 58 tokens to every session and 809 once invoked, about $0.0002 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 2026-08-30.

3d-modelingClaude CodeCreative and mediaDesign and UXai-3dblenderblender-mcpblender-python
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