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Blender Skill Harmonizer

Community
RobLe3
blender-skill-harmonizer

Harmonize multiple Blender skills into a coherent pipeline with clear activation precedence, handoff artifacts, dependency gates, and conflict-resolution rules. Use when a Blender task spans several skills, when reference-locked work conflicts with generic production workflow, after adding/updating skills, or when repeated failures indicate skill interference or missing inter/intra-play.

Overview

PublisherRobLe3
Repositorycc-blender-skill
Skill nameblender-skill-harmonizer
Stars
131
Forks
14
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by RobLe3 on GitHub. Read the source before you install it.

Installation

Install the Blender Skill Harmonizer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/RobLe3/cc-blender-skill/tree/main/plugin/skills/blender-skill-harmonizer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/RobLe3/cc-blender-skill.git /tmp/cc-blender-skill
mkdir -p .claude/skills
cp -r /tmp/cc-blender-skill/plugin/skills/blender-skill-harmonizer .claude/skills/blender-skill-harmonizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Blender Skill Harmonizer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Blender Skill Harmonizer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Blender Skill Harmonizer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

MCP backend

Use when: Multi-skill Blender orchestration, skill graph audits, resolving overlaps between text-to-blender/pro-workflow/reference-to-3d/wireframe/UV/fit/repair skills, defining source-of-truth precedence, artifact contracts, and sequential-vs-parallel execution plans.

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.

Blender Skill Harmonizer

This is the meta-layer for the Blender skill stack. It prevents independently useful skills from producing incoherent work when combined.

Use a Merge → Consistency → Optimize → Store cycle:

  1. Merge: list all triggered skills and their intended outputs.
  2. Consistency: detect conflicts in assumptions, coordinate systems, source hierarchy, object naming, or validation gates.
  3. Optimize: choose one orchestrator, one source-of-truth policy, and a staged/parallel execution plan.
  4. Store: write the chosen plan and durable lessons into project docs/memory.

Skill category map

  • Top-level orchestrators: text-to-blender, quality-refinement-autoloop, mascot-logo-reconstruction.
  • General production: blender-pro-workflow, blender-modeling, blender-materials, blender-lighting, blender-cameras, blender-rendering, blender-animation, blender-export.
  • Reference reconstruction: reference-to-3d, wireframe-to-3d, reference-analysis-validator, source-part-segmentation, orthographic-registration, multiview-constraint-solver, contour-to-mesh, texture-driven-mesh-fitting, landmark-fit-repair, multiview-fit-loop, fit-repair-optimizer.
  • Texture/UV: blender-uv-texturing, atlas-uv-fitting, closed-surface-uv-coverage.
  • Look calibration: reference-look-calibration.
  • Animation/motion design: texture-state-animation, orbital-hud-motion, animation-quality-gate, coordinated by blender-animation.
  • Task-specific/full workflow: mascot-logo-reconstruction.

Activation precedence

For a task involving references/templates/textures:

  1. blender-skill-harmonizer — choose the pipeline and conflict policy. 1a. quality-refinement-autoloop — if output is rejected/subpar, freeze product work, diagnose, sanitize/patch generic skill knowledge, validate, then retry.
  2. reference-analysis-validator — source manifest and source-of-truth classification.
  3. orthographic-registration — view consistency and coordinate contract.
  4. multiview-constraint-solver — rigid feasibility and canonical view policy.
  5. fit-repair-optimizer — dependency repair queue if validation fails.
  6. source-part-segmentation — split overlapping structural/decorative masks.
  7. contour-to-mesh / wireframe-to-3d / blender-modeling — geometry, selected by source type.
  8. texture-driven-mesh-fitting + landmark-fit-repair — fit mesh boundaries and named landmarks to source/texture.
  9. atlas-uv-fitting / blender-uv-texturing / closed-surface-uv-coverage — UV, texture fit, and full front/back/side surface coverage.
  10. multiview-fit-loop — render/compare/adjust loop.
  11. reference-look-calibration + blender-materials / blender-lighting / blender-rendering — look only after geometry/UV gates.
  12. blender-export — only after validation gates.
  13. blender-animation — only after static fit acceptance.
  14. texture-state-animation / orbital-hud-motion — design texture/HUD motion as layered, source-derived animation.
  15. animation-quality-gate — render contact sheet and reject bad motion before final export.

