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BC alphashop-sel-newproduct

AlphaShop新品选品SKILL：基于关键词和商品筛选条件生成深度市场分析和新品推荐报告。 支持Amazon和TikTok平台的跨境电商选品，提供市场评级、竞争分析、新品推荐、热销品对比等功能。

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 8 files · 1 script body ≈ 2 589 tokens Open the sourcegithub.com↗ analyzed 2 d ago

AlphaShop新品选品SKILL：基于关键词和商品筛选条件生成深度市场分析和新品推荐报告。 支持Amazon和TikTok平台的跨境电商选品，提供市场评级、竞争分析、新品推荐、热销品对比等功能。

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

IntegrationData and analyticsMarketingtype 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%
96
Quality 40%
74
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 · 4

✓ No critical or high findings

Medium and low: 4
  • low Exfiltration read-dotenv QUICKSTART.md:35
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv QUICKSTART.md:41
    Reads a .env file
    source .env
  • low Exfiltration read-dotenv test.sh:31
    Reads a .env file (test fixture / example file)
    source .env
    fixture
  • low Exfiltration read-dotenv test.sh:35
    Reads a .env file (test fixture / example file; quoted — discussed, not commanded)
    echo "   运行: cp .env.example .env 并编辑"
    fixturequoted

Files scanned: 8. 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. 79 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2589 tokens
  • 100Running it twice. No mutating operations
  • low 13 top-level sections: this looks like several domains in one skill

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 99: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -222 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 45 headings
  • +3Step-by-step instructions: 79 items
  • +4Has examples (15 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.