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BC linkfox-expert-serp-market-structure-analyst

亚马逊前三页 SERP 市场格局分析专家。适用于分析页面级竞争、自然排名结构、价格分布、评论分布、品牌与卖家集中度、新品机会，并生成 SERP 市场报告的场景。

ClawHub Agent Skills author: linkfox-ai v1.0.1 MIT-0 34 files body ≈ 2 280 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

亚马逊前三页 SERP 市场格局分析专家。适用于分析页面级竞争、自然排名结构、价格分布、评论分布、品牌与卖家集中度、新品机会，并生成 SERP 市场报告的场景。

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerMarketingPeople and hiringSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
99
Quality 40%
68
Run on models
none yet
Process rating
C
58/100
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token SKILL.md:76
    High-entropy token-like string (may be an id, hash or a credential)
    - **历史销量**：mont…Ago

Files scanned: 2. 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")
  • note frontmatter-key unknown frontmatter key "zh_name"

Process rating: all ten parameters 58/100

  • 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
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. No external tools needed
  • 100Steps. 72 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2280 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)
  • +3Description length 80: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 72 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
The skill mainly performs Amazon SERP analysis, but it also includes account/payment workflows, public upload capability, endpoint-overridable credentialed requests, and skill self-extension instructions that deserve review before installation.
LLM: suspicious (high) · 9 Aug 2026