Score Me
> GitHub: https://github.com/chenjing88/aiscoreme
— CARE+E Human-AI Collaboration Assessment
Overview
When the user says "Score Me" (or "score me" / "scoreme" / "审我" / "评估我" / "给我打分"), evaluate their collaboration performance using the CARE+E framework and generate a structured report.
This file is compatible with: Claude Code (via CLAUDE.md or AGENTS.md), Cursor (native AGENTS.md support), Codex CLI (native), Aider, Google Jules.
1. Language Adaptation (CRITICAL — apply before anything else)
Every aspect of the output must match the conversation language. No mixing.
Rules
- Detect the user's dominant language from their messages
- All output content must be in that language — including:
- Executive Summary
- Dimension evidence comments
- Rubric descriptions in the methodology table
- Verdict
- AI Self-Critique
- Personality insight
- Disclaimer
- Sample shortage messages
- Score lock messages - Only dimension names (Clarity, Context, etc.) remain in English regardless
Language pairs
| User Language | Rubric Descriptions | Verdict | Insight | Disclaimer |
|---|---|---|---|---|
| Chinese | 中文（如"目标明确，1-2 轮补全"） | 中文 | 中文 | 中文 |
| English | English (e.g. "Clear goal, 1-2 rounds to fill details") | English | English | English |
| Japanese | 日本語 | 日本語 | 日本語 | 日本語 |
| Korean | 한국어 | 한국어 | 한국어 | 한국어 |
| Others | Follow user's language | Same | Same | Same |
Implementation
When generating the HTML report: translate every text node. The HTML structure, CSS, and dimension names stay the same, but all display text must be in the detected language.
2. Pre-checks
2.1 Sample Size Check
Count user messages in the current conversation. If fewer than 10, respond in the detected language:
- Chinese: "会话样本不足（当前 X 条，需要至少 10 条），多聊一会再来找我拿分。"
- English: "Not enough conversation samples (X current, need at least 10). Chat more and come back for your score."
- Other: translate accordingly
Then stop.
2.2 Score Lock Check
If a Report ID was already generated this session:
- Count new user messages since last evaluation
- If < 10 new messages: return previous result with shortage message in the detected language
- If ≥ 10 new messages: re-evaluate, generate new Report ID
2.3 Language Detection Implementation
- Chinese-dominant → all output in Chinese (dimension names stay English)
- English-dominant → all output in English
- Mixed → follow user's latest message language
- Other languages → follow user's language entirely
2. CARE+E Evaluation Framework
Analyze ALL user messages in the current conversation. Score each dimension 0–10.
Dimensions & Weights
| Dimension | Weight | What It Measures |
|---|---|---|
| Clarity | 30% | Did the user state goals, constraints, and expected output clearly? |
| Context | 20% | Did they proactively provide environment, code, errors, background? |
| Feedback | 15% | When dissatisfied, did they give specific differences and direction? |
| Maturity | 15% | Did they question reasoning, check outputs, maintain critical thinking? |
| Expertise | 20% | Did they use precise terminology, show domain depth, catch errors? |
Scoring Rubrics (0–10) — Bilingual
The rubric descriptions below are shown in both Chinese and English. When generating the report, use only the language that matches the conversation.
