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AC ljh-qianchuan

千川掉量诊断器。用户描述掉量症状，按决策树一步步走，两分钟给出第一动作。 触发方式：/ljh-qianchuan、「千川消耗掉了」「投放量突然没了」「ROI 掉了怎么办」 Douyin ad (Qianchuan) traffic-drop diagnostic tool. Walks through a decision tree one node at a time to find the first action within two minutes. Trigger: /ljh-qianchuan, "my ad spend dropped", "Qianchuan traffic disappeared", "ROI suddenly dropped" 不要在非千川的广告平台问题或网站流量下降问题上自动触发。

ClawHub Agent Skills author: HandsoMeng v1.0.0 MIT-0 2 files body ≈ 2 003 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

千川掉量诊断器。用户描述掉量症状，按决策树一步步走，两分钟给出第一动作。 触发方式：/ljh-qianchuan、「千川消耗掉了」「投放量突然没了」「ROI 掉了怎么办」 Douyin ad (Qianchuan) traffic-drop diagnostic tool.

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

AnalyzerCommercetype 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. 48 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2003 tokens
    • 100Running it twice. No mutating operations
    • low 16 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

    • +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 3 example trigger phrases
    • +3Description length 364: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 48 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 not clearly malicious, but it needs Review because it expands beyond its advertised diagnostic purpose and uses persistent local files without enough upfront disclosure.
    LLM: suspicious (high) · 6 Aug 2026