AD qubitclient-vqa-review
Quantum experiment VISUAL review and Question Answering (VQA) using Vision Language Models (VLM). Analyze experiment RESULT PLOTS to: (1) Describe plot types and axes, (2) Classify experiment outcomes (Expected/Suboptimal/Anomalous/Apparatus issue), (3) Scientific reasoning with next-step suggestions, (4) Assess fit reliability, (5) Extract physical parameters from plots, (6) Evaluate experiment status (SUCCESS/FAILURE). Note: Use tools for numerical fitting on RAW DATA. Supports 20+ experiment families including T1, T2, Rabi, Ramsey, spectroscopy, DRAG, pinchoff, and more.
Quantum experiment VISUAL review and Question Answering (VQA) using Vision Language Models (VLM).
As a process D 38/100 · Unfinished process — weak spots: steps, result and completion, when it triggers
How to improve
- 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: 2. 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 38/100
- 0Steps. Prose only: no discrete steps
- 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
- 20When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1920 tokens
- 100Running it twice. No mutating operations
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)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 580: enough signal without eating the budget
- +4Structure: 22 headings
- +4Has examples (19 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.