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developer-assessment-evaluator开发者评估评估员

Agent Skill

developer-assessment-evaluator 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

451

周安装

19

GitHub Stars

公开资料未说明

下载量

158
CodexClaudeCursorGemini CLI

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

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:developer-assessment-evaluator(开发者评估评估员)
来源仓库:https://github.com/clous-ai/agents
仓库路径:skills/developer-assessment-evaluator
安装命令:
npx skills add clous-ai/agents --skill "developer-assessment-evaluator"
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 npx skills 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。

AgentSkills.tonpx skills
npx skills add clous-ai/agents --skill "developer-assessment-evaluator"

简介

developer-assessment-evaluator 用于评估开发者能力与项目匹配度。

  • 适用于 Codex、Claude、Cursor、Gemini CLI 中辅助人才筛选与技术评审。
  • 通过 npx 从 GitHub 仓库安装后可调用评估模型输出结构化报告。
  • 注意模型可能存在偏差,建议人工复核关键结论。
  • 适合 HR 与技术团队联合使用,提升招聘与任务分配效率。

SKILL.md

name
developer-assessment-evaluator
description
This skill should be used when the user asks to "evaluate a coding assessment", "review take-home project", "score technical interview", "analyze coding challenge submission", "detect trivia over-indexing", or "provide assessment feedback". Evaluates engineering assessments against rubrics while detecting common anti-patterns like excessive focus on trivia over problem-solving ability.
version
0.1.0
license
MIT

Developer Assessment Evaluator

Evaluate technical assessments (take-homes, live coding, system design) using structured rubrics while detecting over-indexing on trivia vs. core engineering skills.

Purpose

Provide objective, fair evaluation of candidate technical work that:

  • Assesses problem-solving ability over memorization
  • Detects over-reliance on algorithmic trivia
  • Evaluates code quality, testing, documentation
  • Ensures consistent scoring across candidates
  • Identifies signal vs. noise in technical performance

When to Use

Invoke when:

  • Reviewing completed take-home assignments
  • Scoring live coding interviews
  • Evaluating system design discussions
  • Detecting assessment anti-patterns
  • Training interviewers on evaluation criteria
  • Calibrating scoring across interview team

Core Evaluation Framework

1. Problem-Solving Process (40%)

Assess HOW candidate approaches problem:

  • Asks clarifying questions before coding
  • Breaks problem into manageable steps
  • Considers edge cases and constraints
  • Iterates when hitting obstacles
  • Explains thought process clearly

Scoring:

  • 5: Methodical approach, clear reasoning, handles ambiguity well
  • 3: Adequate approach, some structure
  • 1: Jumps to code without planning, struggles with unknowns

Anti-Pattern Detection: If candidate knows optimal algorithm immediately → may be memorized, probe deeper with follow-up

2. Code Quality (30%)

Evaluate production-readiness:

  • Readable variable/function names
  • Appropriate abstractions
  • No obvious bugs or edge case failures
  • Handles errors gracefully
  • Follows language idioms

Scoring:

  • 5: Production-ready code, well-organized
  • 3: Functional code, some quality issues
  • 1: Poor structure, hard to maintain

3. Testing & Validation (20%)

Check verification approach:

  • Writes test cases (unit, integration)
  • Tests edge cases (empty input, large input, null)
  • Validates assumptions
  • Handles error conditions

Scoring:

  • 5: Comprehensive tests, edge cases covered
  • 3: Basic tests present
  • 1: No testing or validation

4. Communication (10%)

Assess explanation clarity:

  • Explains design decisions
  • Articulates trade-offs
  • Responds to feedback
  • Documents approach

Scoring:

  • 5: Clear, thorough explanations
  • 3: Adequate communication
  • 1: Unclear or defensive

Detecting Trivia Over-Indexing

Warning Signs:

  • Assessment requires obscure algorithm knowledge
  • Solution depends on memorizing specific pattern
  • Time pressure favors memorization over problem-solving
  • No partial credit for good process but wrong algorithm

Example - Bad Assessment: "Implement Dijkstra's algorithm from memory in 45 minutes" → Tests memorization, not problem-solving

Example - Good Assessment: "Design a route-finding system for our delivery app. Consider real-world constraints." → Tests applied problem-solving

Rebalancing:

  • Allow candidates to look up algorithms
  • Value process over perfect solution
  • Give hints/guidance during interview
  • Accept multiple valid approaches

Evaluation Rubric Template

{
  "candidate": "Name",
  "assessment_type": "take-home|live-coding|system-design",
  "evaluation": {
    "problem_solving": {
      "score": 4,
      "weight": 0.40,
      "evidence": "Methodical approach, asked good clarifying questions, handled edge cases"
    },
    "code_quality": {
      "score": 3,
      "weight": 0.30,
      "evidence": "Functional code, some naming could be clearer"
    },
    "testing": {
      "score": 5,
      "weight": 0.20,
      "evidence": "Comprehensive unit tests, tested edge cases thoroughly"
    },
    "communication": {
      "score": 4,
      "weight": 0.10,
      "evidence": "Clear explanations, good documentation"
    }
  },
  "weighted_score": 4.0,
  "recommendation": "Strong Hire",
  "feedback": "Excellent problem-solving and testing. Could improve variable naming.",
  "trivia_concerns": false
}

Providing Constructive Feedback

Feedback Structure:

## Strengths
- [Specific strength with example]
- [Specific strength with example]

## Areas for Growth
- [Constructive suggestion with example]
- [Constructive suggestion with example]

## Overall Assessment
[Summary and recommendation]

Best Practices:

  • Be specific (cite code examples)
  • Balance positive and constructive
  • Focus on behaviors, not person
  • Suggest improvements, don't just criticize

Using Supporting Resources

Templates

  • templates/rubric-template.json - Assessment rubric schema
  • templates/feedback-template.md - Candidate feedback structure

References

  • references/anti-patterns.md - Common assessment anti-patterns
  • references/trivia-vs-skills.md - Distinguishing memorization from ability

Scripts

  • scripts/detect-trivia.py - Analyze assessment for trivia over-indexing
  • scripts/score-assessment.py - Calculate weighted scores

Progressive Disclosure: Detailed anti-patterns, trivia detection techniques, and feedback examples in references/.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

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

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

Claude Code

26.9%
按下载量换算43

Codex

22.3%
按下载量换算35

Gemini CLI

17.19%
按下载量换算27

OpenCode

11.4%
按下载量换算18

Antigravity

7.55%
按下载量换算12

windsurf

3.14%
按下载量换算5

安全审计

暂无安全审计结果可展示。

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。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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