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Dfm

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earthtojake
dfm

Design-for-manufacturing review of a part for sheet metal, CNC machining, or injection molding - bends, reliefs and flat patterns; machining access, internal corners, deep features and setups; draft, undercuts and projected area. Use when the user asks whether a part can be bent, machined or molded, asks about manufacturability or tooling, or asks for a DFM review or redesign.

Overview

Publisherearthtojake
Repositorytext-to-cad
Skill namedfm
Stars
16.5K
Forks
1.7K
Bundled files
6
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.

  • 6 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 Dfm 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/earthtojake/text-to-cad/tree/main/skills/dfm
  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/dfm .claude/skills/dfm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dfm 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 Dfm 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 Dfm 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.

DFM review

Provenance: maintained in earthtojake/text-to-cad. Use the installed local skill files as the runtime source of truth.

Produce a process-specific DFM review of the supplied design. This is a guided review skill, not an automatic feature-recognition or manufacturing certification engine. Report unavailable checks explicitly.

Pick the process, then read its reference. One of them, or more than one when the user is comparing:

ProcessReferenceMeasurement
Sheet metalreferences/sheet-metal.mdnone; use CAD inspection or supplied dimensions
CNC machining / turningreferences/cnc.mdnone; use CAD inspection or supplied dimensions
Injection moldingreferences/injection-molding.mdscripts/mold_tool.py measures draft, undercuts and projected area

Each reference carries that process's review checklist, its two fallback sources, and a worked reasoning example. The rules below apply to all three.

For additive manufacturing, use $dfam-check, which measures mesh printability per process.

Evidence first

Prefer the user's actual supplier/tooling specification over general guidance. Record conflicting specifications rather than silently choosing.

Identify the reviewed file and revision, units, and bodies. Prefer exact STEP/B-rep measurements for radii and analytic faces. If only a mesh is available, record its resolution and approximation limits. A screenshot supports a suspected issue, not a measured pass/fail. Source-code parameters describe design intent; verify that they match the artifact being reviewed before treating them as evidence.

When $cad is available, use its documented inspection workflow for geometry facts. If it cannot measure a required feature, use supplied dimensions with provenance or mark the check unverified; do not invent commands or measurements. Never infer alloy, resin, strength, or stock thickness from a rendering material or color.

Rule selection

  1. Use the selected shop's specification for the actual material and process.
  2. Where none is supplied, use the two references named in the process file as a starting point. Neither establishes the capabilities of an arbitrary machine, tool or fixture.
  3. Record URL/document version, access date, section, units, and the applicable material/tooling conditions with every adopted limit. If the source cannot be checked, report the missing rule rather than manufacture a default.

Do not turn a supplier's recommendation into a physical law or its machine capacity into a universal process limit. Preserve uncertainty and distinguish measured geometry from planned manufacturing decisions.

Report and redesign

Return a concise Markdown report in chat or the user's requested report file:

  • Scope: artifact/revision, parts, process, material, units, tooling assumptions.
  • Findings: part/feature, evidence and measurement method, applicable rule and source, result, and a concrete suggested change.
  • Coverage: checks performed and checks not measured, including what is needed to resolve them. No findings is not a blanket manufacturability approval.
Part / featureEvidenceApplicable ruleResultSuggested action
Named feature + locationMeasured value, units, artifact revision, methodSource section + threshold + conditionspass / fail / review / unverifiedSpecific change or missing evidence

Use pass only for a measured feature satisfying a cited applicable limit; fail for a measured violation; review for a qualitative risk; and unverified when evidence or process context is missing. Include units, measurement uncertainty, and source section/table in numerical comparisons. If uncertainty straddles the threshold, leave the check unverified. Keep cost suggestions separate from manufacturing constraints. If only a render is provided, list visible concerns as review items and request geometry or dimensions for the required measurements. Never fill a report with invented feature IDs or sample values.

For requested redesign, preserve the original artifact and use $cad if available to modify the source, regenerate, and recheck the new artifact. Check affected neighboring features as well as the original finding. Without editing tools, provide a specific change list. A review request alone does not request edits, uploads, ordering, or machine operation.

Geometry measurement (injection molding only)

scripts/mold_tool.py is the one measurement script here; the other two processes have no geometry analyzer. Install requirements.txt first — only these measurements need it.

bash
python scripts/mold_tool.py measure part.stl --pull z
python scripts/mold_tool.py pulls part.stl

The tool is fact-only: it reports measurements and never emits pass/fail. Comparisons against resin, texture and tooling limits belong to the review. references/injection-molding.md documents what each fact family means and how to read it.

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 Dfm AI skill do?

Design-for-manufacturing review of a part for sheet metal, CNC machining, or injection molding - bends, reliefs and flat patterns; machining access, internal corners, deep features and setups; draft, undercuts and projected area. Use when the user asks whether a part can be bent, machined or molded, asks about manufacturability or tooling, or asks for a DFM review or redesign.

Why use Dfm on TypingMind?

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

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

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 Dfm?

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

Is the Dfm 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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