Screen and evaluate resumes against criteria using ZhiPu GLM-V multimodal model. Reads multiple resume files (PDF/DOCX/TXT), compares against user-defined screening criteria, and outputs a Markdown table with pass/fail analysis. Use when the user wants to filter resumes, compare candidates, or batch-evaluate job applications.
Batch-read resumes and screen candidates against your criteria using the ZhiPu GLM-V multimodal model.
| Type | Formats | Max Count | Source |
|---|---|---|---|
| Resume (URL) | pdf, docx, txt | 50 | URL |
| Resume (Local) | pdf only | pages ≤ 50 total | Local path |
Local PDF / 本地 PDF: Local PDF files are converted page-by-page into images (base64) before sending to the model. is required (). URL files support full formats including pdf/docx/txt. 本地 PDF 会自动逐页转为图片（base64）传给模型，需要安装 （）。URL 文件支持 pdf/docx/txt 等全格式。
PyMuPDFpip install PyMuPDFPyMuPDFpip install PyMuPDFAfter running the script, you must display the complete screening result (Markdown table) exactly as returned. Do not summarize, truncate, or only say "screening completed". Users need each candidate's detailed analysis to decide.
-o), provide the file path and show file content| Resource | Link |
|---|---|
| Get API Key | https://bigmodel.cn/usercenter/proj-mgmt/apikeys |
| API Docs | Chat Completions / 对话补全 |
This script reads the key from the ZHIPU_API_KEY environment variable and shares it with other Zhipu skills.
脚本通过 ZHIPU_API_KEY 环境变量获取密钥，与其他智谱技能共用同一个 key。
Get Key / 获取 Key： Visit Zhipu Open Platform API Keys / 智谱开放平台 API Keys to create or copy your key.
Setup options / 配置方式（任选一种）：
OpenClaw config (recommended) / OpenClaw 配置（推荐）： Set in openclaw.json under skills.entries.glmv-resume-screen.env:
"glmv-resume-screen": { "enabled": true, "env": { "ZHIPU_API_KEY": "你的密钥" } }
Shell environment variable / Shell 环境变量： Add to ~/.zshrc:
export ZHIPU_API_KEY="你的密钥"
💡 If you already configured another Zhipu skill (for example
zhipu-toolsorglmv-caption), they share the sameZHIPU_API_KEY, so no extra setup is needed. 💡 如果你已为其他智谱 skill（如zhipu-tools、glmv-caption）配置过 key，它们共享同一个ZHIPU_API_KEY，无需重复配置。
python scripts/resume_screen.py \
--files "https://example.com/resume1.pdf" "https://example.com/resume2.docx" \
--criteria "3年以上工作经验，有Python开发经验，有大型项目管理经验"
python scripts/resume_screen.py \
--files "https://example.com/resume1.pdf" "https://example.com/resume2.docx" \
--criteria "本科以上学历，5年后端开发经验" \
--output result.md
python scripts/resume_screen.py \
--files "https://example.com/resume1.pdf" "https://example.com/resume2.docx" \
--criteria "有机器学习经验" \
--output result.json --pretty
python scripts/resume_screen.py \
--files "https://example.com/resume1.pdf" \
--criteria "前端开发岗位，3年经验" \
--system-prompt "你是一位资深技术面试官，特别关注候选人的项目深度和技术选型能力"
The model outputs a Markdown table like this:
| 序号 | 候选人姓名 | 是否符合 | 符合程度 | 原因分析 |
| ---- | ---------- | ----------- | -------- | ----------------------------------------------------------- |
| 1 | 张三 | ✅ 符合 | 高 | 5年后端经验，熟练使用Python和Django，主导过3个大型项目 |
| 2 | 李四 | ❌ 不符合 | 低 | 仅1年开发经验，主要使用Java，无Python经验 |
| 3 | 王五 | ⚠️ 部分符合 | 中 | 3年Python经验但无项目管理经验，技术栈匹配但缺乏大型项目经历 |
python scripts/resume_screen.py --files FILE [FILE...] --criteria CRITERIA [OPTIONS]
| Parameter | Required | Description |
|---|---|---|
--files, -f | ✅ | Resume file URLs (pdf/docx/txt, URL only, max 50) |
--criteria, -c | ✅ | Screening criteria text |
--model, -m | No | Model name (default: glm-4.6v) |
--system-prompt, -s | No | Custom system prompt (default: professional HR assistant) |
--temperature, -t | No | Sampling temperature 0-1 (default: 0.3) |
--max-tokens | No | Max output tokens (default: 4096) |
--output, -o | No | Save result to file (.md for markdown, .json for JSON) |
--pretty | No | Pretty-print JSON output |
API key not configured: → Guide user to configure ZHIPU_API_KEY
Authentication failed (401/403): → API key invalid/expired → reconfigure
Rate limit (429): → Quota exhausted → wait and retry
Local path provided: → Error: only URLs supported
Content filtered: → warning field present → content blocked by safety review
Timeout: → Resumes too large or too many → reduce file count