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Forgecad Grade Model

Organization
ForgeCAD
forgecad-grade-model

Grade a ForgeCAD or CAD-as-code model against a requirement, brief, prompt, reference, or acceptance criteria with evidence and a 0-10 score.

Overview

PublisherForgeCAD
Repositoryforgecad-public-kit
Skill nameforgecad-grade-model
Stars
942
Forks
104
Bundled files
1
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.

  • 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 ForgeCAD on GitHub. Read the source before you install it.

Installation

Install the Forgecad Grade Model 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/ForgeCAD/forgecad-public-kit/tree/mainline/skills/forgecad-grade-model
  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/ForgeCAD/forgecad-public-kit.git /tmp/forgecad-public-kit
mkdir -p .claude/skills
cp -r /tmp/forgecad-public-kit/skills/forgecad-grade-model .claude/skills/forgecad-grade-model
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Forgecad Grade Model 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 Forgecad Grade Model 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 Forgecad Grade Model 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.

Grade Model

Grade the delivered model against the requirement, not against what could be fixed later. Never edit the model unless the user explicitly requests repairs — then record the baseline grade first, change, and re-grade.

Workflow

  1. Extract the requirement into a checklist, must-haves separate from nice-to-haves. If the brief is vague, grade the most reasonable literal interpretation and mark unverifiable items Unknown.
  2. Run the model: forgecad run <model>.forge.js (in the ForgeCAD repo use the local build, node dist-cli/forgecad.js run ...). If it fails to execute, stop and apply the caps.
  3. Render at least iso, front, right, top to a scratch dir; add views (back, bottom, close-up, section) when the model is asymmetric, hollow, mechanical, or likely to hide mistakes.
  4. Open and look at every PNG — never score from command output alone. Check silhouette, proportions, required features, part boundaries, interfaces, and whether the model reads as the requested artifact from more than one angle.
  5. Inspect whenever hidden internals, fit, wall thickness, or assembly behavior are central to the brief — grading without inspecting them caps the score. Delegate evidence choice, commands, and manifest reading to the forgecad-inspect-model skill. Findings (unexpected collisions, thin regions, floating bodies, wrong component counts) are evidence, not warnings to wave away.
  6. Score: fill the rubric, apply caps, give a final 0-10 in whole or .5 increments. Unknowns count against the score.

Rubric

DimensionPointsWhat To Look For
Requirement fit4Satisfies the stated must-haves, captures the intended object/function, no drift into a generic substitute.
Geometric completeness2Correct silhouette, proportions, visible details, part boundaries, internal structure when required, no missing major components.
Mechanical/manufacturing plausibility2Believable materials/process, real interfaces, clear load paths, fasteners/seats/clearances where needed, no impossible assembly.
Validation health1Runs cleanly, renders from multiple views, targeted inspections reveal no major unaddressed issues.
Code/model quality1Parametric clarity, readable organization, meaningful names, no debug junk, appropriate ForgeCAD APIs.

Anchors: 9-10 = evidence-backed match for the brief; 7-8 = recognizable and mostly complete, fixable gaps; 5-6 = main idea only; below 5 = broken or loosely related.

Evidence Caps

Maximum scores, applied after the rubric:

  • Model does not execute: max 2.
  • Executes but cannot be rendered: max 4.
  • No rendered images visually inspected: max 5.
  • Only one flattering view inspected: max 6.
  • A must-have requirement is absent: max 6.
  • Visually recognizable but physically impossible for the requested use: max 6.
  • Internals, fit, walls, or assembly central to the brief but uninspected: max 7.
  • Multi-part assembly violates the ForgeCAD component model — sibling imports, assembly-space coordinates inside parts, structural translate() placement, missing connector mates, or parent/child data flow confusion: max 7; max 6 when the violation makes the assembly brittle or mechanically wrong.
  • Inspection finds unexpected collisions, floating bodies, critical thin walls, or wrong connectivity: max 6; max 5 when the defect invalidates the main function.
  • Delivered as a finished product/prototype but presented with default flat lighting (no scene() rig), a generically colorful or material-false palette, or teaching-diagram styling: max 8. Does not apply when the brief asks for a blockout or bare technical study.
  • Any score relying on an assumption the evidence cannot verify: mark it Unknown, never score above 8.

Report Format

markdown
Score: N / 10

Requirement checklist:
| Item | Result | Evidence |
| --- | --- | --- |
| ... | Pass/Partial/Fail/Unknown | render/inspection/file evidence |

Evidence reviewed: run outcome; views inspected; inspection highlights.
Why this score: decisive strengths and defects.
Caps applied: list, or `None`.
Next fixes: the 2-5 highest-leverage improvements.

Grading Rules

  • A beautiful render with missing functional geometry is not a high score.
  • Grade the default returned model unless the user names a parameter set or variant.
  • No points for comments, labels, or intentions absent from the geometry.
  • Decorative screws, floating labels, and teaching-diagram callouts are not real mechanical interfaces.
  • For multi-part assemblies, require the component model: parts build locally at origin, expose connectors/metadata, the parent positions them, and inter-part data flows through parent props and returned metadata.
  • Cite which render or inspection finding drove the grade.
  • When comparing models, use identical checklist, cameras, inspection evidence, and caps for all.

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 Forgecad Grade Model AI skill do?

Grade a ForgeCAD or CAD-as-code model against a requirement, brief, prompt, reference, or acceptance criteria with evidence and a 0-10 score.

Why use Forgecad Grade Model on TypingMind?

Because you install it once and use it with any model. Forgecad Grade Model 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 Forgecad Grade Model in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ForgeCAD/forgecad-public-kit/tree/mainline/skills/forgecad-grade-model. 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 Forgecad Grade Model?

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 Forgecad Grade Model?

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

Is the Forgecad Grade Model AI skill free?

Yes. It is published on GitHub by ForgeCAD 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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