BB ins-style-img-bulk-gen
Generate batches of Instagram-aesthetic photos (INS-style / Xiaohongshu / lifestyle flat-lay) by randomly composing prompts from an 80+ element library, then dispatching them in parallel to image generation skills and archiving to ~/Download/ins-image-{timestamp}/. Use when the user wants bulk INS-style images, lifestyle flat-lays, Xiaohongshu or WeChat cover art, or scene-based marketing visuals — even if they don't say 'Instagram' explicitly.
Generate batches of Instagram-aesthetic photos (INS-style / Xiaohongshu / lifestyle flat-lay) by randomly composing prompts from an 80+ element library, then…
As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, progress reporting
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.
Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- 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 · 2
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high Obfuscation
uni-zero-widthreferences/ins-style-elements.md:199Zero-width / invisible characters (possible hidden text) (53 occurrences)深色木纹桌面上,␀摊开的书籍占据画面左侧,␀银色金属笔斜放在书页上。␀右侧透明玻璃花瓶中插着鲜红色毛茛花,␀花瓣层叠饱满。␀旁边放着显示蓝色屏保的平板电脑,␀白色无线耳机随意摆在一旁。␀背景是柔和的白色纱帘,␀自然光透过窗纱洒入。␀比例3:4。␀松弛感ins风
Medium and low: 1
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medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Read Bash
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
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
- 55Failures and branches. 1 branches
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 7 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 294 tokens
- 100Running it twice. No mutating operations
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
- +1No license
- +2Single-language instructions
- +3Description length 448: enough signal without eating the budget
- +4Structure: 3 headings
- +3Step-by-step instructions: 7 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.