SKILLEMALL.ai
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BC bambu-studio-ai

From chat to finished print — the first full-pipeline AI 3D printing skill. Single-color (STL) and multi-color AMS (OBJ+MTL) with AI-optimized color pipeline: shadow removal, CIELAB nearest-neighbor color mapping, texture smoothing. Auto-orient, auto-scale, 11-point printability analysis, mesh repair. All 9 Bambu Lab printers. 4 AI 3D generation providers.

modbender/skill-library-mcp Agent Skills author: modbender MIT 19 files body ≈ 7 253 tokens Open the sourcegithub.com↗ analyzed 3 d ago

From chat to finished print — the first full-pipeline AI 3D printing skill.

As a process C 63/100 · Has gaps — weak spots: when it triggers, inputs and preconditions

ProcedureGitHubInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
61
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. 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: 19. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 7253 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "env"
  • note frontmatter-key unknown frontmatter key "secrets"
  • note frontmatter-key unknown frontmatter key "security"
  • note frontmatter-key unknown frontmatter key "keywords"

Process rating: all ten parameters 63/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 7253 tokens
  • 85Steps. 101 steps, 1 vague phrases
  • 100Failures and branches. 3 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 20 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (3 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)
  • -255 emoji in the instructions: noise for the model
  • -31 of 9 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 358: enough signal without eating the budget
  • +4Structure: 61 headings
  • +3Step-by-step instructions: 101 items
  • +3Output format is stated explicitly
  • +4Has examples (12 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.