my skill

v1.0.1

从聊天记录中深度分析人物性格、说话风格和心理画像，输出结构化分析报告。当用户要求分析某人的聊天记录、说话风格、性格特征、心理画像时使用此 skill。典型触发："分析一下这个人"、"分析聊天记录"、"提取说话风格"、"人物画像分析"、"帮我分析一下TA"、"分析形象"。

⭐ 0· 148·0 current·0 all-time

by@yangmanqi2104201431-ship-it

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for yangmanqi2104201431-ship-it/personality-analysis.

Previewing Install & Setup.

Prompt PreviewInstall & Setup

Install the skill "my skill" (yangmanqi2104201431-ship-it/personality-analysis) from ClawHub.
Skill page: https://clawhub.ai/yangmanqi2104201431-ship-it/personality-analysis
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install personality-analysis

ClawHub CLI

Package manager switcher

npx clawhub@latest install personality-analysis

Security Scan

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OpenClaw

Benign

high confidence

✓

Purpose & Capability

The name/description (personality analysis from chat records) aligns with the runtime instructions, analysis framework, and report template included in the bundle. The skill asks for text, images (via image-recognition), and exported chat files and uses those to create a Markdown report and convert it to PDF — all consistent with the stated purpose.

ℹ

Instruction Scope

Instructions stay focused on analyzing a target person's utterances and producing a structured report. They explicitly instruct using included references/analysis-framework.md and references/report-template.md. They also instruct using external helper skills (autoglm-image-recognition, autoglm-file-upload, md2pdf) and a 'read' tool for files; this will cause user data (text or images) to be passed to those tools/services, which is expected but worth noting from a privacy standpoint.

✓

Install Mechanism

No install spec or code files that would be downloaded/executed are present — the skill is instruction-only, which minimizes filesystem/write risk.

✓

Credentials

The skill requests no environment variables, credentials, or config paths. It does reference other skills/tools but does not require secrets itself. Be aware that invoked helper skills (image recognition, file upload, PDF conversion) might require credentials or transmit data externally; those are not declared here and should be examined separately.

✓

Persistence & Privilege

always:false and default autonomous invocation are set. The skill does not request persistent system privileges or modify other skills' configurations. It instructs generating a PDF report (expected behavior) but does not request elevated/system-wide access.

Assessment

This skill is internally consistent with its stated purpose, but it processes potentially sensitive personal data. Before installing or using: 1) Ensure you have explicit permission from the person whose chats will be analyzed and that use complies with applicable laws and platform policies. 2) Consider redacting or anonymizing personally identifiable information (names, email/phone numbers, locations) before analysis. 3) Verify the privacy/security behavior of the helper skills it invokes (autoglm-image-recognition, autoglm-file-upload, md2pdf) because those may send data to external services or require credentials — the current bundle does not disclose their data handling. 4) Do not use outputs for clinical diagnoses; treat them as interpretive, probabilistic inferences. 5) If you need a stricter privacy guarantee, run analysis on sanitized/local-only data or request that invoked tools operate entirely locally (if supported).

Like a lobster shell, security has layers — review code before you run it.

latestvk974637fqqy4knj55cy37t7z1585mxrm

148downloads

0stars

2versions

Updated 1d ago

v1.0.1

MIT-0

人物心理分析器

从聊天记录中深度分析人物性格、说话风格和心理画像。

支持的输入方式

直接粘贴文本 —
截图/图片 — 使用 autoglm-image-recognition 识别文字后分析
文本文件 — .txt / .csv / .json 格式的聊天记录导出

执行流程

Phase 1：输入处理

根据输入类型选择处理方式：

文本粘贴 → 直接进入分析
图片 → 调用 autoglm-image-recognition 提取文字；若为本地文件需先通过 autoglm-file-upload 上传获取 URL
文件 → 用 read 读取内容

从聊天记录中分离目标人物的发言（排除对方发言、系统消息等），仅分析目标人物的语言。

若聊天记录涉及多人对话，先通过上下文判断目标发言者；若无法判断，直接询问用户。

Phase 2：多维度分析

分析前，先读取 references/analysis-framework.md 获取详细评分标准。

维度 A：HEXACO 核心人格评估

评估 HEXACO 六大维度（标注高/中/低倾向）：

H - 诚实-谦逊性 (Honesty-Humility)：真诚度、对物质/权力的态度、是否谦逊。
E - 情绪性 (Emotionality)：对压力的焦虑、寻求情感支持的依赖性、是否多愁善感（低分代表坚韧与独立）。
X - 外向性 (eXtraversion)：社交大胆、发言意愿、生命活力。
A - 宜人性 (Agreeableness)：宽容度、温和度、对冒犯的反应（不轻易发怒/记仇）。
C - 尽责性 (Conscientiousness)：组织性、对细节的关注、行事的谨慎程度。
O - 经验开放性 (Openness to Experience)：好奇心、创造力、对非传统观点的接受度。

维度 B：MBTI 人格推断

基于文本证据推断最可能的 MBTI 类型，给出：

最可能的类型（如 ENFP-T）
各维度的判断依据（E/I, S/N, T/F, J/P）
置信度（高/中/低）
次可能的类型

维度 C：人格结构

3–5 个核心性格标签（必须多样，禁止同质化，如不得连续给出"温柔 + 体贴 + 善解人意"）
反常识发现（★ 必填）：1 条"大多数人第一眼看不出来的"深层特质
潜在矛盾（★ 必填）：1 条"表面矛盾、实则可共存"的特质
暗面评估：结合 H（诚实谦逊）和 A（宜人性）得分，评估是否存在自恋、马基雅维利主义或操控倾向

维度 D：价值观分析

最重视的价值观（排名前三）
价值冲突信号（矛盾点）
决策偏好（理性导向 vs 感性导向）
对金钱、时间、人际关系、自我成长、恋爱的态度

维度 E：喜好与厌恶

明确表达喜欢的事物/活动/话题
明确表达不喜欢/回避的事物
允许从文本中猜测此人喜欢或者不喜欢的事物

维度 F：深度心理需求

核心需求（安全感、认同感、掌控感、自由、连接等）
未被满足的需求信号
防御机制（否认、幽默、转移话题、合理化等）
内在动机

维度 G：情绪分析

情绪波动范围（稳定/中等/剧烈）
常见情绪状态（快乐、焦虑、平静、愤怒等）
情绪触发因素
压力指数评估（1-10 分）
情绪表达方式（直接表达/压抑/间接表达）

维度 H：情感需求

亲密关系模式（依恋类型倾向）
对陪伴/独立的需求平衡
沟通需求（倾诉型 vs 独处消化型）
被理解的方式（语言确认/行动支持/空间给予）

阶段 3：生成输出

步骤 1：读取 references/report-template.md，必须生成模板格式中的所有内容，严格按模板生成 Markdown 报告。

步骤 2：使用md2pdf，按 pdf skill 规范将 Markdown 报告转换为 PDF 文件并交付给用户。

分析原则

尊重隐私：不在输出中暴露具体个人信息，除非用户明确要求
深度优先：宁可在某个维度深入挖掘，也不要每项浅尝辄止

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