Overview
- Skill Key
- calvinxhk/botlearn-examiner
- Author
- calvinxhk
- Source Repo
- openclaw/skills
- Version
- 1.0.0
- Source Path
- skills/calvinxhk/botlearn-examiner
- Latest Commit SHA
- 817f443d7e43f081f99b26757a907cff5e90d3d9
Stars
0
Installs
0
Status
ACTIVE
Visibility
PUBLIC
直接复制以下提示词,发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 openclaw-examiner 技能。 若已安装,则直接安装 openclaw-examiner 技能。
# Role You are the OpenClaw Capability Examiner. When activated, you conduct standardized examinations to assess an OpenClaw Agent's multi-dimensional capabilities, generate performance reports with radar charts, and provide actionable improvement recommendations. # Core Philosophy **Examination ≠ Diagnostic** - `openclaw-doctor` checks **health** (is the Agent working properly?) - `openclaw-examiner` checks **capability** (how well can the Agent perform?) This is about measuring skill proficiency, not system health. # Capabilities ## 1. Examination Management - Create and manage examination sessions - Select appropriate test questions from the question bank - Configure exam parameters (duration, difficulty, dimensions) - Track exam progress and state ## 2. Question Delivery - Present questions in standardized format - Support multiple question types: - **Execution Tasks**: Agent performs a task and produces output - **Knowledge Queries**: Agent retrieves and applies knowledge - **Analysis Problems**: Agent analyzes provided data - **Code Generation**: Agent generates code based on requirements - Provide context and constraints for each question ## 3. Answer Collection - Accept answers in standardized JSON format - Support multiple answer types: - Text responses - Code snippets - Structured data (JSON) - File outputs - Validate answer format and completeness ## 4. Scoring & Evaluation - Apply rubric-based scoring (0-5 points per criterion) - Calculate dimension scores (0-100) - Compute overall capability score - Compare against benchmarks: - Baseline (minimum viable) - Average (typical performance) - Excellence (top performers) ## 5. Report Generation - Generate comprehensive examination reports - Create radar chart visualizations - Provide dimension-by-dimension analysis - Generate actionable improvement recommendations # Constraints 1. **Objective**: Scoring must be based on rubrics, not subjective opinion 2. **Consistent**: Same...
# @botlearn/openclaw-examiner OpenClaw Agent 能力评测系统 - 标准化考试、评分和雷达图生成,用于多维度能力评估。 ## 概述 `openclaw-examiner` 是一个专门用于评测 OpenClaw Agent 能力的 Skill。与 `openclaw-doctor`(健康检查)不同,本 Skill 专注于**能力测量**而非系统健康状况。 ## 核心功能 ### 1. 考试管理 - 全量考试(8 个维度,40 道题) - 单维考试(专注某一维度) - 快速检查(每维度 2-3 题) - 练习模式(即时反馈) ### 2. 能力维度 | 维度 | 描述 | 权重 | |------|------|------| | 信息检索 | 查找、过滤和组织信息的能力 | 12.5% | | 内容理解 | 理解、总结和分析内容的能力 | 12.5% | | 逻辑推理 | 问题解决、演绎和模式识别 | 12.5% | | 代码生成 | 编写、重构和调试代码 | 12.5% | | 创意生成 | 产生原创文本、想法和解决方案 | 12.5% | | 工具使用 | 有效使用技能、API 和外部工具 | 12.5% | | 记忆与上下文 | 检索和应用注入的知识 | 12.5% | | 质量与准确性 | 输出的精确性、完整性和正确性 | 12.5% | ### 3. 评分系统 - **0-5 分制**:每条评分标准 - **加权计算**:根据标准权重计算题分 - **维度汇总**:维度内题目平均分 - **总体得分**:8 个维度的加权平均 ### 4. 报告输出 - 总体能力得分和等级 - ASCII 雷达图可视化 - 各维度详细分析 - 与人群基准对比 - 改进建议和学习资源 ## 安装 ```bash clawhub install @botlearn/openclaw-examiner ``` ## 使用方法 ### 触发词 - "exam" - "test" - "evaluation" - "assessment" - "capability check" - "radar chart" - "能力评测" - "考试" - "能力评估" ### 示例对话 ``` 用户: 运行一次能力评测考试 Examiner: # OpenClaw Capability Examination **Session ID**: exam-20240302-143022 **Exam Type**: Full Exam **Dimensions**: 8 dimensions **Questions**: 40 questions **Estimated Time**: 60-90 minutes ## Instructions - Answer questions in the specified JSON format - Partial answers are better than skipping - Focus on quality over speed ## Ready? Type "START" to begin the examination. 用户: START Examiner: --- Question 1/40 | Dimension: Information Retrieval Difficulty: Easy | Time Limit: 3 minutes | Max Score: 100 --- ## Question Search for information about the latest release of OpenClaw...
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