BD bambu-studio-ai
Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pipeline: search → generate → analyze → colorize → preview → open BS → user slice → print → monitor. Supports all 9 Bambu Lab printers (A1 Mini, A1, P1S, P2S, X1C, X1E, H2C, H2S, H2D).
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Dangerous commands
cmd-autorun-instructionSKILL.md:630Instructs the agent to auto-run a script on every session (detector / deny-list definition)| AI model has holes/floating parts | Expected. Always run `analyze.py --repair`. |
detector
Files scanned: 37. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6576 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "env" - note
frontmatter-keyunknown frontmatter key "secrets" - note
frontmatter-keyunknown frontmatter key "security" - note
frontmatter-keyunknown frontmatter key "keywords"
Process rating: all ten parameters 49/100
- 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
- 30Running it twice. 21 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70Execution cost. Instruction body is 6576 tokens
- 85Steps. 130 steps, 1 vague phrases
- 100Failures and branches. 17 branches, has a failure section
- 100Consistency. Name and required fields are in place
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 16 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)
- +3Output format is not stated: the model decides each time
- -236 emoji in the instructions: noise for the model
- -31 of 10 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 392: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 130 items
- +4Has examples (7 code blocks)
- +4Reference files are cited in the instructions (6 of 7)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.