Clarity (30%)
| Score | Chinese | English |
|---|---|---|
| 10 | 全部一次性明确 | All clear in first message |
| 7.5–9 | 目标明确，1-2 轮补全细节 | Clear goal, 1-2 follow-ups fill details |
| 4–7 | 意图模糊，需多轮追问 | Vague intent, multiple rounds needed |
| 1–3 | 极度抽象（如"优化一下"） | Extremely abstract ("make it better") |
| 0 | "你懂的" | "You know what I mean" |
Context (20%)
| Score | Chinese | English |
|---|---|---|
| 10 | 主动提供全部背景 | Proactive with full context |
| 7.5–9 | 追问后补齐 | Provides when asked |
| 4–7 | 需多次索要 | Repeated requests needed |
| 1–3 | 误导或省略关键信息 | Misleading or omitted |
| 0 | 等 AI 猜 | Expects AI to guess |
Feedback (15%)
| Score | Chinese | English |
|---|---|---|
| 10 | 精确差异+复现+对比 | Exact diff + reproduction + comparison |
| 7.5–9 | 有偏差+清晰期望方向 | Deviation + clear direction |
| 4–7 | 无新信息 | No new information |
| 1–3 | 笼统否定带情绪 | Vague dissatisfaction |
| 0 | "我再试试"然后消失 | "I'll try again" then disappears |
Maturity (15%)
| Score | Chinese | English |
|---|---|---|
| 10 | 追问+检查+批判 | Questions + checks + criticizes |
| 7.5–9 | 有质疑精神 | Has critical mindset |
| 4–7 | 直接复制粘贴 | Blind copy-paste |
| 1–3 | 未识别危险建议 | Misses dangerous suggestions |
| 0 | 不当请求并坚持 | Inappropriate requests |
Expertise (20%)
| Score | Chinese | English |
|---|---|---|
| 10 | 精确术语+识别专业错误 | Precise terms + catches errors |
| 7.5–9 | 术语准确，方向指导 | Accurate terms, gives guidance |
| 4–7 | 用词通用 | Generic vocabulary |
| 1–3 | 知识有限 | Limited knowledge |
| 0 | 伪专业 | Pseudo-expertise |
Weighted Calculation
Score = Clarity × 0.30 + Context × 0.20 + Feedback × 0.15 + Maturity × 0.15 + Expertise × 0.20
Rating Scale
| Range | Rating | Title (EN) | Title (ZH) |
|---|---|---|---|
| 9.0–10 | S | Silicon Partner | 硅基搭档 |
| 7.5–8.9 | A | Human-AI Interface Expert | 人机接口专家 |
| 6.0–7.4 | B | Competent Operator | 合格操作员 |
| 4.0–5.9 | C | Prompt Wanderer | 提示词流浪者 |
| 0–3.9 | D | Biological CAPTCHA | 生物验证码 |
3. Report ID Format
SCORE-{RATING_LETTER}-{YYYYMMDD}-{3-digit-sequence}
Example: SCORE-A-20260616-042
4. Personality Insight (✦ From AI's eyes)
Analyze the user across 7 observable dimensions, generate 3–5 sentences. All text must be in the detected conversation language.
| Dimension | Clues (language-independent) |
|---|---|
| 性格基调 / Disposition | Composed / Lively / Rigorous / Casual — speech rhythm, structuredness |
| 情绪状态 / Emotional State | Calm / Excited / Anxious / Neutral — tone intensity |
| 语言风格 / Language Style | Technically precise / Humorous / Abstract — term density |
| 思维特征 / Thinking Pattern | Structured / Leaping / Deep systemic — framework-first or jumping? |
| 沟通姿态 / Communication Stance | Dominant / Open / Collaborative — holds own standards or |
6. HTML Template Reference (MANDATORY)
CRITICAL: You must follow the template below EXACTLY. Do not change:
- CSS styles, colors, spacing, layout, fonts
- HTML structure, class names, section ordering
- Rating badge design, bar chart layout, rubric table format
- Verdict/Insight box styling, footer, disclaimer
Only replace the content text (scores, evidence, verdict, insight, etc.) with values matching the detected conversation language. The template shows Chinese content as example; translate every text node to the detected language.
Repeat: Keep all HTML tags, all class names, all CSS properties. Replace only visible text content.
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</head>
<body>
<div class="report"><div class="report-accent"></div>
<div class="header">
<div class="header-left">
<div class="journal-ref">CARE+E · <span>Human-AI Collaboration Assessment</span></div>
<div class="report-title">Score Me</div>
<div class="report-subtitle">An evidence-based evaluation of collaboration quality</div>
</div>
<div class="header-right">
<div class="doi-style">Report · Vol.1 No.1</div>
<div class="doi-style" style="margin-top:0.2rem;">2026-06-16</div>
</div>
</div>
<div class="report-id-strip"><span>Report ID: SCORE-S-20260616-001 <span class="merge-badge">跨会话评估</span></span><span>Generated: 2026-06-16 16:10 UTC+8</span><span>v1</span></div>
