MCPInstall

reference-analysis-validator

Measure and validate supplied reference images, wireframes, texture atlases, and Blender renders before declaring a reconstruction 1:1. Use when an asset must match a template, when visual feedback says the output is off, when part counts must be exact, or before exporting a brand mascot/logo reconstruction. Pairs with reference-to-3d, contour-to-mesh, orthographic-registration, atlas-uv-fitting, and Blender MCP.

Installs
853
Last 24h
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/roble3/cc-blender-skill --skill reference-analysis-validator
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads reference-analysis-validator automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

MCP backend

Use when: Any 1:1 reference/model validation task involving masks, overlays, part counts, centroids, bounding boxes, SSIM/IoU, source manifests, or fail-before-export gates.

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.

Reference Analysis Validator

This skill converts “looks close” into measurable gates. For brand/logo/mascot work, do not model or export until a source manifest and validation thresholds exist.

Required outputs

Create these in the asset output folder:

  • reference_manifest.json — classified source files, expected parts, thresholds.
  • source_analysis/*.json — image metadata, masks/components/landmarks.
  • validation/front_overlay_reference.png — reference and render overlay.
  • validation/front_mask_validation.json — IoU/SSIM/bbox/centroid report.

Workflow

  1. Classify sources: front, side, back, top, texture atlas, decals, maps, lightmap, aura/context.
  2. Build/refresh reference_manifest.json with hard expected counts and view roles.
  3. Extract masks/components from each source using scripts/reference_manifest_compiler.py or existing analyzers.
  4. Render model from matching orthographic camera with reference planes hidden.
  5. Compare reference mask vs render mask using scripts/render_overlay_validator.py.
  6. Refuse final export if hard gates fail.

Modality rule

Compare like with like. A wireframe edge mask compared against a shaded beauty render gives misleadingly low IoU. For hard gates, render a flat silhouette/matte pass from Blender or compare reference edges to render edges. Use render_overlay_validator.py --reference-mode ... --render-mode ... when the source and render need different mask extraction modes.

Default validation gates

  • primary structural part count: exact.
  • front silhouette IoU: target >= 0.90 for rigid/logotype shapes; >= 0.82 acceptable for first mascot reconstruction pass.
  • bbox center drift: <= 12 px at 1024 px validation size.
  • bbox size drift: <= 3% of image dimension.
  • face/eye/smile landmark drift: <= 2% of image dimension when landmarks are defined.

Failure policy

If a repeated mismatch occurs, record the measured failure, then route to the missing specialty skill:

  • wrong silhouette → contour-to-mesh
  • wrong depth/side/back → orthographic-registration
  • wrong textures → atlas-uv-fitting
  • wrong whole workflow → mascot-logo-reconstruction

Read when needed

  • references/metrics-and-thresholds.md for metric definitions and recommended gates.

Sources distilled

Official/library docs to prefer while extending this skill:

  • OpenCV contour features: moments, area, perimeter, bounding rectangles.
  • OpenCV shape matching / Hu moments.
  • OpenCV homography and geometric transforms.
  • scikit-image SSIM for perceptual comparison.

FAQ

What does the reference-analysis-validator skill do?

Measure and validate supplied reference images, wireframes, texture atlases, and Blender renders before declaring a reconstruction 1:1. Use when an asset must match a template, when visual feedback says the output is off, when part counts must be exact, or before exporting a brand mascot/logo reconstruction. Pairs with reference-to-3d, contour-to-mesh, orthographic-registration, atlas-uv-fitting, and Blender MCP.

How do I install reference-analysis-validator?

Run npx skills add https://github.com/roble3/cc-blender-skill --skill reference-analysis-validator in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/reference-analysis-validator/ (personal) or .claude/skills/reference-analysis-validator/ (project).

Which AI agents can use reference-analysis-validator?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is reference-analysis-validator safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains reference-analysis-validator?

It is published by roble3 in roble3/cc-blender-skill. It has 853 recorded installs.

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