SKILLEMALL.ai

BD 1688-distribution-amazon

将1688商品铺货到Amazon店铺并保存为店小宝待发布草稿的工具链。当用户提到「铺货Amazon」「上架Amazon」「Amazon发品」「把1688商品发到Amazon」「亚马逊铺货」「生成Amazon草稿」「直接发布到Amazon」等场景时必须使用此技能。当前版本只创建店小宝 Amazon 待发布草稿;即便用户要求直接发布,也应告知仅支持保存草稿并引导用户去店小宝完成后续发布。覆盖:环境检查、店铺确认、session 初始化、1688商品查询、Amazon类目映射、主图白底/裁剪/翻译、Amazon schema解析、CPV映射、店小宝Amazon payload构造、本地草稿保存。

ClawHub Agent Skills author: 1688AiInfra v0.1.0 MIT-0 20 files · 1 script body ≈ 1 774 tokens Open the sourceclawhub.ai analyzed 2 d ago

将1688商品铺货到Amazon店铺并保存为店小宝待发布草稿的工具链。当用户提到「铺货Amazon」「上架Amazon」「Amazon发品」「把1688商品发到Amazon」「亚马逊铺货」「生成Amazon草稿」「直接发布到Amazon」等场景时必须使用此技能。当前版本只创建店小宝 Amazon…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureCommerceSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. 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: 20. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 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
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 35 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1774 tokens
  • 100Running it twice. No mutating operations
  • low 17 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (12 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)
  • +3Output format is not stated: the model decides each time
  • -32 of 15 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 298: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 35 items
  • +4Has examples (16 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: clean
This skill is an e-commerce automation workflow that clearly focuses on creating Amazon draft listings and does not show hidden publishing, exfiltration, or destructive behavior.
LLM: benign (high) · VirusTotal: · 1 Jun 2026