# NUKnightLab/sql-mysteries

Inspired by @veltman's command-line mystery, use SQL to research clues and find out whodunit!

Repository: https://github.com/NUKnightLab/sql-mysteries
Canonical: https://ross.abutalabs.com/products/sql-mysteries
Language: JavaScript
License: MIT
License Family: permissive
Last push: 2025-04-14T15:06:31+00:00

## Health v2 (maintenance only)
Score: 39/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 16, release rhythm 35, longevity 100
- inputs: {"age_days": 3150, "days_push": 506, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2189, forks 313 (observed 2026-08-28T04:06:23.823050+00:00)

## What it is
SQL Murder Mystery is an interactive game and self-directed lesson where players use SQL queries against a SQLite database to solve a fictional murder. It can be played in the browser or downloaded as a database file to solve locally with any SQL environment.

## Use cases
- learn sql basics through a fun game
- practice writing sql queries
- teach sql to beginners in a classroom
- find a hands-on sql exercise for experienced users
- solve a detective puzzle using database queries

## When to choose
- you want an engaging, gamified way to learn or practice SQL
- you need a ready-made classroom exercise with beginner and experienced tracks
- you want to practice querying a realistic SQLite database

## When to avoid
- you need comprehensive SQL training beyond basic SELECT and JOIN queries
- you want production database tooling rather than a learning game

## Facets
- artifact type: learning-resource
- maturity: stable
- function: database
- domain: education, databases, developer-tools
- platform: cross-platform, python
- tags: sql, sqlite, game, tutorial, puzzle, interactive-learning, education, web

## Member repositories
- NUKnightLab/sql-mysteries (main) score 39

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:23.823050+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:47:43.295557+00:00, confidence not recorded.
  - readme: https://github.com/NUKnightLab/sql-mysteries (fetched 2026-08-28T04:06:23.823050+00:00, sha 46f113b2ba12)
- Data as of 2026-08-30T08:39:29.467469+00:00.