BC quantinuum
Write and run quantum circuits using Quantinuum's Guppy language on the Selene emulator. Triggers on Guppy, Selene, Quantinuum, SWAP test, QSP/QSVT, Shor / modular exponentiation, quantum kernel, qubit circuits, parameter sweeps, the selene_run schema, or quantum topological data analysis (QTDA).
As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
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: 38. 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 ≈ 14794 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 57/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 34 mutating operations with no state check
- 40Consistency. Frontmatter name (quantinuum) differs from the folder (quantum)
- 40Execution cost. Instruction body is 14794 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 100Steps. 85 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 6 branches, has a failure section
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (17 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 297: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 85 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (33 of 33)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.