القائمة
Use when searching or explaining InternAgent job discovery, recommendation, match scoring, source labels, query expansion, city filters, or missing skills.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when inspecting AI assistant file-based long-term memory, session JSONL archives, soft consolidation, Dream updates, or assistant context isolation.
Use when running Agent golden-case evaluation, checking intent accuracy, tool-call accuracy, RAG grounding, job matching, interview follow-up policy, or resume rubric compliance.
Use when reading saved jobs, application status counts, manual follow-up notes, or the user's application tracking state.
Use when inspecting FastAPI routes, controllers, backend service boundaries, repository-backed endpoints, API surface, or route availability.
Use when inspecting or explaining Qingcheng AI chat complexity classification, prompt-template injection, SupervisorAgent routing, selected tool allowlists, or agent planning for a user message.
Use when changing or reviewing the Next.js frontend UI, Tailwind layout, chat/interview pages, resume/job/application screens, or frontend API wrappers.
| name | job-search-tool |
| description | Use when searching or explaining InternAgent job discovery, recommendation, match scoring, source labels, query expansion, city filters, or missing skills. |
Use this skill to run the project job discovery pipeline through JobService.discover_jobs. It reuses repository search, taxonomy, dedupe, recommendation scoring, and resume-aware matching when a user is supplied.
Run inside the API container:
docker compose exec api python /app/skills/job-search-tool/scripts/discover_jobs.py --keyword Java --city 北京 --experience intern --email admin@example.com
Inputs: --keyword, --city, --experience, --skills, --limit, optional --user-id or --email.
The script emits compact JSON: total, source_kind, fallback_notice, query_expansions, and jobs[] with title, company, city, source, URL, recommendation score, matched/missing skills, and priority. It never claims a static fallback is live market evidence.
Present only returned jobs. Mention source and fallback notices. For resume-aware results, explain matched and missing skills. Never say the system submitted an application.
python skills/job-search-tool/scripts/discover_jobs.py --self-test
python skills/job-search-tool/scripts/discover_jobs.py --help
docker compose exec api python /app/skills/job-search-tool/scripts/discover_jobs.py --keyword 产品 --city 北京 --limit 5