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nlp-search

Identify natural-language intents and extract entities for HR-IMS search and analytics workflows.

Updated Jan 8, 2026
Search Tags:#backend#nlp#inventory#semantic-search#intent-recognition#hr-ims#query-parsing
One-click install
npx skills add https://github.com/Arnutt-N/hr-ims --skill nlp-search
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nlp-search
Source: https://github.com/Arnutt-N/hr-ims/tree/main/.claude/skills/nlp-search
Command: npx skills add https://github.com/Arnutt-N/hr-ims --skill nlp-search

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Handling natural language queries for HR-IMS is error-prone and time-consuming. This skill translates everyday questions into structured intents and entities to drive precise searches and analytics across items, users, and requests.

Core Features & Use Cases

  • Intent recognition: Classifies user queries into actions like search items, search users, search requests, or stats.
  • Entity extraction & filtering: Detects statuses, dates, categories, and locations to refine results.
  • Unified results & versatility: Returns relevant results with context for quick follow-ups and actions.
  • Use Case: A supervisor asks for “pending requests from last week” and receives a filtered result set with user names and statuses.

Quick Start

Ask the system to find pending inventory items created this week.

Frequently Asked Questions about nlp-search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I parse natural language queries for intent recognition in an HR inventory management system?▼

Natural language query parsing for an HR-IMS is handled by an NLP pipeline that classifies intents and extracts entities to map inputs into precise Prisma-based queries. It identifies actions like searching items, users, and requests.

What's the best way to implement semantic search for backend HR requests and inventory items?▼

Implementing semantic search for backend HR requests involves recognizing user intents and extracting entities like dates and statuses. This skill maps natural language inputs to deterministic queries, returning filtered result sets for quick follow-ups.

Can I use NLP to extract entities like dates and statuses for filtering backend inventory searches?▼

You can use NLP entity extraction to detect statuses, dates, categories, and locations from natural language inputs. This refines inventory search results by mapping the extracted parameters directly to structured database queries.

How do I map ambiguous natural language inputs to database queries without returning incorrect HR records?▼

Mapping ambiguous natural language inputs to database queries requires conservative fallbacks within the NLP pipeline. The system deterministically maps recognized intents and entities to Prisma queries, ensuring precise results while handling edge cases safely.

Does this natural language processing pipeline support statistics generation for HR analytics workflows?▼

The natural language processing pipeline supports statistics generation by classifying user queries into a stats intent. It drives HR analytics workflows across frontend and backend services by extracting relevant entities to filter and aggregate request data.

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