Generic blender-pro-workflow is subordinate to the reference-locked order whenever the source is a template/brand asset.

Shared artifact contract

Every non-trivial multi-skill pipeline should keep these files in the output folder:

  • reference_manifest.json
  • registration_report.json
  • atlas_regions.json
  • validation/multiview_fit_report.json
  • validation/*_overlay.png
  • ALIGNMENT_REPAIR_QUEUE.json or equivalent when gates fail
  • BUILD_NOTES.md

Conflict rules

  • Source conflict beats implementation. If templates disagree, stop claiming final fit and write a conflict report.
  • Front-view brand read beats side/back/top unless canonical policy says otherwise.
  • Reference-locked geometry beats generic modeling. Do not primitive-first rebuild after a contour/source mask exists.
  • UV/texture fit waits for geometry lock. Texture audit may run in parallel, but final UV assignment waits.
  • Lighting/look waits for material and camera lock. Do not tune lights to compensate for wrong geometry.
  • Export waits for validation. No final GLB without pass/fail reports.

Sequential vs parallel orchestration

Sequential gates:

  1. source conflict / multiview rigidity
  2. structural part count
  3. front geometry
  4. multiview geometry
  5. UV/material texture fit
  6. lighting/look
  7. export/animation

Parallel lanes allowed after their blockers clear:

  • atlas region classification can run while geometry is being repaired;
  • lighting reference statistics can run while UVs are being prepared;
  • export target checks can run while visual validation is pending;
  • validation tooling improvements can run anytime, but must not mutate product geometry.

Read when needed

  • references/handoff-contracts.md for exact input/output contracts between skills.

Script

  • scripts/skill_graph_audit.py audits the local manifest, roles, missing paths, and overlap warnings.

Animation handoff rule

For reference-locked mascots/logos, animation is not a generic spin/pulse task. Use texture-state-animation for material/texture state changes, orbital-hud-motion for circles/HUD/aura, and animation-quality-gate before accepting or exporting. If an animation is rejected as ugly, run a RALPH loop before rebuilding.

Closed-surface coverage handoff rule

For closed or extruded reference-locked assets, front texture fit is not enough. Before look-dev or animation acceptance, run closed-surface-uv-coverage to verify that front cap, back cap, and sidewall surfaces each have explicit UV/generated/procedural coverage. Curves, planes, HUD, and aura elements are accents only and do not count as surface texture fill.

Quality-refinement autoloop handoff rule

When a user rejects output quality, or repeated failures show missing skill depth, do not continue blind retries. Invoke quality-refinement-autoloop: preserve the baseline, capture evidence, classify failure dimension, decide whether existing skills are sufficient, sanitize any new lesson into generic skill guidance, validate the skill stack, then repair the artifact. Publication prep is only done on explicit user request.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Blender Skill Harmonizer AI skill do?

Harmonize multiple Blender skills into a coherent pipeline with clear activation precedence, handoff artifacts, dependency gates, and conflict-resolution rules. Use when a Blender task spans several skills, when reference-locked work conflicts with generic production workflow, after adding/updating skills, or when repeated failures indicate skill interference or missing inter/intra-play.

Why use Blender Skill Harmonizer on TypingMind?

Because you install it once and use it with any model. Blender Skill Harmonizer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Blender Skill Harmonizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RobLe3/cc-blender-skill/tree/main/plugin/skills/blender-skill-harmonizer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Blender Skill Harmonizer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Blender Skill Harmonizer?

As many as you like. As long as a model supports skills, you can use Blender Skill Harmonizer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Blender Skill Harmonizer AI skill free?

Yes. It is published on GitHub by RobLe3 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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