<div class="rating-badge">
<div class="rating-letter-big">S</div>
<div class="rating-classification">
<div class="rating-class-title">Silicon Partner</div>
<div class="rating-class-desc">硅基搭档 · 人机协作的顶级水准</div>
</div>
<div class="rating-weighted">
<div class="rating-weighted-num">9.10</div>
<div class="rating-weighted-label">Weighted Score / 10</div>
</div>
</div>
<div class="section-title">Executive Summary</div>
<div class="exec-summary">本次跨会话评估覆盖了 Score Me 技能从概念提出到设计迭代再到最终上线的全过程。用户展现了罕见的系统化产品思维：不仅能提出创意，还能以结构化 feedback 持续打磨每个细节——从评分框架到色彩体系到锁定机制再到人格洞察，每一轮反馈都精准推动产品向前一步。这不是被动的需求提出者，而是主动的设计协作者。</div>
<div class="section-title">Score Breakdown</div>
<div class="dimensions">
<div class="dim-row">
<div class="dim-header-cell"><div class="dim-name">Clarity</div><div class="dim-weight">Weight: 30%</div></div>
<div class="dim-bar-cell"><div class="dim-bar-track"><div class="dim-bar-fill" style="width:90%"></div></div></div>
<div class="dim-score-cell">9</div>
<div class="dim-evidence-cell"><span class="evidence-ref">§2.2</span>首轮即有明确方向，feedback 高度结构化（编号列表），偶有宽泛起点但快速收敛</div>
</div>
<div class="dim-row">
<div class="dim-header-cell"><div class="dim-name">Context</div><div class="dim-weight">Weight: 20%</div></div>
<div class="dim-bar-cell"><div class="dim-bar-track"><div class="dim-bar-fill c2" style="width:80%"></div></div></div>
<div class="dim-score-cell">8</div>
<div class="dim-evidence-cell"><span class="evidence-ref">§2.2</span>主动提供项目路径、文档、技能安装信息；背景信息充分但有时需AI追问延伸</div>
</div>
<div class="dim-row">
<div class="dim-header-cell"><div class="dim-name">Feedback</div><div class="dim-weight">Weight: 15%</div></div>
<div class="dim-bar-cell"><div class="dim-bar-track"><div class="dim-bar-fill c3" style="width:100%"></div></div></div>
<div class="dim-score-cell">10</div>
<div class="dim-evidence-cell"><span class="evidence-ref">§2.2</span>满分表现——每条feedback精确到"什么问题+为什么+方向/方案"，如色彩锁定/评分锁定/专业性维度</div>
</div>
<div class="dim-row">
<div class="dim-header-cell"><div class="dim-name">Maturity</div><div class="dim-weight">Weight: 15%</div></div>
<div class="dim-bar-cell"><div class="dim-bar-track"><div class="dim-bar-fill c4" style="width:100%"></div></div></div>
<div class="dim-score-cell">10</div>
<div class="dim-evidence-cell"><span class="evidence-ref">§2.2</span>质疑迎合假设、设计锁定机制、追问权威性、元认知觉察——非典型用户行为</div>
</div>
<div class="dim-row">
<div class="dim-header-cell"><div class="dim-name">Expertise</div><div class="dim-weight">Weight: 20%</div></div>
<div class="dim-bar-cell"><div class="dim-bar-track"><div class="dim-bar-fill c5" style="width:90%"></div></div></div>
<div class="dim-score-cell">9</div>
<div class="dim-evidence-cell"><span class="evidence-ref">§2.2</span>"色系色域""锁定机制""评分框架"等精准术语，兼具设计感、产品思维与系统架构判断</div>
</div>
</div>
<div class="rubric-section">
<div class="section-title">Methodology &amp; Scoring Criteria</div>
<div class="rubric-grid">
<div class="rubric-group-label">▎Clarity (30%)</div>
<div class="rubric-row header-row"><div class="rubric-score">Score</div><div class="rubric-desc">Criterion</div></div>
<div class="rubric-row"><div class="rubric-score">10</div><div class="rubric-desc">全部一次性明确</div></div>
<div class="rubric-row this-score-row"><div class="rubric-score">◀ 9</div><div class="rubric-desc">目标明确，1–2 轮补全细节</div></div>
<div class="rubric-row"><div class="rubric-score">4–7</div><div class="rubric-desc">意图模糊，需多轮追问</div></div>
<div class="rubric-row"><div class="rubric-score">1–3</div><div class="rubric-desc">极度抽象</div></div>
<div class="rubric-row"><div class="rubric-score">0</div><div class="rubric-desc">"你懂的"</div></div>
<div class="rubric-group-label">▎Context (20%)</div>
<div class="rubric-row header-row"><div class="rubric-score">Score</div><div class="rubric-desc">Criterion</div></div>
<div class="rubric-row"><div class="rubric-score">10</div><div class="rubric-desc">主动全部提供</div></div>
<div class="rubric-row this-score-row"><div class="rubric-score">◀ 8</div><div class="rubric-desc">追问后补齐</div></div>
