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AB china-shopping

Recommend suitable Chinese shopping platforms for a user-provided product type, product name, or shopping need. Use when the user asks where to buy something in China, which Chinese e-commerce platform fits a product category, where to shop for electronics, clothing, groceries, beauty products, or similar items, or asks questions like "买这个去哪", "推荐购物网站", "哪里买比较合适", or "中国购物推荐".

ClawHub Agent Skills author: haidong v1.1.0 MIT-0 7 files body ≈ 686 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process B 70/100 · Nearly there — weak spots: inputs and preconditions

ProcedureSoftware 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
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
B
70/100
Nearly there
Inputs and preconditions w 11
0
Tools and files w 18
60
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

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

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 70/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 30 steps, 1 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 686 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 379: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 30 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    This is a self-contained shopping-platform recommender that uses a local Python script and bundled data without requesting credentials, network access, persistence, or file writes.
    LLM: benign (high) · VirusTotal: benign · 14 May 2026