AB microscopy-scale-bar-adder
Add accurate, publication-ready scale bars to microscopy images given pixel-to-unit calibration data.
As a process B 74/100 · Nearly there — weak spots: when it triggers
How to improve
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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "skill-author"
Process rating: all ten parameters 74/100
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 36 steps
- 100Failures and branches. 6 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1514 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- low 12 top-level sections: this looks like several domains in one skill
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)
- +3Description length 101: 120–800 characters recommended
- +2Single-language instructions
- +4Structure: 13 headings
- +3Step-by-step instructions: 36 items
- +3Output format is stated explicitly
- +4Has examples (3 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.
External checks
ClawHub: clean
This skill is a local image-editing helper that reads a user-selected microscopy image, draws a scale bar, and saves an output image, with no evidence of hidden network, credential, persistence, or destructive behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026