AC yolo-vision-tools
Use Ultralytics YOLO to perform computer vision tasks, such as detecting people or objects in images and videos, classifying images, estimating human poses, and tracking cars, people, or animals in videos.
As a process C 62/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, progress reporting
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: 17. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 62/100
- 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) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 85Steps. 50 steps, 1 vague phrases
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2804 tokens
- 100Running it twice. Mutating operations check current state
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)
- +1No license
- +2Single-language instructions
- +3Description length 205: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 50 items
- +3Output format is stated explicitly
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (9 of 9)
- +3All 6 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.
External checks
ClawHub: clean
This is a coherent YOLO computer-vision toolkit, with expected local setup, model download, diagnostics, training, and output-file behavior disclosed in the artifacts.
LLM: benign (high) · VirusTotal: · 29 May 2026