SKILLEMALL.ai

AD perceptron

Image and video analysis powered by Isaac vision models. Capabilities include visual Q&A, object detection, OCR, captioning, counting, and grounded spatial reasoning with bounding boxes, points, and polygons. Supports streaming, structured outputs, DSL composition, and in-context learning. NOT for: generating images (analysis only).

ClawHub Agent Skills author: Subraiz Ahmed v1.0.1 6 files body ≈ 1 397 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerMedia and videoInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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.
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: 6. 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")

Process rating: all ten parameters 46/100

  • 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
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 75Steps. 3 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1397 tokens
  • 100Running it twice. No mutating operations
  • 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
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 334: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 3 items
  • +4Has examples (7 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

External checks

ClawHub: clean
This skill is a coherent Perceptron vision-analysis helper, but users should understand that selected images, videos, URLs, and outputs may go to an external API.
LLM: benign (medium) · VirusTotal: · 29 May 2026