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ELI5

ELI5 — A Claude Code skill that explains anything to anyone: kids, managers, engineers, parents. Adapts tone, vocabulary, and analogies to match the audience.

DreambigOuDreambigOu
75/ 100

公开评测 · 综合采用结论

核心能力可用,建议在受控范围内试用

查看评测依据 评测我的项目基于公开项目证据,非安全认证或安装推荐
1.6kstars
88forks
最近更新 6个月前
评测生成时间(北京时间)
本报告引擎
v3.10.0
当前引擎
v3.16.0

本报告与当前引擎使用不同规则;原分数不会自动更新,不同版本的分数不宜直接对比。

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进入后确认来源与额度,提交才会创建任务。

Evaluation report

综合采用结论

75
B
满分 100
值得试用低风险
决策摘要

核心能力可用,建议在受控范围内试用

75%
中置信度
77
文档
100
安全
71
质量
52
活跃
50
采用
  • 基础评测完成+25/25确定性评分与静态安全扫描已完成
  • README 有效证据+12/254,792 个去重后的有效字符
  • 独立证据来源+8/202 类非重复证据,重复文件不叠加
  • 仓库元数据+10/10已取得仓库状态与采用数据
  • 活跃记录+5/5已取得最近提交时间
  • AI 复核+15/15已完成结构化 AI 证据复核
How it works · 流程图

ELI5 技能工作流程

README 描述了从用户提示到输出解释的连续步骤,包括受众检测和校准。

AI 提取 · 证据约束

左右滑动查看完整图示

ELI5 技能工作流程README 描述了从用户提示到输出解释的连续步骤,包括受众检测和校准。包含受众确定参数应用调整用户提示输入检测受众处理校准风格处理生成解释输出
图示依据
  • • How It Works 章节描述检测受众并校准词汇、类比、语气等
  • • Usage Examples 展示用户提示包含受众如'my manager'
  • • Supported Audiences 表格列出可检测的受众类型
五维表现
ELI5 解决真实需求:按受众调整解释风格,目标用户与场景明确,示例具体。文档清晰,安装步骤简单,但缺少输入参数与限制说明,设计证据不足。
质量证据
  • Supported Audiences 表格列出年龄、年级、职位、关系类别
  • Usage Examples 提供具体命令如'ELI5 what a database index is'
  • Installation 章节给出 git clone 和 cp 命令
  • Evaluations 章节展示运行脚本和示例输出
  • License 章节声明 MIT
采用建议
优势
  • 问题与用途描述
  • 有效 README
  • 安装或接入步骤
  • 可执行示例
  • 未发现已知高风险模式
关注点
  • 缺少输入、参数或工具说明
  • 缺少限制、权限或边界
  • 未说明输入格式或参数,如如何指定受众
  • 未列出限制或边界,如不支持的语言或主题
  • 设计部分仅描述校准维度,无具体实现细节
适合

需要向非技术受众解释技术概念的场景、教育或培训中简化复杂主题、跨部门沟通,如向经理或设计师解释、家长向孩子解释技术概念

不建议直接用于

需要精确技术文档或规范说明的场景、非英语语言环境(未提及多语言支持)

也有自己的公开项目?先看完证据,再用当前规则生成独立报告。

评测我的项目 →
文档证据
77/100
问题与用途描述10 分
有效 README12 分
安装或接入步骤14 分
可执行示例16 分
输入、参数或工具说明11 分
输出或结果说明9 分
限制、权限或边界12 分
错误处理或排障8 分
许可证信息5 分
结构化章节3 分
安全证据
低风险
未发现已知高风险模式

静态扫描不是安全保证,生产接入前仍应人工复核权限和数据边界。

优先改进清单
  1. 01补充输入、参数或工具说明
  2. 02补充限制、权限或边界
方法、证据与局限展开
数据来源

GitHub Repository API

扫描范围

2 个文件 · 13,083 字符

评测引擎

v3.10.0 · AI 复核已启用(deepseek-chat)

局限
  • 静态评测不会安装或执行项目代码
  • 安全扫描基于高信号文件与已知模式,不能替代人工审计
  • 流行度只反映采用程度,不代表安全或工程质量

30 天热度趋势

README

ELI5 — Explain Like I Am 5

A Claude Code skill that explains anything to anyone — kids, managers, engineers, parents. It adapts tone, vocabulary, analogies, and framing to match the audience.

