SKILLEMALL.ai

BC geekbi-ozon-research-skill

通过极鲸云查询和组合分析 Ozon 当前支持站点的真实商品、店铺、类目、关键词和评论数据,完成跨境选品、市场调研、竞品分析、SKU/SPU、价格带、需求供给、履约和用户反馈研究。用户提出 Ozon 综合选品、俄罗斯及其他可用市场机会、竞品店铺、类目赛道、搜索词或口碑研究,且需要多类数据形成经营判断时使用。只依据极鲸云真实返回数据,不提供 Ozon 平台内图搜、利润承诺或卖家后台操作。

ClawHub Agent Skills author: GeekBI v0.1.0 MIT-0 33 files body ≈ 267 tokens Open the sourceclawhub.ai analyzed 3 d ago

通过极鲸云查询和组合分析 Ozon 当前支持站点的真实商品、店铺、类目、关键词和评论数据,完成跨境选品、市场调研、竞品分析、SKU/SPU、价格带、需求供给、履约和用户反馈研究。用户提出 Ozon…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureCommercetype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
C
53/100
Has gaps
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: 31. 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 267 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
  • +4No input/output examples
  • -32 of 13 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 193: enough signal without eating the budget
  • +4Structure: 4 headings
  • +3Step-by-step instructions: 17 items
  • +4Reference files are cited in the instructions (7 of 13)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
The skill matches its Ozon research purpose, but it persists login tokens in several local folders, including the current workspace, which needs review before use.
LLM: suspicious (high) · 7 Sept 2026