Skill: meteoryf/ai-job-hunter-pro (version 1.3.0)

--- name: ai-job-hunter-pro description: AI-powered job search assistant with RAG-based resume-JD matching, automated application pipeline, and status tracking. Use when the user wants to search for jobs matching their resume, auto-apply to positions, track application status, generate tailored cover letters, or analyze their job search funnel. Trigger phrases include "find jobs for me", "match my resume to jobs", "auto-apply", "track my applications", "job search report", "optimize my resume for this job". metadata: openclaw: emoji: "🎯" requires: bins: ["python3"] --- # AI Job Hunter Pro Intelligent job search assistant with RAG-based semantic matching, automated applications, and data-driven tracking. ## Setup (first-time only) Run the setup script to install dependencies and initialize the vector database: ```bash cd {SKILL_DIR} pip install -r scripts/requirements.txt python3 scripts/setup_rag.py --init ``` Then create your profile: ```bash cp assets/profile_template.json ~/job_profile.json # Edit ~/job_profile.json with your info ``` Import your resume (PDF or DOCX): ```bash python3 scripts/rag_engine.py --import-resume ~/path/to/resume.pdf ``` ## Core Workflows ### Workflow 1: Smart Job Search (RAG Matching) When user says "find jobs for me" or "match my resume": 1. Load user profile from `~/job_profile.json` 2. Run RAG matching engine: ```bash python3 {SKILL_DIR}/scripts/rag_engine.py \ --mode search \ --platforms linkedin,boss \ --min-score 0.75 \ --max-results 20 ``` 3. Present results sorted by match score 4. For each job, show: title, company, match score, top matching skills, missing skills 5. Ask user which jobs to apply to, or auto-apply if configured ### Workflow 2: Auto-Apply Pipeline When user says "apply to these jobs" or "auto-apply": 1. For each selected job: ```bash python3 {SKILL_DIR}/scripts/apply_pipeline.py \ --job-id <id> \ --mode dry-run \ --generate-cover-letter \ --optimize-ats ``` 2. In dry-run mode: show generated cover letter and ATS-optimized resume highlights for review 3. After user confirms, switch to `--mode submit` 4. Log result to tracker database ### Workflow 3: Application Tracking When user says "track my applications" or "job search report": ```bash python3 {SKILL_DIR}/scripts/tracker.py --report daily ``` Status flow: Discovered → Applied → Screening → Interview → Offer / Rejected ### Workflow 4: Feedback Loop When user says "I like this job" or "not interested": ```bash python3 {SKILL_DIR}/scripts/rag_engine.py \ --mode feedback \ --job-id <id> \ --signal like|dislike ``` This adjusts the RAG query vectors to improve future recommendations. ## Rules - Always start in dry-run mode. Never submit applications without explicit user confirmation. - Respect platform rate limits: max 20 applications per day across all platforms. - Never misrepresent the user's qualifications in cover letters or applications. - Store all data locally. Never send resume data to external services other than the job platforms themselves. - When a platform returns an error or blocks access, report it clearly and suggest manual fallback. - Always show the match score and reasoning before applying. ## Configuration User config lives at `~/job_profile.json`. Skill config in OpenClaw: ```json { "skills": { "ai-job-hunter-pro": { "enabled": true, "profile_path": "~/job_profile.json", "default_platforms": ["linkedin", "boss"], "max_daily_applications": 20, "min_match_score": 0.75, "require_confirmation": true, "dry_run": true } } } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)