AB alibabacloud-bailian-image-creator
AI image creation skill supporting text-to-image, image editing, image understanding, and more. Uses qwen-image-2.0-pro, wan2.7-image, qwen3.5-plus and other models. Use this skill when users need to generate images, edit images, analyze image content, or perform image-related tasks. Note: on first run, it will auto-manage DashScope API Keys (create/recycle) and may auto-install the Alibaba Cloud CLI ModelStudio plugin.
As a process B 68/100 · Nearly there — weak spots: 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: 15. 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: AI image creation skill supporting text-to-image, image editing, i… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 68/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Execution cost. Instruction body is 4955 tokens
- 100Steps. 48 steps
- 100Failures and branches. 7 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- high The skill tells the model to perform an irreversible action with no human approval
- low The response is described with custom markup (3 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)
- +1No license
- +2Single-language instructions
- +3Description length 423: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 48 items
- +3Output format is stated explicitly
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (4 of 5)
- +3All 7 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.