Glmv Resume Screen logo

Glmv Resume Screen

Organization
zai-org
glmv-resume-screen

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.

Overview

Publisherzai-org
RepositoryGLM-skills
Skill nameglmv-resume-screen
Stars
476
Forks
45
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zai-org on GitHub. Read the source before you install it.

Installation

Install the Glmv Resume Screen AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zai-org/GLM-skills/tree/main/skills/glmv-resume-screen
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zai-org/GLM-skills.git /tmp/GLM-skills
mkdir -p .claude/skills
cp -r /tmp/GLM-skills/skills/glmv-resume-screen .claude/skills/glmv-resume-screen
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Glmv Resume Screen in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Glmv Resume Screen on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Glmv Resume Screen is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

GLM-V Resume Screening Skill

Batch-read resumes and screen candidates against your criteria using the ZhiPu GLM-V multimodal model.

When to Use

  • Filter/screen multiple resumes against specific criteria
  • User mentions "筛选简历", "评估候选人", "简历筛选", "resume screening", "filter candidates"
  • Compare candidates for a job position
  • Batch-evaluate job applications

Supported Input Types

TypeFormatsMax CountSource
Resume (URL)pdf, docx, txt50URL
Resume (Local)pdf onlypages ≤ 50 totalLocal path

Local PDF / 本地 PDF: Local PDF files are converted page-by-page into images (base64) before sending to the model. PyMuPDF is required (pip install PyMuPDF). 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 PyMuPDF(pip install PyMuPDF)。URL 文件支持 pdf/docx/txt 等全格式。

📋 Output Display Rules (MANDATORY)

After 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.

  • Show the full Markdown table (index, name, pass/fail, match level, reasoning)
  • If output was saved (-o), provide the file path and show file content
  • If screening output is empty, explain why

Resource Links

Prerequisites

API Key Setup / API Key 配置(Required / 必需)

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):

  1. OpenClaw config (recommended) / OpenClaw 配置(推荐): Set in openclaw.json under skills.entries.glmv-resume-screen.env:

    json
    "glmv-resume-screen": { "enabled": true, "env": { "ZHIPU_API_KEY": "你的密钥" } }
  2. Shell environment variable / Shell 环境变量: Add to ~/.zshrc:

    bash
    export ZHIPU_API_KEY="你的密钥"

💡 If you already configured another Zhipu skill (for example zhipu-tools or glmv-caption), they share the same ZHIPU_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。

How to Use

Basic Screening

bash
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"

Save as Markdown

bash
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

Save as JSON

bash
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

Custom System Prompt

bash
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"

Output Example

The model outputs a Markdown table like this:

markdown
| 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 |

CLI Reference

python scripts/resume_screen.py --files FILE [FILE...] --criteria CRITERIA [OPTIONS]
ParameterRequiredDescription
--files, -f✅Resume file URLs (pdf/docx/txt, URL only, max 50)
--criteria, -c✅Screening criteria text
--model, -mNoModel name (default: glm-4.6v)
--system-prompt, -sNoCustom system prompt (default: professional HR assistant)
--temperature, -tNoSampling temperature 0-1 (default: 0.3)
--max-tokensNoMax output tokens (default: 4096)
--output, -oNoSave result to file (.md for markdown, .json for JSON)
--prettyNoPretty-print JSON output

Error Handling

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Glmv Resume Screen AI skill do?

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.

Why use Glmv Resume Screen on TypingMind?

Because you install it once and use it with any model. Glmv Resume Screen is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Glmv Resume Screen in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zai-org/GLM-skills/tree/main/skills/glmv-resume-screen. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Glmv Resume Screen?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Glmv Resume Screen?

As many as you like. As long as a model supports skills, you can use Glmv Resume Screen with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Glmv Resume Screen AI skill free?

Yes. It is published on GitHub by zai-org under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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