51mee Resume Match — 技能 — openclaw中文资讯站

技能详情（站内镜像，无评论）

人岗匹配。触发场景：用户要求匹配简历和职位；用户问这个候选人适合这个职位吗；用户要筛选最匹配的候选人。

通信与消息

作者：51mee @51mee-com

许可证：MIT-0

MIT-0 ·免费使用、修改和重新分发。无需归因。

版本：v1.2.1

统计：⭐ 0 · 129 · 0 current installs · 0 all-time installs

⭐ 0

安装量（当前） 0

🛡 VirusTotal ：良性 · OpenClaw ：良性

Package：51mee-com/51mee-resume-match

安全扫描（ClawHub）

VirusTotal ：良性
OpenClaw ：良性

OpenClaw 评估

The skill's requests and runtime instructions are coherent with its stated purpose (resume↔JD matching), but it instructs sending potentially sensitive resumes to an external API and omits any authentication details or vendor documentation — review privacy and auth before use.

目的

Name/description (人岗匹配) align with the SKILL.md: it documents calling an external match API with a resume file and job description to produce scores and a report. No unrelated binaries, env vars, or config paths are requested.

说明范围

Instructions are narrowly scoped to POSTing a resume file and jd_text to https://openapi.51mee.com/api/v1/parse/match and to format the returned JSON into a report. However, the skill explicitly instructs uploading resumes (sensitive personal data) to an external endpoint and gives no guidance about consent, redaction, or privacy. It also omits any authentication steps (API key/token) which is unusual for an external API.

安装机制

Instruction-only skill with no install spec or code to write to disk. Lowest install risk; nothing will be downloaded or installed by the skill itself.

证书

The skill requests no environment variables or credentials, which is coherent with the manifest. That said, many SaaS APIs require an API key — the absence of any declared auth is noteworthy. If the real API actually requires credentials, the skill's manifest is incomplete and could cause silent failures or unexpected unauthenticated requests.

持久

always is false and there are no claims of modifying other skills or system configs. The skill does not request persistent platform privileges.

综合结论

This skill appears to do what it says (call a matching API with a resume and job description) but before installing consider: (1) Privacy: resumes contain personal data — check whether you have candidate consent and whether sending resumes to https://openapi.51mee.com is acceptable. (2) Authentication: the documentation example shows no API key; verify whether the real API requires credentials and where to store them securely. (3) Vendor vetti…

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「51mee Resume Match」。简介：人岗匹配。触发场景：用户要求匹配简历和职位；用户问这个候选人适合这个职位吗；用户要筛选最匹配的候选人。。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/51mee-com/51mee-resume-match/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
name: 51mee-resume-match
description: 人岗匹配。触发场景：用户要求匹配简历和职位；用户问这个候选人适合这个职位吗；用户要筛选最匹配的候选人。
---

# 人岗匹配技能

## 功能说明

评估候选人与职位的匹配程度，生成匹配度评分和分维度分析。

## API 调用

**接口地址**: `https://openapi.51mee.com/api/v1/parse/match`

**请求方式**: POST (multipart/form-data)

**参数**:
- `file`: 简历文件（必填）
- `jd_text`: 职位描述文本（必填）

**调用命令**:
```bash
curl -X POST "https://openapi.51mee.com/api/v1/parse/match" 
  -F "file=@候选人简历.pdf" 
  -F "jd_text=岗位职责:n1. 负责系统架构设计nn任职要求:n- 5年以上Java开发经验n- 熟悉Spring Boot"
```

## 返回数据结构

```json
{
  "code": 0,
  "message": "success",
  "data": {
    "overall_score": 85,
    "overall_level": "良好",
    "star_rating": 4,
    
    "dimensions": {
      "skill_match": {
        "score": 90,
        "level": "优秀",
        "matched_skills": ["Java", "Spring Boot", "MySQL"],
        "missing_skills": ["Docker", "K8s"],
        "details": "核心技能完全匹配"
      },
      "experience_match": {
        "score": 85,
        "level": "良好",
        "required_years": 5,
        "actual_years": 6,
        "industry_match": true,
        "details": "经验年限符合要求"
      },
      "education_match": {
        "score": 95,
        "level": "优秀",
        "required": "本科",
        "actual": "本科",
        "details": "学历符合要求"
      },
      "salary_match": {
        "score": 70,
        "level": "一般",
        "budget_range": "20K-25K",
        "expected_range": "25K-30K",
        "details": "期望薪资略高于预算"
      }
    },
    
    "advantages": [
      "技术栈高度匹配，Java/Spring/MySQL 都有实战经验",
      "有大型项目经验，处理过高并发场景",
      "职业发展路径清晰，稳定性好"
    ],
    
    "gaps": [
      "缺少容器化经验（Docker/K8s）",
      "期望薪资 25K，略高于预算 20K"
    ],
    
    "risks": [
      "最近一份工作时间较短（8个月）"
    ],
    
    "interview_suggestions": [
      "重点考察高并发项目细节",
      "了解跳槽原因",
      "评估容器化技术学习能力"
    ],
    
    "recommendation": {
      "should_interview": true,
      "confidence": 85,
      "reason": "综合素质优秀，技术匹配度高，值得深入沟通"
    }
  }
}
```

## 匹配维度说明

| 维度 | 字段 | 权重 | 说明 |
|------|------|------|------|
| 技能匹配 | `skill_match` | 高 | 技术栈是否对口 |
| 经验匹配 | `experience_match` | 高 | 工作年限、行业背景 |
| 学历匹配 | `education_match` | 中 | 教育背景 |
| 薪资匹配 | `salary_match` | 视情况 | 期望与预算对比 |

## 评分等级

| 分数 | 等级 | 星级 |
|------|------|------|
| 90-100 | 优秀 | ⭐⭐⭐⭐⭐ |
| 75-89 | 良好 | ⭐⭐⭐⭐ |
| 60-74 | 一般 | ⭐⭐⭐ |
| 0-59 | 较差 | ⭐⭐ |

## 输出模板

```markdown
## 候选人匹配报告

**候选人**: [姓名]
**综合匹配度**: [score]/100 ⭐⭐⭐⭐

### 分维度评估
| 维度 | 得分 | 评级 | 说明 |
|------|------|------|------|
| 技能匹配 | [score] | [level] | [details] |
| 经验匹配 | [score] | [level] | [details] |
| 学历匹配 | [score] | [level] | [details] |
| 薪资匹配 | [score] | [level] | [details] |

### 核心优势 ✅
- [advantage1]
- [advantage2]

### 需关注 ⚠️
- [gap1]
- [gap2]

### 面试建议
- [suggestion1]
- [suggestion2]

### 推荐
**[建议/不建议]面试** - [reason]
```

## 批量筛选流程

当用户要筛选多个候选人时：

1. **定义岗位要求** - 收集完整的职位描述
2. **逐个匹配** - 对每个候选人调用匹配接口
3. **对比排序** - 按 `overall_score` 排序
4. **输出报告** - 生成排名对比表

## 注意事项

- 必须同时提供简历文件和职位描述
- 职位描述越详细，匹配越准确
- 先检查返回的 `code` 字段
- 匹配结果是 AI 分析，最终决策需人工判断