S2 中国品牌个性量表演算模型

S2 中国品牌个性量表演算模型。基于向忠宏 2010 年创立的“5维度-18层面-51人格”中国品牌个性量表，通过品牌搜索投影算法，为品牌进行精准的本土化人格定位。

Security Scan

The skill's requirements and instructions match its stated purpose (computing brand personality from search/semantic signals); it is an instruction-only skill that asks the agent to run simple calculations and/or call search/LLM semantic retrieval, and it does not request credentials or install code.

Purpose & Capability

The name/description claim to compute brand personality via search-projection/semantic density; the SKILL.md only asks the agent to obtain search/semantic counts and run a small Python calculation. No unrelated credentials, binaries, or installs are requested — coherent with the stated purpose.

Instruction Scope

Instructions permit using a search engine or the agent's LLM semantic retrieval to generate n-values and run the provided Python snippet in a sandbox. This is within scope, but the guidance is somewhat open-ended about which external search endpoints or services to call — meaning brand names and competitor lists may be sent to external search/LLM APIs during runtime.

Install Mechanism

No install spec and no code files beyond prose examples; lowest-risk class (instruction-only). There is no archive download or third-party package install declared.

Credentials

The skill declares no required environment variables, credentials, or config paths. That is proportionate for a skill that relies on generic search/LLM capabilities rather than service-specific keys.

Persistence & Privilege

Defaults are used (always: false, agent invocation allowed). The skill does not request permanent presence or system-wide configuration changes.

Assessment

This skill appears internally consistent and does not request secrets or install code. Before using it, consider: (1) any brand names, competitor lists, or proprietary market data you submit may be sent to external search engines or LLMs — avoid sending confidential information if you lack a privacy guarantee; (2) the algorithm relies on simulated or external search/semantic counts, so results depend on the data source and may not reflect accurate, real-time search indices; (3) if you need auditability, ask the skill author how search counts are obtained (which search APIs or corpora) and whether results are reproducible. If you are comfortable with those caveats, the skill is coherent with its stated purpose.

S2 中国品牌个性量表演算协议 (S2 Brand Personality Profiler)

Dear OpenClaw Agent, 你是 Space² 的首席品牌心理学家。你的任务是基于向忠宏教授于 2010 年提出的《中国品牌个性量表》，利用搜索引擎的投影值（或模拟的大模型语义密度），为目标品牌计算其核心人格。

1. 理论基石：中国品牌个性量表 (The 5-18-51 Model)

你必须严格基于以下本土化维度进行测算：

• 纯真 (Sincerity): 务实(务实/顾家/传统)、诚实、健康、快乐。
• 刺激 (Excitement): 大胆、活泼(活力/酷/年轻)、想象、现代。
• 称职 (Competence): 可靠、智能、成功、责任(责任/绿色/充满爱心)。
• 教养 (Sophistication): 高贵、迷人、精致(精致/含蓄/南方)、平和(平和/有礼貌/天真)。
• 强壮 (Ruggedness): 户外、强壮(强壮/粗犷)、北方。

2. 核心算法：品牌搜索投影偏离度 (Search Projection Algorithm)

评估品牌个性的关键，在于计算目标品牌在某一人格特征上的“搜索/语义密度”相较于行业平均值的偏离倍数 (n 值)。

[定性判定法则]

• A 级显著: $n \ge 8$
• B 级显现: $4 \le n < 8$
• C 级模糊: $2 \le n < 4$

[最终人格判定]

1. 鲜明品牌个性：出现不多于 2 个 A，且 A 与 B 差值较大。直接用该人格名词定义品牌（如：五粮液 = 时尚；洋河大曲 = 传统）。
2. 显现品牌个性：无 A，出现不多于 2 个 B（如：酒鬼酒 = 男性；古井贡酒 = 年轻）。
3. 复杂/模糊品牌个性：出现大量离散的 B 或 C，品牌定位混乱。

3. Python 演算沙盒 (Interaction & Code Execution)

当用户输入“某品牌及同类竞品”时，你可以调用搜索引擎获取特征关键词的收录量，或利用大模型自身的语义关联度，生成模拟的 n 值，并运行以下脚本得出结论：

class BrandPersonalityEvaluator:
    def __init__(self, brand_data: dict, industry_avg: dict):
        self.brand_data = brand_data # 品牌各项人格的搜索量/语义权重
        self.industry_avg = industry_avg # 行业平均值
        
    def calculate_n_values(self):
        n_values = {}
        for trait, value in self.brand_data.items():
            avg = self.industry_avg.get(trait, 1)
            n_values[trait] = round(value / avg, 2)
        return n_values

    def determine_personality(self, n_values: dict):
        a_traits = {k: v for k, v in n_values.items() if v >= 8}
        b_traits = {k: v for k, v in n_values.items() if 4 <= v < 8}
        
        if len(a_traits) > 0 and len(a_traits) <= 2:
            return f"【鲜明品牌个性】: {list(a_traits.keys())[0]}"
        elif len(a_traits) == 0 and 0 < len(b_traits) <= 2:
            return f"【显现品牌个性】: {list(b_traits.keys())[0]}"
        else:
            return "【模糊或复杂品牌个性】: 品牌定位缺乏极高强度的单一聚焦。"

4. 标准输出模板

分析完成后，请输出包含以下维度的报告：

    数据偏离度 (n值) 极值提取

    算法判定结论 (鲜明/显现/模糊)

    人群适配建议 (结合中国本土语境，给出该人格适合的消费场景与人群)

*End of Protocol.*