BC Nano-Banana-Cut
AI图片生成与智能切割工具，基于AceData Nano Banana模型，支持多分辨率多尺寸生成，自动切割为2/4/6/9宫格，自带瀑布流作品管理、批量下载功能。使用场景：(1) 输入prompt生成AI图片并自动切割成九宫格等多宫格 (2) 上传图片进行智能多宫格切割 (3) 管理生成的图片作品，支持打包下载
As a process C 56/100 · Has gaps — 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.
For the model run — optional
- 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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 56/100
- 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. 1 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. No external tools needed
- 100Steps. 94 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1905 tokens
- low 16 top-level sections: this looks like several domains in one skill
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
- -4Absolute local paths (C:\Users, /home/…): not portable
- -2localhost URLs: will not work for another user
- -234 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 157: enough signal without eating the budget
- +4Structure: 72 headings
- +3Step-by-step instructions: 94 items
- +4Has examples (15 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.
External checks
ClawHub: suspicious
This is a real image-generation and cutting tool, but its local web server exposes broad unauthenticated file, admin, credential, and shutdown controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026