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AC ljh-xuanpin

选品判断器。你报一个候选品，我按五步逐项核对，给出值不值得打的结论。 触发方式：/ljh-xuanpin、「这个品值不值得打」「帮我判断一下这个候选品」「选品怎么判断」 Product selection judgment. Give a candidate product, get a five-step go/no-go verdict. Trigger: /ljh-xuanpin, "should I pursue this product", "judge this product candidate" 选购个人消费品、挑选股票基金这类非打品选品场景，不要自动触发。

ClawHub Agent Skills author: HandsoMeng v1.0.0 MIT-0 2 files body ≈ 1 825 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

选品判断器。你报一个候选品，我按五步逐项核对，给出值不值得打的结论。 触发方式：/ljh-xuanpin、「这个品值不值得打」「帮我判断一下这个候选品」「选品怎么判断」 Product selection judgment.

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

ProcedureCommercetype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
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

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "displayName"
    • note frontmatter-key unknown frontmatter key "slug"

    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. 33 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1825 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 290: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 33 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    The skill is mostly a product-selection advisor, but it adds under-disclosed persistent file writes, business-data archiving, and onboarding/promotional behavior that users should review first.
    LLM: suspicious (high) · 6 Aug 2026