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reference-analysis-validator

reference-analysis-validator

github.com/roble3Creative & media128882 installsUpdated 2w ago
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About reference-analysis-validator

reference-analysis-validator is a creative skill for turning visual references into measurable modeling and export checks. It defines source manifests, image analyses, overlays, and threshold-based gates for brand, logo, and mascot work.

Key features

  1. 01Classifies reference sources and view roles
  2. 02Compiles manifests with expected parts and thresholds
  3. 03Extracts masks, components, and landmarks
  4. 04Compares reference and render masks
  5. 05Blocks export when hard gates fail

Use cases

  1. 01Validate a logo model against reference views
  2. 02Check mascot silhouette and landmarks
  3. 03Track repeated mismatches to a specialty skill

Install

Copy the command and run it in a terminal.

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

After installing, type/reference-analysis-validatorin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
metrics-and-thresholds.md
scripts/
reference_manifest_compiler.py
render_overlay_validator.py

Details

From the project README

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.

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