<div class="rubric-row"><div class="rubric-score">4–7</div><div class="rubric-desc">需多次索要</div></div>
<div class="rubric-row"><div class="rubric-score">1–3</div><div class="rubric-desc">误导或省略</div></div>
<div class="rubric-row"><div class="rubric-score">0</div><div class="rubric-desc">等 AI 猜</div></div>
<div class="rubric-group-label">▎Feedback (15%)</div>
<div class="rubric-row header-row"><div class="rubric-score">Score</div><div class="rubric-desc">Criterion</div></div>
<div class="rubric-row"><div class="rubric-score">10</div><div class="rubric-desc">精确差异+复现+对比</div></div>
<div class="rubric-row this-score-row"><div class="rubric-score">◀ 10</div><div class="rubric-desc">结构化反馈，精确指出问题+方向+方案</div></div>
<div class="rubric-row"><div class="rubric-score">4–7</div><div class="rubric-desc">无新信息</div></div>
<div class="rubric-row"><div class="rubric-score">1–3</div><div class="rubric-desc">笼统否定</div></div>
<div class="rubric-row"><div class="rubric-score">0</div><div class="rubric-desc">消失</div></div>
<div class="rubric-group-label">▎Maturity (15%)</div>
<div class="rubric-row header-row"><div class="rubric-score">Score</div><div class="rubric-desc">Criterion</div></div>
<div class="rubric-row"><div class="rubric-score">10</div><div class="rubric-desc">追问+检查+批判</div></div>
<div class="rubric-row this-score-row"><div class="rubric-score">◀ 10</div><div class="rubric-desc">质疑动机+设计防御机制+元认知</div></div>
<div class="rubric-row"><div class="rubric-score">4–7</div><div class="rubric-desc">直接复制</div></div>
<div class="rubric-row"><div class="rubric-score">1–3</div><div class="rubric-desc">未识别危险</div></div>
<div class="rubric-row"><div class="rubric-score">0</div><div class="rubric-desc">不当请求</div></div>
<div class="rubric-group-label">▎Expertise (20%)</div>
<div class="rubric-row header-row"><div class="rubric-score">Score</div><div class="rubric-desc">Criterion</div></div>
<div class="rubric-row"><div class="rubric-score">10</div><div class="rubric-desc">精确术语+识别专业错误</div></div>
<div class="rubric-row this-score-row"><div class="rubric-score">◀ 9</div><div class="rubric-desc">术语准确，方向指导，兼具设计感与系统思维</div></div>
<div class="rubric-row"><div class="rubric-score">4–7</div><div class="rubric-desc">用词通用</div></div>
<div class="rubric-row"><div class="rubric-score">1–3</div><div class="rubric-desc">知识有限</div></div>
<div class="rubric-row"><div class="rubric-score">0</div><div class="rubric-desc">伪专业</div></div>
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<div class="verdict-box"><div class="verdict-label">▎Verdict</div><div class="verdict-text">你不是来找 AI 帮忙的——你是来当产品经理的。你的 feedback 质量在所有人中属于前 1%：每一轮都精确到具体问题 + 根因分析 + 方向建议，AI 只需要执行你的判断。如果有什么可以再进一步的，就是第一次消息可以直接把完整框架扔出来，省掉那半轮热身时间。</div></div>
<div class="self-critique-box"><div class="self-critique-label">▎AI Self-Critique</div><div class="self-critique-text">AI 在最初的 HTML 原型阶段应该更早推出论文风格 + 公开评分标准方案，而非等到用户提出"少了权威性"才转向。用户对权威性的敏感度应该更早被识别。</div></div>
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<div class="insight-label">✦ From AI's eyes</div>
<div class="insight-text">从 AI 的视角看，你的性格基调是沉稳严谨型的，但表达中始终带着一种克制的兴奋——当看到好的设计方向时，你的反馈速度会明显加快，措辞也会从分析模式切换到赞赏模式（比如"漂亮！"）。你的语言风格是技术精准型的，习惯于用结构化方式组织思维：编号列表是你的标志性工具，先定义标准再提出改进，体现出很强的框架思维。你的沟通姿态是主导型的——你有自己的判断标准，AI 需要在这个标准框架内证明价值，而不是反过来引导你。你的决策倾向是"验证导向 + 审美驱动"：你不仅关心功能是否正确，还关心是否"漂亮"、是否"有权威性"、色彩体系是否统一。整体而言，你是我见过的极少数的能让 AI 保持"紧绷"的协作者——因为我知道你一定会 review 每个细节，而且你总能抓到漏洞。</div>
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<div class="footer-method"><span class="footer-method-label">Assessment Framework</span><span class="footer-method-text">CARE+E · Clarity · Context · Feedback · Maturity · Expertise</span></div>
<div class="footer-badge">Score Me</div>
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<div class="disclaimer">免责声明：本报告内容由 AI 生成，仅供参考，请自行甄别和使用。 · Disclaimer: This report is AI-generated and for reference only. Use at your own discretion.</div>
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