nl-to-query

Name: nl-to-query
Availability: InStock
Author: sports-data-hq

Translate natural language hockey questions into structured query filters and PuckAPI operations.

1|Updated May 5, 2026

Search Tags:#sports analytics #natural language #data filtering #query translation #hockey #puckapi

Ready to run

npx skills add https://github.com/sports-data-hq/hockey-skills --skill nl-to-query-sports-data-hq

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nl-to-query
Source: https://github.com/sports-data-hq/hockey-skills/tree/main/skills/nl-to-query
Command: npx skills add https://github.com/sports-data-hq/hockey-skills --skill nl-to-query-sports-data-hq

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill converts plain-English hockey research questions into precise, executable data filters so you can analyze game, team, goalie, and player data without guessing field names or thresholds.

Core Features & Use Cases

Natural Language Translation: Maps fuzzy hockey language like outshot, back-to-back, shutout, or quality start into structured query conditions.
Endpoint Selection and Execution: Chooses the right PuckAPI command for games, teams, goalies, players, schedules, or game details.
Validation and Self-Correction: Checks for zero-result or implausible queries, retries with adjusted parameters, and flags sample-size caveats.
Use Case: Ask whether home teams that were outshot still won, and this Skill will translate the question, run the query, and summarize the matching results.

Quick Start

Ask the nl-to-query skill to translate your hockey research question into structured filters and return the matched results with a validation check.

Frequently Asked Questions about nl-to-query

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

FAQPage Schema

How do I translate natural language hockey questions into structured data queries?▼

Natural language hockey questions are translated into structured data queries by mapping fuzzy terms like outshot or shutout into executable query filters, selecting the right PuckAPI endpoint, and running the query to return validated results.

Can I filter hockey analytics data using plain English instead of writing manual query logic?▼

You can filter hockey analytics data using plain English by converting research questions into structured query conditions, applying them to game, team, goalie, or player endpoints, and summarizing the matched records.

What happens when a hockey data query returns zero results or implausible outputs?▼

When a hockey data query returns zero results or implausible outputs, the system self-corrects by retrying with adjusted parameters, validates ambiguous results, and flags sample-size caveats to ensure accurate reporting.

How do I query hockey game, team, goalie, and player data with multiple combined conditions?▼

Querying hockey game, team, goalie, and player data with multiple combined conditions involves translating natural language research questions into structured filters, selecting the appropriate PuckAPI command, and executing the data operation.

Does this natural language query translation work for PuckAPI hockey endpoints?▼

This natural language query translation works for PuckAPI hockey endpoints by selecting the correct command for games, teams, goalies, players, schedules, or game details, and mapping schema-aware fields to structured query conditions.

What are the limitations of using natural language to query hockey analytics data?▼

Limitations of using natural language to query hockey analytics data include the need for schema-aware mapping to avoid ambiguous results, dependency on endpoint availability, and the requirement to validate sample sizes to prevent implausible query outputs.