AD linkfox-aigc-imagegen-brand-gene-extract
品牌基因样式提取原子技能。根据商品图片与用户品牌基因参数（主色、字体、平台、地区、语言），提取统一的品牌视觉语言（Brand DNA），输出结构化 brandGeneJson 供下游原子技能消费。品牌基因提取、brand gene extract、brand DNA、品牌视觉定义、品牌调性提取、brand style extraction、visual identity extraction。被套图编排层（linkfox-aigc-imagegen-cloth / product 套图编排路径）在步骤三中调用；当用户说"提取品牌基因"、"定义品牌风格"、"brand gene"、"品牌视觉"时触发。
品牌基因样式提取原子技能。根据商品图片与用户品牌基因参数（主色、字体、平台、地区、语言），提取统一的品牌视觉语言（Brand DNA），输出结构化 brandGeneJson 供下游原子技能消费。品牌基因提取、brand gene extract、brand DNA、品牌视觉定义、品牌调性提取、brand…
As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
The same skill appears in 1 more place: ClawHub
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: 4. 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 41/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (linkfox-aigc-imagegen-brand-gene-extract) differs from the folder (linkfox-expert-aigc-imagegen-brand-gene-extract)
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 100Steps. 50 steps
- 100Execution cost. Instruction body is 1834 tokens
- 100Running it twice. No mutating operations
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (6 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
- +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 3 example trigger phrases
- +3Description length 303: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 50 items
- +4Has examples (4 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.