io.github.XJTLUmedia/ai-hr-management-toolkit

Run a comprehensive algorithmic analysis on a resume in a single call. Accepts raw text or a file (base64-encoded PDF/DOCX/TXT/MD or URL). Runs the full 5-node pipeline (Ingestion → Sanitization → Tokenization → Classification → Serialization) and returns: pipeline confidence scores, classified entities by type, categorized skills with proficiency estimates, structured experience timeline, career analysis, contact info, metrics/achievements, section quality assessment, and data quality scores. Optionally matches against a job description. This is a one-call alternative to chaining parse_resume + inspect_pipeline + classify_entities + extract_skills_structured + extract_experience_structured + compute_similarity.

Assess a resume against recruiter-defined criteria. Supports 8 criteria axes: Education, Experience, Skills, Certifications, Knowledge Stack, Competitions, Thresholds, and Job Qualification. Returns per-axis scores, weighted overall score, and a pass/review/reject decision.

Generate comprehensive ATS dashboard statistics and hiring health report. Provide the full ATS state (candidates, jobs, interviews, offers). Returns key metrics, pipeline health, hiring velocity, and actionable insights.

Generate a full set of realistic demo data for the ATS (Applicant Tracking System). Returns a complete ATSState with sample jobs, candidates at various pipeline stages, scheduled interviews, and offers. Useful for testing, demonstrations, or populating an empty ATS instance.

Manage interview feedback in the ATS. Actions: submit (add feedback to completed interview), get (retrieve feedback for an interview), update (modify existing feedback), list_pending (interviews awaiting feedback), list_completed (interviews with feedback, optionally filtered by candidate), analyze (aggregate feedback for a candidate across all interviews), summary (hiring signal summary for a job or all jobs).

Manage candidates in the ATS pipeline. Supports add, update, delete, move between stages, bulk move, and list/filter operations. Pass the current candidates record and an action to perform. Returns the updated candidates and a summary.

Manage job postings in the ATS. Actions: create (new job posting), update (edit fields), delete, list (with optional status/department filter), search (by keyword in title/description), close, reopen. Pass current jobs record and an action.

Manage candidate notes in the ATS. Actions: add (create note on candidate), update (edit note content), list (get all notes for a candidate), delete (remove a note), search (find notes by keyword across one or all candidates), bulk_add (add notes to multiple candidates). Pass the current candidates record.

Manage offers in the ATS. Actions: create (validate & structure), update_status (draft→pending-approval→approved→sent→accepted/declined), delete (remove offer), list (all offers with optional filters), compare (side-by-side offer comparison), validate (check for issues). Returns structured offer data.

Analyze the ATS hiring pipeline. Given candidates and optional pipeline stage config, returns stage distribution, conversion rates between stages, average time-in-stage, and bottleneck identification. Useful for hiring funnel analysis.

Full CRUD for interviews. Actions: create (validate & schedule with conflict detection), update (reschedule/modify), delete (permanent removal), list (filter by candidateId/jobId/status), get (single interview by id). Pass existing interviews record for conflict checks.

Global search across the ATS. Actions: search (keyword search across candidates, jobs, interviews, offers — scoped optionally), filter_candidates (structured filter by stage/job/tags/score), get_entity (retrieve a single entity by type+id). Pass the full ATS state.

Parse multiple resume files at once and run the full algorithmic pipeline on each. Returns raw text, pipeline analysis, keywords, entities, and confidence scores for each file. The LLM client should interpret and structure the results.

Run Named Entity Recognition on resume text. Extracts 12 entity types (PERSON, ORGANIZATION, DATE, SKILL, LOCATION, EMAIL, PHONE, URL, EDUCATION_DEGREE, CERTIFICATION, JOB_TITLE, METRIC) with per-entity confidence scores (0-1) and domain-aware disambiguation (e.g., Java the language vs Java the island). Returns classified entities grouped by type, confidence statistics, and ambiguity analysis. 100% algorithmic — no AI calls needed.

Compare a resume against a job description using cosine similarity, Jaccard index, TF-IDF overlap, and skill matching. Returns a computed fit tier (strong/moderate/weak/poor), per-skill gap analysis with categories, and actionable gap recommendations. No AI calls — all scoring is algorithmic.

Detect and structure date ranges, metrics, sections, and work experience from resume text. Returns structured experience entries with titles, organizations, technologies, and achievements extracted algorithmically using NER, date patterns, and TF-IDF. Also detects career progression trajectory. No AI calls.

Export parsed resume results to a specified format (JSON or CSV text). Accepts an array of structured resume results and returns formatted output.

Extract and structure work experience from resume text using algorithmic analysis only (no AI). Uses date range detection, metric extraction, NER entity classification (job titles, organizations, skills), and heuristic block splitting to produce structured experience entries. Each entry includes detected title, organization, date range, duration estimate, associated metrics/achievements, and technologies. Returns structured data plus overall career statistics.

Extract keywords from resume text using TF-IDF analysis, then overlay entity classification (NER) and skill categorization. Returns ranked keywords enriched with entity type, skill category, and confidence scores. No AI calls — all computation is algorithmic.

Extract and categorize skills from resume text using algorithmic analysis only (no AI). Combines NER entity classification (with disambiguation), TF-IDF keyword ranking, section detection, and frequency-based proficiency estimation. Returns skills organized by 13 categories (programming_language, framework, database, devops_cloud, ml_ai, design, methodology, tool, soft_skill, testing, security, web_frontend, mobile, other) with estimated proficiency levels and supporting evidence. Far more structured than extract_keywords — use this when you need categorized, proficiency-rated skill output.

Run the full 5-node atomic deconstruction pipeline (Ingestion → Sanitization → Tokenization → Classification → Serialization) on resume text. Returns stage-by-stage metrics, confidence scores, entity classification with disambiguation, data quality assessment, and assumption audit. Use this to understand HOW the parser processes a resume and WHERE confidence is low.

Manage and analyze candidates: rank by fit, filter by criteria, recommend pipeline stage changes, compare candidates side-by-side, or get a summary. Operates on candidate data passed in — does not access browser storage.

Parse a resume file (PDF, DOCX, TXT, MD) or URL and extract text with algorithmic pre-analysis including keyword extraction, metrics detection, section identification, and experience estimation.

Send parsed resume results via email using SMTP. Requires SMTP configuration (host, port, user, pass) and recipient email. Sends an HTML summary of all results.