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candidate-evaluation

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Original

English
🇨🇳

Translation

Chinese

Candidate Evaluation Skill

候选人评估技能

Evaluate GitHub contributors for engineering roles at Pollinations.
针对Pollinations公司的工程岗位评估GitHub贡献者。

When to Use

适用场景

  • User asks to evaluate a contributor or candidate
  • User wants to research GitHub profiles for hiring
  • User needs to update CONTRIBUTORS.md with candidate analysis
  • User mentions "hiring", "candidate", "MLOps", or "evaluate contributor"
  • 用户要求评估某贡献者或候选人
  • 用户为招聘需求调研GitHub个人资料
  • 用户需要将候选人分析内容更新至CONTRIBUTORS.md
  • 用户提及“招聘”“候选人”“MLOps”或“评估贡献者”

Evaluation Criteria

评估标准

Must-Have Skills (Weight: High)

必备技能（权重：高）

  • Python: Primary language proficiency
  • DevOps: Docker, CI/CD, infrastructure
  • GPU/ML Deployment: Model serving, inference optimization
  • Python：核心语言熟练度
  • DevOps：Docker、CI/CD、基础设施相关能力
  • GPU/ML Deployment：模型部署、推理优化能力

Nice-to-Have Skills (Weight: Medium)

加分技能（权重：中）

  • Kubernetes, vLLM, TGI
  • Quantization (GGUF, ONNX)
  • CI/CD pipelines (GitHub Actions)
  • Kubernetes, vLLM, TGI
  • 模型量化（GGUF、ONNX）
  • CI/CD流水线（GitHub Actions）

Work Style Indicators (Weight: Medium)

工作风格指标（权重：中）

  • PR size preference (small, focused = good)
  • Response time to reviews
  • Documentation quality
  • Test coverage habits
  • PR规模偏好（小型、聚焦的PR更佳）
  • 评审回复时效
  • 文档质量
  • 测试覆盖习惯

Evaluation Process

评估流程

  1. Gather Data via GitHub MCP or 
    gh api
    :
    bash
    # Get user repos
gh api users/{username}/repos --jq '.[].name'

# Search PRs in pollinations
gh api search/issues -X GET -f q='repo:pollinations/pollinations author:{username}'

# Search code for MLOps keywords
gh api search/code -X GET -f q='user:{username} docker OR kubernetes OR gpu OR vllm'
  2. Analyze Repositories for:
    • ML/AI projects (ComfyUI, HuggingFace, PyTorch)
    • DevOps tooling (Docker, CI/CD, scripts)
    • API/backend experience
    • Star counts and activity
  3. Check Pollinations Contributions:
    • Merged PRs (high signal)
    • Open issues/discussions
    • Project submissions
  4. Generate Profile with:
    • Fit score (1-10)
    • Strengths (bullet points)
    • Weaknesses (bullet points)
    • Key repositories table
    • Hiring recommendation
  1. 数据收集：通过GitHub MCP或
    gh api
    工具获取：
    bash
    # Get user repos
gh api users/{username}/repos --jq '.[].name'

# Search PRs in pollinations
gh api search/issues -X GET -f q='repo:pollinations/pollinations author:{username}'

# Search code for MLOps keywords
gh api search/code -X GET -f q='user:{username} docker OR kubernetes OR gpu OR vllm'
  2. 仓库分析：重点关注以下内容：
    • ML/AI项目（ComfyUI、HuggingFace、PyTorch）
    • DevOps工具（Docker、CI/CD、脚本）
    • API/后端开发经验
    • 星标数量与活跃程度
  3. Pollinations贡献核查：
    • 已合并的PR（参考价值高）
    • 开放的议题/讨论
    • 项目提交记录
  4. 生成评估报告：包含以下模块：
    • 匹配度得分（1-10分）
    • 优势（项目符号列表）
    • 劣势（项目符号列表）
    • 核心仓库表格
    • 招聘建议

Output Format

输出格式

Use ASCII box art for visual appeal:
┌─────────────────────────────────────────────────────────────────────────────┐
│  FIT: X.X/10  │  GitHub: username  │  Repos: N  │  Focus: Area             │
└─────────────────────────────────────────────────────────────────────────────┘
✅ STRENGTHS
  • Point 1
  • Point 2
❌ WEAKNESSES
  • Point 1
  • Point 2
📦 KEY REPOS
RepoTechWhat It Does
🎯 VERDICT: Recommendation
使用ASCII边框提升视觉效果：
┌─────────────────────────────────────────────────────────────────────────────┐
│  FIT: X.X/10  │  GitHub: username  │  Repos: N  │  Focus: Area             │
└─────────────────────────────────────────────────────────────────────────────┘
✅ 优势
  • 要点1
  • 要点2
❌ 劣势
  • 要点1
  • 要点2
📦 核心仓库
仓库名称技术栈功能描述
🎯 结论：招聘建议

Skills Matrix Format

技能矩阵格式

╔═══════════════════╦════════╦════════╦════════╦═══════════════╗
║     CANDIDATE     ║ Python ║ GPU/ML ║ Docker ║   FIT SCORE   ║
╠═══════════════════╬════════╬════════╬════════╬═══════════════╣
║ username          ║ █████  ║ ███    ║ ████   ║     X.X/10    ║
╚═══════════════════╩════════╩════════╩════════╩═══════════════╝

Legend: █ = Skill Level (1-5)
╔═══════════════════╦════════╦════════╦════════╦═══════════════╗
║     CANDIDATE     ║ Python ║ GPU/ML ║ Docker ║   FIT SCORE   ║
╠═══════════════════╬════════╬════════╬════════╬═══════════════╣
║ username          ║ █████  ║ ███    ║ ████   ║     X.X/10    ║
╚═══════════════════╩════════╩════════╩════════╩═══════════════╝

说明: █ = 技能等级 (1-5)

Reference Files

参考文件

  • AGENTS.md
    - Project guidelines and contributor attribution
  • AGENTS.md
    - 项目指南与贡献者归属说明

Example Queries

示例查询

  • "Evaluate @username for MLOps role"
  • "Research GitHub profile for {username}"
  • "Add {username} to CONTRIBUTORS.md"
  • "Compare candidates X and Y"
  • "评估@username是否适配MLOps岗位"
  • "调研{username}的GitHub个人资料"
  • "将{username}添加至CONTRIBUTORS.md"
  • "对比候选人X与Y"