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Dfam Check

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earthtojake
dfam-check

Measure mesh files against Design for Additive Manufacturing (DfAM) rules and report printability findings per process (FDM, SLS, SLA/DLP, metal PBF, MJF). Use when the user asks whether a part is printable, wants overhang/wall-thickness/support analysis of an `.stl`, `.obj`, `.ply`, or `.3mf` mesh, wants a build-orientation recommendation, or wants DfAM redesign guidance before slicing with `$gcode` or regenerating geometry with `$cad`.

Overview

Publisherearthtojake
Repositorytext-to-cad
Skill namedfam-check
Stars
16K
Forks
1.7K
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Dfam Check 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.
  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/earthtojake/text-to-cad.git /tmp/text-to-cad
mkdir -p .claude/skills
cp -r /tmp/text-to-cad/skills/dfam-check .claude/skills/dfam-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dfam Check 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 Dfam Check 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 Dfam Check 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.

DfAM Check

Provenance: maintained in earthtojake/text-to-cad. Use the installed local skill files as the runtime source of truth; the repository link is only for provenance and release review.

Use this skill to produce conservative, evidence-backed DfAM reports for mesh files before slicing or printing. It measures geometry facts locally and compares them against per-process design limits; it never slices, uploads, or starts print jobs.

Geometry Inspection

Use scripts/dfam_tool.py in the active project Python environment for all geometry facts (install requirements.txt first — every run needs it). The tool is fact-only: it reports measurements and never emits pass/fail or readiness statuses. Comparisons and verdicts belong to this workflow. Do not estimate wall thickness, overhang angles, or support volume by eye or from renders when the tool can measure them.

bash
python scripts/dfam_tool.py measure part.stl --angle-limit 45
python scripts/dfam_tool.py orientations part.stl --angle-limit 45

Set --angle-limit to the target process's self-supporting angle from references/process-limits.md before measuring, and re-run when the target process changes: the aggregate support-area facts are binned against it.

STEP/STP input is boundary-representation CAD, not a mesh. When the $cad skill is installed, export an STL sidecar with it first, then measure the STL here. Report that remediation instead of attempting raw STEP parsing. measure on a STEP exits 1 with {"error": "failed to load mesh: ..."}; that is the wrong-input signal, not a missing dependency — do not install extra mesh loaders to work around it.

A fact family that cannot compute returns {"error": ...} in its place rather than costing the report its other measurements — wall_thickness does this when the dependency set is incomplete, support_volume on geometry with no convex hull. That report is PARTIAL: it carries "partial": true, names the families in partial_sections, and the command exits 2 (0 is a complete report, 1 a mesh that would not load at all). Treat every such object as an unmeasured fact (❓ need more info), never as a measurement of zero, and reinstall requirements.txt before comparing wall limits.

Workflow

  1. Collect print intent: target process, material, layer height, and any machine or material datasheet the user can provide. If the process is unknown, measure once with the default 45° limit, then present findings per candidate process rather than guessing a single verdict.
  2. Read references/process-limits.md and select the limit column for the target process. A user-provided machine/material datasheet overrides the defaults; cite whichever source is used for every comparison.
  3. Run measure on the exact upload file. Do not inspect only a generator script, source CAD model, or console summary of the file.
  4. Run orientations when the process requires supports and the measured support area is nonzero. Report any candidate that materially reduces support area, with its build-height tradeoff.
  5. Compare each measured fact to the cited limit and report findings with restrained status labels:
    • ✅ pass: the measured fact satisfies the cited limit.
    • ❌ fail: a measured fact directly violates the cited limit.
    • ❓ need more info: missing process context, unmeasured geometry, sampling too sparse to trust, or tool limitations.
  6. Order findings by severity: watertightness first (blocks slicing for every process), then wall thickness, then overhangs/supports, then orientation and cost signals.

Comparison

Compare only trustworthy pairs of evidence.

  • Cite the limit source (process-limits table row, or the user's datasheet field) and the measured fact (JSON field path) for every finding.
  • Treat p05_mm below the wall-thickness limit as a violation even when min_mm alone could be a sampling outlier; report both values.
  • On an assembly, wall_thickness reports body_count and a per_body breakdown. Attribute a violation to the body it belongs to; a thin figure pooled across bodies is not a finding against the part as a whole.
  • Do not apply support-angle findings to powder processes (SLS, MJF); the relevant powder-process check is trapped-volume powder escape, which this tool does not yet measure — report that as ❓ need more info when enclosed cavities are likely.
  • Do not silently rescale geometry. scale.units_suspect is measured from the bounding-box diagonal: when it is true, the source is probably in meters or inches, every down-facing face reads as resting on the plate, and overhang and support figures of 0.0 mean nothing. Report a unit/scale finding and ask the user to confirm units before comparing anything against a material limit.
  • Support-volume ratios are coarse upper bounds; report them as cost signals, not hard failures, unless the user has set an explicit budget.

Redesign Handoff

For every ❌ fail, include a concrete, plain-language redesign instruction with target numbers (for example "thicken the wall at [12.4, 3.0, 8.1] from 0.6 mm to ≥1.2 mm" or "chamfer the overhang at [23.3, 10.0, 52.0] to ≥45°"). When the $cad skill is installed, offer to apply the redesign instructions with it and re-measure the regenerated geometry here, repeating until no ❌ fail findings remain. When $cad-viewer is installed, hand the measured file path(s) to it so the user can inspect the findings visually.

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 Dfam Check AI skill do?

Measure mesh files against Design for Additive Manufacturing (DfAM) rules and report printability findings per process (FDM, SLS, SLA/DLP, metal PBF, MJF). Use when the user asks whether a part is printable, wants overhang/wall-thickness/support analysis of an `.stl`, `.obj`, `.ply`, or `.3mf` mesh, wants a build-orientation recommendation, or wants DfAM redesign guidance before slicing with `$gcode` or regenerating geometry with `$cad`.

Why use Dfam Check on TypingMind?

Because you install it once and use it with any model. Dfam Check 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 Dfam Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/earthtojake/text-to-cad/tree/main/skills/dfam-check. 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 Dfam Check?

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 Dfam Check?

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

Is the Dfam Check AI skill free?

Yes. It is published on GitHub by earthtojake 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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