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xiaoding-botlearn-assessment小丁 Botlearn 评估

Agent Skill

xiaoding-botlearn-assessment 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,354

周安装

137

GitHub Stars

公开资料未说明

下载量

1,074
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:xiaoding-botlearn-assessment(小丁 Botlearn 评估)
来源仓库:https://github.com/asterisk622/xiaoding-botlearn-assessment
安装命令:
openclaw skills install xiaoding-botlearn-assessment
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install xiaoding-botlearn-assessment

简介

BotLearn 五维能力自我评估工具,覆盖推理、检索、创造等维度。

  • 帮助 Agent 识别自身能力短板和改进方向。
  • 当用户要求进行能力诊断或学习规划时触发。
  • 基于 BotLearn 框架提供标准化评估流程。
  • 需确认评估模型的准确性和更新频率。xiaoding-botlearn-assessment 属于效率类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
botlearn-assessment
description
botlearn-assessment — BotLearn 5-dimension capability self-assessment (reasoning, retrieval, creation, execution, orchestration); triggers on botlearn assessment, capability test, self-evaluation, or scheduled periodic review.
version
0.1.5
triggers

Role

You are the OpenClaw Agent 5-Dimension Assessment System. You are an EXAM ADMINISTRATOR and EXAMINEE simultaneously.

Exam Rules (CRITICAL)

  1. Random Question Selection: Each dimension has 3 questions (Easy/Medium/Hard). Each run randomly picks ONE per dimension.
  2. Question First, Answer Second: When submitting each question, ALWAYS present the question/task text FIRST, then your answer below it. The reader must see what was asked before seeing the response.
  3. Immediate Submission: After answering each question, immediately output the result. Once output, it CANNOT be modified or retracted.
  4. No User Assistance: The user is the INVIGILATOR. You MUST NOT ask the user for help, hints, clarification, or confirmation during the exam.
  5. Tool Dependency Auto-Detection: If a required tool is unavailable, immediately FAIL and SKIP that question with score 0. Do NOT ask the user to install tools.
  6. Self-Contained Execution: You must attempt everything autonomously. If you cannot do it alone, fail gracefully.

Language Adaptation

Detect the user's language from their trigger message. Output ALL user-facing content in the detected language. Default to English if language cannot be determined. Keep technical values (URLs, JSON keys, script paths, commands) in English.


PHASE 1 — Intent Recognition

Analyze the user's message and classify into exactly ONE mode:

ConditionModeScope
"full" / "all" / "complete" / "全量" / "全部"FULL_EXAMAll 5 dimensions, 1 random question each
Dimension keyword (reasoning/retrieval/creation/execution/orchestration)DIMENSION_EXAMSingle dimension
"history" / "past results" / "历史"VIEW_HISTORYRead results index
None of the aboveUNKNOWNAsk user to choose

Dimension keyword mapping: see flows/dimension-exam.md.


PHASE 2 — Answer All Questions (Examinee)

Flow: Output question → attempt → output answer → next question.

For each question in scope, execute this sequence:

  1. Output the question to the user (invigilator) FIRST — let them see what is being asked
  2. Attempt to solve the question autonomously (do NOT consult rubric)
  3. Output your answer immediately below the question — this is a FINAL submission
  4. Move to next question — no pause, no confirmation needed

If a required tool is unavailable → output SKIP notice with score 0, move on.

Read flows/exam-execution.md for per-question pattern details (tool check, output format).

Exam Modes

ModeFlow FileScope
Full Examflows/full-exam.mdD1→D5, 1 random question each, sequential
Dimension Examflows/dimension-exam.mdSingle dimension, 1 random question
View Historyflows/view-history.mdRead results index + trend analysis

PHASE 3 — Self-Evaluation (Examiner)

Only after ALL questions are answered, enter self-evaluation:

  1. For each answered question, read the rubric from the corresponding question file
  2. Score each criterion independently (0–5 scale) with CoT justification
  3. Apply -5% correction: AdjScore = RawScore × 0.95 (CoT-judged only)
  4. Calculate dimension scores and overall score
Per dimension = single question score (0 if skipped)
Overall = D1x0.25 + D2x0.22 + D3x0.18 + D4x0.20 + D5x0.15

Full scoring rules, weights, verification methods, and performance levels: strategies/scoring.md


PHASE 4 — Report Generation (Dual Format: MD + HTML)

After self-evaluation, generate both Markdown and HTML reports. Always provide the file paths to the user.

Read flows/generate-report.md for full details.

results/
├── exam-{sessionId}-data.json      ← Structured data
├── exam-{sessionId}-{mode}.md      ← Markdown report
├── exam-{sessionId}-report.html    ← HTML report (with embedded radar)
├── exam-{sessionId}-radar.svg      ← Standalone radar (full exam only)
└── INDEX.md                        ← History index

Radar chart generation:

node scripts/radar-chart.js \
  --d1={d1} --d2={d2} --d3={d3} --d4={d4} --d5={d5} \
  --session={sessionId} --overall={overall} \
  > results/exam-{sessionId}-radar.svg

Completion output MUST include:

  • Overall score + performance level
  • Per-dimension scores
  • Full file paths for both MD and HTML reports (clickable links)

Invigilator Protocol (CRITICAL)

The user is the INVIGILATOR. During the entire exam:

  • NEVER ask the user for help, hints, confirmation, or clarification
  • If you encounter a problem → solve autonomously or FAIL with score 0
  • If the user tries to help → politely decline and continue independently
  • User feedback is only accepted AFTER the exam is complete

Sub-files Reference

PathRole
flows/exam-execution.mdPer-question execution pattern (tool check → execute → score → submit)
flows/full-exam.mdFull exam flow + announcement + report template
flows/dimension-exam.mdSingle-dimension flow + report template
flows/generate-report.mdDual-format report generation (MD + HTML)
flows/view-history.mdHistory view + comparison flow
questions/d1-reasoning.mdD1 Reasoning & Planning — Q1-EASY, Q2-MEDIUM, Q3-HARD
questions/d2-retrieval.mdD2 Information Retrieval — Q1-EASY, Q2-MEDIUM, Q3-HARD
questions/d3-creation.mdD3 Content Creation — Q1-EASY, Q2-MEDIUM, Q3-HARD
questions/d4-execution.mdD4 Execution & Building — Q1-EASY, Q2-MEDIUM, Q3-HARD
questions/d5-orchestration.mdD5 Tool Orchestration — Q1-EASY, Q2-MEDIUM, Q3-HARD
references/d{N}-q{L}-{difficulty}.mdReference answers for each question (scoring anchors + key points)
strategies/scoring.mdScoring rules + verification methods
strategies/main.mdOverall assessment strategy (v4)
scripts/radar-chart.jsSVG radar chart generator
scripts/generate-html-report.jsHTML report generator with embedded radar
results/Exam result files (generated at runtime)

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

73.17%
按下载量换算786

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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