AB atlas-banana-textimage
Generates images from text prompts using the AtlasCloud Nanobanana 2 model (google/nano-banana-2/text-to-image). Use this skill whenever the user wants to create, render, or generate an image from a text description using Nanobanana or AtlasCloud. Triggers on phrases like: "gerar imagem", "generate image", "create image", "criar imagem com prompt", "draw a scene", or any request to produce a visual from a text prompt. Always use this skill when the user mentions Nanobanana, AtlasCloud image generation, or wants to produce an image from descriptive text.
As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, consistency
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: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Generates images from text prompts using the AtlasCloud Nanobanana… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 65/100
- 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
- 30Running it twice. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (atlas-banana-textimage) differs from the folder (nano-banana-text-image)
- 100Tools and files. No external tools needed
- 100Steps. 9 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 1 branches, has a failure section
- 100Execution cost. Instruction body is 734 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
- +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
- +5Description quotes 5 example trigger phrases
- +3Description length 559: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 9 items
- +4Has examples (3 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.