Ever needed to explain a technical concept to your manager? Or break down code for a 5th grader? ELI5 makes Claude automatically adjust its explanation style based on who's listening.

Read the full blog post on how this skill was built: Building an ELI5 Skill for Claude

Supported Audiences

CategoryExamples
Ages5, 10, 15, 20, 30, 40+
Grade Levels5th grade, Middle school, Senior High, College, Graduate school
Job RolesManager, Engineer, Designer, Director, Product Manager
RelationshipsWife, Husband, Parents, Kids, Friend

Usage Examples

ELI5 what a database index is
Explain this code to my manager
Break down how git merge conflicts work for a 5th grader
Explain this error to my mom
Simplify this for a designer

How It Works

The skill detects the target audience from your prompt and calibrates:

  • Vocabulary — no jargon for kids, proper terminology for engineers
  • Analogies — toys and playground for age 5, business outcomes for managers
  • Tone — playful for children, professional for directors, warm for family
  • Depth — short and sweet for simple audiences, nuanced for grad students
  • Framing — impact/risk for managers, UX for designers, architecture for engineers

Installation

Copy the skill into your Claude Code skills directory:

git clone https://github.com/DreambigOu/ELI5.git
cp -r ELI5/skills/eli5 ~/.claude/skills/eli5

Then use it in Claude Code by saying things like "ELI5 this" or "explain this to my manager."

Evaluations

Read the full guide on how the eval system works: How to Evaluate a Claude Code Skill

Adding a New Test Case

Test cases are defined in eli5-workspace/evals.json. Add a new entry to the evals array:

{
  "id": 3,
  "name": "explain-recursion-teenager",
  "prompt": "Explain recursion like I'm 15",
  "audience": "Age 15",
  "assertions": [
    "Uses social media, gaming, or phone references as analogies",
    "Tone is casual but not cringey — no 'fellow kids' energy",
    "Correctly explains the concept of a function calling itself",
    "Mentions a base case or stopping condition"
  ]
}

Each test case needs:

  • A prompt — what the user would say to Claude
  • A name — directory-friendly identifier for storing results
  • Assertions — specific, verifiable criteria to grade against (4 per test works well)

Running Evaluations

Prerequisites: Claude Code CLI installed and the skill installed at ~/.claude/skills/eli5/.

# Run all tests + auto-grade with pass/fail
python eli5-workspace/run-evals.py

# Run a single test
python eli5-workspace/run-evals.py --test=1

# Skip baseline, only test the skill
python eli5-workspace/run-evals.py --with-skill-only

# Grade existing outputs without re-running tests
python eli5-workspace/run-evals.py --grade-only

The script does three things:

  1. Runs each prompt twice — once with the skill, once without (baseline)
  2. Auto-grades every output against its assertions using Claude
  3. Prints a pass rate summary comparing skill vs baseline

Example output:

--- Test 1: explain-db-index-age5 ---
  [with skill]
    PASS  #1 — No technical jargon present
    PASS  #2 — Uses book/page analogy and toy/messy room analogy
    PASS  #3 — Sentences are short and conversational
    PASS  #4 — Warm, enthusiastic tone with "huuuge", "super duper fast"
  [baseline]
    PASS  #1 — No technical jargon found
    FAIL  #2 — Uses phone book analogy, not child-friendly
    PASS  #3 — Sentences are generally short
    FAIL  #4 — Tone is informative but encyclopedic

=========================================
  PASS RATE SUMMARY — Iteration 1
=========================================
  With Skill:    10/12 passed (83.3%)
  Without Skill: 5/12 passed (41.6%)
  Delta:         41.7%
=========================================

Results are saved to eli5-workspace/iteration-N/, auto-incrementing with each run. Each test case produces grading.txt files with detailed evidence.

Current Results

See eli5-workspace/eval-results.md for the full evaluation strategy and detailed grading.

MetricWith SkillWithout SkillDelta
Pass Rate83.3%41.6%+41.7%

The biggest improvement is in audience-specific framing — especially for non-technical audiences like managers (0% baseline to 50% with skill).

Contributing

PRs welcome! Ideas for improvement:

  • Add more audience types (e.g., CEO, intern, journalist)
  • Add non-English language support
  • Improve evaluation coverage with more test cases

License

MIT