SKILLEMALL.ai
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BC cad

Creates and edits parametric CAD parts and assemblies as Python build123d models that cadgen's engine builds and the CAD Viewer shows live; exports STEP, STL, 3MF, GLB and xbf files on request; resolves prompt references, measures geometry, and articulates or animates assemblies. Use for 3D CAD modeling, editing a model script (any change to a part's or assembly's `.py`), organizing a CAD project, exporting or converting CAD files, Quick Edit prompts, mechanisms, and explode or showcase animations. Opens and reviews existing XBF, STEP/STP, STL, 3MF and GLB files in the CAD Viewer.

earthtojake/text-to-cad Agent Skills author: earthtojake MIT 13 files body ≈ 5 151 tokens Open the sourcegithub.com↗ analyzed 4 h ago

Creates and edits parametric CAD parts and assemblies as Python build123d models that cadgen's engine builds and the CAD Viewer shows live; exports STEP, STL…

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5151 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 54/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5151 tokens
  • 100Steps. 19 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (4 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • +2Single-language instructions
  • +3Description length 587: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 19 items
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (10 of 10)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.