SKILLEMALL.ai

BC linkfox-mpstats-ozon-seller-products

MPSTATS Ozon 俄罗斯站按卖家 ID 下钻商品列表。返回该卖家下全部 SKU 的销量、销售额、价格、评分、库存、周转、损失销售额等完整指标,支持多维数值筛选、排序、货币换算。用于店铺结构分析、卖家爆款拆解、竞争对手店铺对标。当用户提到 Ozon 卖家商品、Ozon 店铺分析、Ozon 卖家下钻、Ozon 卖家 SKU、Ozon 店铺爆款、Ozon 竞争店铺、MPSTATS seller, Ozon seller drill-down, Ozon shop audit, Russian marketplace seller SKUs, Ozon store structure 时触发此技能。即使用户未明确说"MPSTATS",只要意图是按 Ozon 卖家 ID 看该店铺下全部商品的销售表现,也应触发此技能。

ClawHub Agent Skills author: linkfox-ai v1.0.6 MIT-0 6 files body ≈ 1 730 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerCommercetype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
97
Quality 40%
73
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Secrets in code secret-high-entropy-token scripts/onboarding.py:49
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    or "eyJh…iJ9")
    quoted

A further 2 matches are quotations in this security skill's documentation and are not counted as findings.

Files scanned: 6. 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 52/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 60Failures and branches. 2 branches
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1730 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 (9 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
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 362: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (5 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: suspicious
The skill provides the advertised Ozon analytics, but also bundles sensitive login, API-key, billing, feedback, and credential-handling flows that need Review before installation.
LLM: suspicious (high) · 14 Aug 2026