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candidate-evaluation
by pollinations
Your Friendly Open-Source Gen-AI Platform
⭐ 3,819🍴 583📅 Jan 23, 2026
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Use Cases
🧠
AI Model Integration
Integrate LLM and ML models into your application.
✨
Prompt Optimization
Improve prompts for better results.
📊
Automated Data Analysis
AI-powered data analysis and insight extraction.
SKILL.md
name: candidate-evaluation description: Evaluate GitHub contributors for MLOps/engineering roles. Use when analyzing candidates, researching GitHub profiles, or updating CONTRIBUTORS.md with hiring assessments. allowed-tools: "Read, Write, Edit, Grep, Bash(gh api:), Bash(git:)"
Candidate Evaluation Skill
Evaluate GitHub contributors for engineering roles at Pollinations.
When to Use
- 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"
Evaluation Criteria
Must-Have Skills (Weight: High)
- Python: Primary language proficiency
- DevOps: Docker, CI/CD, infrastructure
- GPU/ML Deployment: Model serving, inference optimization
Nice-to-Have Skills (Weight: Medium)
- Kubernetes, vLLM, TGI
- Quantization (GGUF, ONNX)
- CI/CD pipelines (GitHub Actions)
Work Style Indicators (Weight: Medium)
- PR size preference (small, focused = good)
- Response time to reviews
- Documentation quality
- Test coverage habits
Evaluation Process
-
Gather Data via GitHub MCP or
gh api:# 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' -
Analyze Repositories for:
- ML/AI projects (ComfyUI, HuggingFace, PyTorch)
- DevOps tooling (Docker, CI/CD, scripts)
- API/backend experience
- Star counts and activity
-
Check Pollinations Contributions:
- Merged PRs (high signal)
- Open issues/discussions
- Project submissions
-
Generate Profile with:
- Fit score (1-10)
- Strengths (bullet points)
- Weaknesses (bullet points)
- Key repositories table
- Hiring recommendation
Output Format
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
| Repo | Tech | What It Does |
|---|
🎯 VERDICT: Recommendation
Skills Matrix Format
╔═══════════════════╦════════╦════════╦════════╦═══════════════╗
║ CANDIDATE ║ Python ║ GPU/ML ║ Docker ║ FIT SCORE ║
╠═══════════════════╬════════╬════════╬════════╬═══════════════╣
║ username ║ █████ ║ ███ ║ ████ ║ X.X/10 ║
╚═══════════════════╩════════╩════════╩════════╩═══════════════╝
Legend: █ = Skill Level (1-5)
Reference Files
AGENTS.md- Project guidelines and contributor attribution
Example Queries
- "Evaluate @username for MLOps role"
- "Research GitHub profile for {username}"
- "Add {username} to CONTRIBUTORS.md"
- "Compare candidates X and Y"
Score
Total Score
80/100
Based on repository quality metrics
✓SKILL.md
SKILL.mdファイルが含まれている
+20
✓LICENSE
ライセンスが設定されている
+10
○説明文
100文字以上の説明がある
0/10
✓人気
GitHub Stars 1000以上
+15
✓最近の活動
1ヶ月以内に更新
+10
✓フォーク
10回以上フォークされている
+5
○Issue管理
オープンIssueが50未満
0/5
✓言語
プログラミング言語が設定されている
+5
✓タグ
1つ以上のタグが設定されている
+5
Reviews
💬
Reviews coming soon


