AD queenshow-detail-page
Generate, update, and query Queenshow editor-next product detail pages through the Queenshow OpenAPI. Use when an agent needs to upload product images or videos, create an empty detail page from product information, wait for the outline task, apply outline sections, submit section image generation tasks, poll final status, fetch the generated document, or update the detail page document with an API key. Supports 9+ industries with proven templates and best practices from 100+ production runs.
Generate, update, and query Queenshow editor-next product detail pages through the Queenshow OpenAPI.
As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
edit-residuethe text marks something as outdated (lines 125): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 48/100
- 0Result and completion. Does not say what the result is
- 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
- 30Running it twice. 18 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
- 100Steps. 56 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1720 tokens
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
- -2localhost URLs: will not work for another user
- +1No license
- +2Single-language instructions
- +3Description length 497: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 56 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.