BD qubitclient-scope
Quantum experiment NUMERICAL curve fitting and parameter extraction. Support for (1) S21 peak detection (single/multi), (2) Optimal π-pulse calibration, (3) Rabi oscillation analysis, (4) T1/T2 relaxation time fitting, (5) DRAG pulse optimization, (6) Power shift characterization, (7) Ramsey fringe analysis, (8) Single-shot readout fidelity, (9) 2D spectrum analysis, (10) Spin Echo T2 fitting, (11) Randomized Benchmarking, (12) XYZ Timing calibration, (13) 2D T1 fitting, (14) Optimal readout frequency, (15) Delta experiment. Provides unified API for curve fitting, parameter extraction, and batch processing with matplotlib/plotly visualization support.
Quantum experiment NUMERICAL curve fitting and parameter extraction.
As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 6260 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 44/100
- 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
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70Execution cost. Instruction body is 6260 tokens
- 100Steps. 65 steps
- 100Consistency. Name and required fields are in place
- 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)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 659: enough signal without eating the budget
- +4Structure: 39 headings
- +3Step-by-step instructions: 65 items
- +4Has examples (51 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.