BB rednote-food-note-maker
Create a Xiaohongshu food post or REDnote food post from a dish photo, restaurant visit theme, or dining-atmosphere reference. This REDnote food image maker plans restaurant review images and AI food photography as a vertical 3:4 food-note sequence: a cover, dish close-up, table or restaurant atmosphere image, and a final detail image for a food recommendation post. Shape title ideas, caption angles, and tags for a restaurant review post, cafe-hopping post, new-menu launch post, restaurant visit images, food diary images, and restaurant social media images.
Create a Xiaohongshu food post or REDnote food post from a dish photo, restaurant visit theme, or dining-atmosphere reference.
As a process B 66/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 15. 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")
Process rating: all ten parameters 66/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 8 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 15 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1974 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
- -32 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 563: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (9 of 9)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.