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Thu, October 1511:15ToolsAI codingOpen sourceAI coding guide

career-ops: An open-source, locally-run AI job-hunting agent that filters roles and tailors resumes

Decision Brief

What changedThe open-source project career-ops has launched on GitHub, a job-hunting agent that runs inside local AI command-line tools, scanning job boards, grading roles A to F, tailoring resumes, and tracking applications.
Why it mattersIt puts job filtering and resume tailoring into the AI command line developers already use, running locally so job data never leaves the machine—an attempt to engineer the job hunt for developers tired of low-yield mass applications and worried about privacy.
Who should careAI coding tool users
Affected stackClaude CodeOpenAIGemini
Builder actionUpdate Claude Code to the latest version
Source confidenceHigh · Official release / blog / repo

Released by developer santifer, career-ops initializes with a single command, npx @santifer/career-ops init, and runs in AI CLIs such as Claude Code, Gemini, Codex, and OpenCode. It grades roles A to F, covering whether a posting is still open, resume match, salary research, and interview prep; it also includes ATS-compatible PDF resume generation, cover letter generation, contact discovery, and application email drafts. Everything runs locally with no resume data uploaded, no email-sending code, and no automatic submission—users always click submit themselves. It also offers job portal scanning across 100+ companies, rejection pattern analysis, and an application tracking dashboard. For developers using AI CLIs, career-ops embeds the job hunt into the daily dev environment, with role evaluation and resume tailoring done in the same terminal. For the privacy-conscious, resume and application data go only from the local machine to the AI provider the user chooses; the project itself has no backend. Before use, note that PDF generation depends on Playwright and Chromium, and under a proxy you must set CAREER_OPS_TRUST_PROXY_EGRESS=1 and ensure the proxy blocks access to internal addresses. First-run setup is done through conversation, with no manual file editing needed.

Summary basis: full article readCompiled from the source scope noted above; the original remains authoritative.

Sources

  • Skill Radar(GitHub 趋势)

    Trending hands-on MCP servers, agent skills, and AI-coding tools discovered from GitHub search momentum.

  • GitHub:santifer/career-ops

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