Multiview Fit Loop

Q: Why use Multiview Fit Loop on TypingMind?

Because you install it once and use it with any model. Multiview Fit Loop 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.

Q: How do I install Multiview Fit Loop in TypingMind?

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

Q: Which AI models can use Multiview Fit Loop?

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.

Q: How many AI models can I use with Multiview Fit Loop?

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

Q: Is the Multiview Fit Loop 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.

Q: 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.

Q: 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.

Community

RobLe3

multiview-fit-loop

Closed-loop compare-adjust-repeat workflow for fitting Blender models to supplied front/side/back/top templates and originals. Use when the user asks to compare the product to templates/originals and adjust until it fits across all dimensions, or when all views must pass measurable bbox/centroid/silhouette/edge validation before export.

Overview

Publisher	RobLe3
Repository	`cc-blender-skill`
Skill name	`multiview-fit-loop`
Stars	131
Forks	14
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

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.
1 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 Multiview Fit Loop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/RobLe3/cc-blender-skill/tree/main/plugin/skills/multiview-fit-loop
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.

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/multiview-fit-loop .claude/skills/multiview-fit-loop

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multiview Fit Loop 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 Multiview Fit Loop 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 Multiview Fit Loop 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-view validation and iterative fitting against reference templates, all-dimension mascot/object reconstruction, front/side/back/top overlay reports, fit deltas, automated adjustment loops.

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.

Multiview Fit Loop

This skill closes the missing loop: render → compare → adjust → render again. It is mandatory when a user says the model still does not fit the templates/originals.

Required loop

Render flat, material-independent silhouettes for every available template view: front, side, back, top.
Extract the template object mask, excluding labels, cyan guides, and background.
Compare template vs render per view:
- bbox center and size
- centroid drift
- silhouette coverage/IoU where modality is valid
- visual overlay
Convert measured deltas into model/camera/recipe adjustments.
Rebuild or transform the model.
Repeat until all hard gates pass or document the remaining conflict.

Constraint inconsistency gate

Before forcing adjustments, check whether the supplied orthographic templates are mutually consistent. A single rigid 3D model cannot simultaneously satisfy contradictory physical ratios, for example if side view says total depth is 0.39 of height but top view says depth is 0.98 of width. When this occurs, stop claiming final fit, write a conflict report, and create separate variants or ask which view is canonical.

Hard gates

all required views have validation reports and overlays;
bbox center drift <= 1.5% of image width;
bbox size drift <= 3% for front, <= 5% for side/top/back first-pass depth;
structural part count exact;
texture UV regions still valid after geometry changes.

View mask rule

For annotated wireframes, choose a template mask mode that isolates the intended construction/object lines and excludes guide colors, labels, captions, and background annotations. For Blender validation renders, use a flat white silhouette on black, not beauty renders with glow/context elements.

Scripts

scripts/multiview_fit_report.py compares view pairs and writes JSON + overlays.

Adjustment rule

Prefer changing recipe parameters or source geometry over camera scale tricks. Camera scale may be used only after model dimensions are correct.

Bundled files

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

scripts/multiview_fit_report.py5 KB

Frequently asked questions

What does the Multiview Fit Loop AI skill do?

Why use Multiview Fit Loop on TypingMind?

Because you install it once and use it with any model. Multiview Fit Loop 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 Multiview Fit Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RobLe3/cc-blender-skill/tree/main/plugin/skills/multiview-fit-loop. 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 Multiview Fit Loop?

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 Multiview Fit Loop?

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

Is the Multiview Fit Loop 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.

View all

Plan Canvas

affaan-m

CommunityPopular

Open plans and HTML artifacts in a local browser canvas where the human annotates elements, chats, and approves or requests changes without leaving the page. Use when presenting a plan for review, or…

Improve Codebase Architecture

mattpocock

CommunityPopular

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.

Grilling

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

Teach

Teach the user a new skill or concept, within this workspace.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

MCP backend

Multiview Fit Loop

Required loop

Constraint inconsistency gate

Hard gates

View mask rule

Scripts

Adjustment rule

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now