wkjobs

wkjobs

Enables searching public LinkedIn job listings and managing a canonical WeKruit candidate profile, including LinkedIn-based login and resume upload, via command-line and MCP tools.

Category
访问服务器

README

wkjobs

Search public job listings from your terminal and connect your results to the same canonical WeKruit candidate profile used by wekruit-pa.

npx wkjobs search "founding engineer" --location "San Francisco"
npx wkjobs login                       # Continue with LinkedIn → WeKruit
npx wkjobs resume add ./resume.pdf --yes
claude mcp add wkjobs -- npx wkjobs mcp

What is implemented

  • anonymous LinkedIn guest job search with normalized JSON or a terminal table;
  • WeKruit device-login client that opens a WeKruit page and requires LinkedIn OIDC;
  • private local storage (mode 0600) for the scoped WeKruit token only;
  • PDF/DOCX content sniffing, 5 MiB cap, SHA-256 hashing, and canonical resume-upload client;
  • MCP tools: search_jobs, auth_status, and get_resume status;
  • deterministic mock backend and fixture-only tests.

The production device and resume endpoints require the corresponding wekruit-pa backend deployment. Until that is deployed, use the mock server:

npm install
npm run mock:api
WKJOBS_API_BASE=http://127.0.0.1:8787 npm run dev -- login

The browser page in the mock simulates LinkedIn consent and never contacts LinkedIn.

Commands

wkjobs search "<query>" [--location X] [--remote] [--hours 72] [--limit N]
wkjobs login                         Connect LinkedIn to a canonical WeKruit user
wkjobs connect                       Alias for login
wkjobs resume add <pdf-or-docx>      Store on that canonical WeKruit profile
wkjobs status                        Show account and resume status
wkjobs mcp                           Run the MCP server over stdio

Global flags: --json, --yes, and --quiet.

Identity and data model

wkjobs has no separate user database. Login is WeKruit authentication backed by LinkedIn OIDC. New people become normal WeKruit candidates; returning people resolve to their existing canonical candidate. A resume enters the existing private original → artifact → parsed resume → user tags/profile → Claire continuation pipeline.

The CLI never asks for a LinkedIn password and never receives a LinkedIn OAuth token.

wkjobs login offers a second, separate step: connecting your own LinkedIn session so results can be personalized. LinkedIn OIDC grants only openid profile email and no job-search API, so personalized results require your browser session rather than an API token. If you accept, wkjobs stores the li_at and JSESSIONID cookies from your own sign-in — plus the user agent that minted them — in ~/.wkjobs/credentials.json at mode 0600. They never leave your machine and are never sent to WeKruit. LinkedIn's terms do not permit automated access, so requests are paced conservatively; the risk to your account is small but not zero.

This step is always opt-in. Declining leaves you on public guest listings, which need no login. --no-linkedin skips it, non-interactive runs skip it by default (the generic --yes does not grant it), and wkjobs logout --linkedin deletes the stored session.

See the corrected plan and the production backend contract.

Data and privacy

search sends the search terms needed to retrieve public LinkedIn guest results. login sends a device-flow request to WeKruit and completes LinkedIn consent in the browser on a WeKruit-owned page. resume add clearly discloses that the document will be stored in WeKruit's canonical candidate profile and may be used by Claire for matching.

Local credentials live at ~/.wkjobs/credentials.json with mode 0600. Override the directory only with an absolute WKJOBS_HOME path. Set WKJOBS_API_BASE to select a development backend.

Development

npm install
npm run check
npm run dev -- search "typescript" --json

CI never logs into LinkedIn and never calls LinkedIn live endpoints; it uses recorded HTML fixtures.

License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选