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Forgecad Reconstruct From Images

Organization
ForgeCAD
forgecad-reconstruct-from-images

Reconstruct a real parametric ForgeCAD object from reference images by using images as evidence, not as a one-view facade.

Overview

PublisherForgeCAD
Repositoryforgecad-public-kit
Skill nameforgecad-reconstruct-from-images
Stars
942
Forks
104
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 ForgeCAD on GitHub. Read the source before you install it.

Installation

Install the Forgecad Reconstruct From Images 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-reconstruct-from-images
  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-reconstruct-from-images .claude/skills/forgecad-reconstruct-from-images
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Forgecad Reconstruct From Images 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 Reconstruct From Images 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 Reconstruct From Images 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.

Reconstruct From Images

The reference image is evidence, not the deliverable. The deliverable is a real parametric object that holds up from front, back, side, top, bottom, and reference camera views — a model that matches one image but falls apart from other angles has failed, even if the comparison board looks close. Cutaway, sectioned, exploded, or transparent references are evidence about the complete object: build the closed artifact and recreate explanatory views with viewer/inspection tools (the main forgecad skill's closed-artifact rule applies).

Companion Skills

  • forgecad — API docs, model authoring, renderer behavior.
  • forgecad-design-spec — when the images underdetermine artifact family, process posture, scale, operating story, or validation boundary.
  • forgecad-build-model — file placement, project structure, decomposition, definition of done.
  • forgecad-inspect-model — pre-delivery inspection for multi-part, internal, mechanical, thin-wall, or fit-sensitive objects.

Core Rule

Infer the real object before matching any camera — identity, manufacture, scale, what hidden sides must contain, what geometry must exist for physical coherence. Reference matching is a validation step after the object exists; never start by chasing pixels or the prettiest view.

Workflow

  1. Stage references in /tmp/<slug>-replicate/refs, keeping originals and adding view names where possible (front, side, rear-iso, top, detail).
  2. Read each image as evidence, recording: visible facts; scale cues; camera cues; unknowns (hidden/occluded geometry); conflicts across images or stylization.
  3. Write a Real Object Brief — a hard gate before modeling: (a) artifact identity + operating story; (b) assumed scale and units; (c) process posture + part/BOM boundary (real geometry vs purchased vs ghost vs omitted); (d) inferred hidden-side geometry + expected canonical front/back/left/right/top/bottom forms; (e) validation views and inspection evidence. Use forgecad-design-spec when these are underdetermined.
  4. Build a coarse 3D blockout — model the object, not the image: large volumes, axes, symmetry, side depth, rear form, underside, hidden continuations. Render canonical views before any reference-camera comparison. Follow forgecad-build-model for project structure.
  5. Calibrate one camera per usable reference, only after the blockout makes sense from canonical views. Use the object center as target; estimate azimuth/elevation/distance/FOV from visible faces and perspective cues; use orthographic when parallel edges stay parallel with no perspective convergence.
  6. Render comparison boards: render the model from each calibrated reference camera and place it next to the original. Never compare from memory.
  7. Iterate one class of change at a time, in order: object hypothesis → major proportions → canonical geometry → camera → details → presentation. If improving one reference view makes another view or a canonical render worse, the object hypothesis is wrong — fix the model, not the camera illusion.
  8. Use every image as a constraint. Never pick one target image and ignore the rest: assign each image a camera, evidence list, and confidence; optimize one shared geometry against the whole set; state how distorted or decorative images were weighted.
  9. Validate the final object: forgecad run, reference comparison boards, canonical renders, and targeted inspections via forgecad-inspect-model.

Comparison Boards

Render with exact --camera specs (see the forgecad CLI doc for supported forms). If exact full camera specs do not render, fix the renderer before continuing — never substitute guesses from default iso renders.

Build side-by-side boards with the bundled self-contained uv helper (installs Pillow on demand). Resolve scripts/compare_images.py relative to the installed forgecad-reconstruct-from-images skill directory:

bash
uv run <skill-dir>/scripts/compare_images.py refs/front.png render-front.png compare-front.png

Use --fit contain (default); use --fit cover only when both images already share the same crop and aspect. Run with --help for other options.

Done and Report

Done means: a written Real Object Brief; real parametric geometry (not a billboard, facade, or one-view shell) that makes sense from all canonical views; honest hidden-side assumptions where images are silent; passes forgecad run; comparison boards plus canonical renders exist. The result fails if it only works from the original camera — one render is never enough; expect several render/compare/inspect iterations.

Report: model path; Real Object Brief summary + assumptions; per-reference camera spec, weighting, and board path; canonical render paths; inspection evidence; remaining mismatches or downgraded confidence.

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 Reconstruct From Images AI skill do?

Reconstruct a real parametric ForgeCAD object from reference images by using images as evidence, not as a one-view facade.

Why use Forgecad Reconstruct From Images on TypingMind?

Because you install it once and use it with any model. Forgecad Reconstruct From Images 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 Reconstruct From Images in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ForgeCAD/forgecad-public-kit/tree/mainline/skills/forgecad-reconstruct-from-images. 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 Reconstruct From Images?

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 Reconstruct From Images?

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

Is the Forgecad Reconstruct From Images 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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