CD meshy-openclaw
Create 3D models, textures, reference images, rigging and animation with Meshy, and prepare models for physical 3D printing: white or multicolor prints, Creative Lab products, sizing and slicer handoff. Drives the Meshy CLI; signs in through the browser, no API key required.
Create 3D models, textures, reference images, rigging and animation with Meshy, and prepare models for physical 3D printing: white or multicolor prints…
As a process D 36/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 36/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 6 mutating operations with no state check
- 40Consistency. Frontmatter name (meshy-openclaw) differs from the folder (meshy-3d-agent)
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 85Steps. 8 steps, 2 vague phrases
- 100Execution cost. Instruction body is 1653 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- +4No input/output examples
- +2Single-language instructions
- +3Description length 275: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 8 items
- +4Reference files are cited in the instructions (5 of 5)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.