Tag

#resume

17 results found

Ai Hr Management Toolkit
@XJTLUmedia

You have 50 resumes to screen. Your AI assistant can reason about candidates — but it cannot open PDFs, extract structured data, or track pipeline stages. This toolkit bridges that gap. Give your AI assistant 24 tools covering the entire hiring workflow: Parse PDFs, DOCX, TXT, Markdown, and URLs into structured JSON Extract skills, experience, keywords, and entities algorithmically Score and rank candidates against job descriptions Run a full ATS: jobs, candidates, interviews, offers, notes, and analytics 23 of 24 tools are 100% algorithmic — no LLM calls, no API keys required. The AI calls tools, interprets the results, and delivers analysis. You just ask questions. All 24 MCP Tools All tools return structured JSON with next_steps hints so the AI knows what to call next. Resume Parsing & Ingestion Tool What it does AI? parse_resume Parse PDF / DOCX / TXT / MD / URL → raw text + contacts, keywords, section map No batch_parse_resumes Parse up to 20 files in one call, full pipeline on each No inspect_pipeline Run the 5-stage analysis pipeline → confidence scores, entity counts, data quality report No Text Analysis & NLP Tool What it does AI? extract_keywords TF-IDF keyword + bigram extraction with NER entity classification No detect_patterns Find date ranges, dollar/percent metrics, team sizes, section boundaries, career trajectory signals No classify_entities NER with 12 entity types (PERSON, ORG, SKILL, JOB_TITLE, LOCATION, DATE, …) + context disambiguation No extract_skills_structured Map extracted skills into 13 categories with proficiency estimation (beginner → expert) No extract_experience_structured Parse work history into structured timeline with start/end dates, achievements, and technologies No analyze_resume_comprehensive Master tool — full pipeline + entities + keywords + skills + experience in one call No Candidate Matching & Scoring Tool What it does AI? compute_similarity Cosine, Jaccard, TF-IDF overlap, and skill-match scores between resume and job description No assess_candidate Score against up to 8 weighted criteria axes → weighted total + pass / review / reject decision Optional manage_candidates Rank, filter, compare, and recommend pipeline stage changes across a candidate pool No Export & Notifications Tool What it does AI? export_results Export structured parse results to JSON or CSV No send_email Send results via SMTP (config passed per call — no server-side secrets stored) No ATS — Jobs Tool What it does AI? ats_manage_jobs Full CRUD for job postings: create, read, update, delete, list, search by title/department/status No ATS — Candidates & Pipeline Tool What it does AI? ats_manage_candidates CRUD + pipeline operations: add, update, move stage, bulk-move, filter by stage/score/tags No ats_pipeline_analytics Stage distribution, conversion rates, avg time-in-stage, bottleneck detection, drop-off analysis No ats_dashboard_stats One-call hiring health report: open roles, candidates by stage, interview load, offer acceptance rate No ats_search Global full-text search across all ATS entities (candidates, jobs, interviews, offers, notes) No ATS — Interviews Tool What it does AI? ats_schedule_interview Create, update, and delete interviews with conflict detection and interviewer availability check No ats_interview_feedback Submit structured feedback, compute consensus score, summarize feedback across all interviewers No ATS — Offers & Notes Tool What it does AI? ats_manage_offers Full offer lifecycle: draft → pending → approved → sent → accepted / declined / expired No ats_manage_notes Add, update, search, and delete timestamped candidate notes No Testing & Seeding Tool What it does AI? ats_generate_demo_data Generate a realistic sample ATS dataset (jobs, candidates, interviews, offers) for testing No assess_candidate optionally calls an LLM when you supply provider + apiKey; it falls back to fully algorithmic scoring otherwise.

2 months ago
Careerproof
@dontellu77

Career and workforce intelligence built on a deep HR ontology — skill taxonomies, role definitions and responsibilities, compensation and incentive structures, learning and development pathways, sourcing strategies, and role/skill evolution mapping. This structured foundation, combined with a RAG knowledge base curated from 50+ premium sources (HBR, McKinsey, BCG, Gartner, Forrester) and updated 3x daily with live web research, powers 6 guided skills and 42 MCP tools for two audiences: working professionals getting personalized career intelligence (CV optimization, salary benchmarking, career strategy), and HR/TA teams running structured talent evaluation, candidate shortlisting, compensation analysis, and consulting-grade workforce research reports. Example Use Cases (for HR/TA teams): 1. Custom Evaluation Models — Train CareerProof on your organization's existing assessment rubrics, scorecards, and evaluation criteria to build custom eval models that evaluate candidates through your specific lens. Upload your competency frameworks and historical assessments, then run inference on new candidates — scored and ranked exactly how your team would, at scale. 2. Candidate Evaluation & Shortlisting — Set up a hiring context with company profile and job description, upload candidate CVs, then batch-rank them with GEM competency scoring and JD-FIT matching. Apply your custom eval models for organization-specific scoring, or deep-dive any candidate with a 360-degree evaluation including tailored interview questions derived from skill taxonomy analysis. 3. Workforce Research Reports — Generate consulting-grade PDF reports across 16 types (salary benchmarking, skills gap analysis, org design, DEI assessment, succession planning, sourcing strategy, and more). Each report is grounded in real-time market data from premium sources and structured around HR ontology — role definitions, compensation structures, L&D pathways, and skill evolution mapping. 4. Compensation & Incentive Benchmarking — Get market-calibrated salary and total compensation intelligence for any role, location, and industry. Analysis is structured around compensation and incentive frameworks from the HR ontology, enriched with live web research and curated knowledge base data covering base salary, equity, bonuses, and benefits. Example Use Cases (for the working professional or career coach): 1. Career Intelligence Chat (Hyper-Personalized) — Ask career strategy questions and get hyper-personalized responses that fuse your CV context with deep insights from the career and workforce RAG knowledge base. Salary benchmarks calibrated to your function and location, industry disruption analysis mapped to your skill profile, and career pivot recommendations grounded in role evolution data — not surface-level answers, but intelligence drawn from the same sources that inform executive strategy. 2. CV Optimization (Hyper-Personalized) — Upload your CV and receive a hyper-personalized positioning pipeline that combines your actual experience with deep insights from our career and workforce RAG knowledge base. Market analysis calibrated to your industry and seniority, career opportunity identification grounded in role/skill evolution data, and targeted edits with trade-off analysis — not generic advice, but intelligence shaped by 50+ premium research sources and your unique career trajectory.

3 months ago
Resume MCP Server
@raeeceip

TypeScript
a year ago