# Uahh/Slscq

申论生成器(

Repository: https://github.com/Uahh/Slscq
Canonical: https://ross.abutalabs.com/products/slscq
Language: C++
License Family: other
Last push: 2024-05-11T11:01:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1823, "days_push": 844, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1136, forks 134 (observed 2026-08-28T04:03:43.789035+00:00)

## What it is
Slscq is a Chinese 'shenlun' (civil-service exam essay) generator written in C++. Given a topic and word count, it randomly assembles a plausible-sounding essay, intended purely for entertainment.

## Use cases
- generate a random chinese essay from a topic
- create funny shenlun-style articles
- generate filler text with a specified word count
- joke generator for chinese exam essays
- self-hosted essay generator web service

## When to choose
- you want a humorous, randomly assembled Chinese essay for fun
- you need a lightweight C++ CLI that runs offline with a data file
- you want to self-host the web version of the generator

## When to avoid
- you need genuinely useful or coherent writing for real exams or work
- you need a serious NLP text-generation model
- the output must reflect any real viewpoint or be used formally

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: cli, nlp
- domain: entertainment, developer-tools
- platform: windows, cli, cpp
- tags: chinese-text, essay-generator, humor, random-text, shenlun, text-generation, natural-language-processing, linux, macos, web-server

## Member repositories
- Uahh/Slscq (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:43.789035+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 2026-08-30T06:36:29.895174+00:00, confidence not recorded.
  - readme: https://github.com/Uahh/Slscq (fetched 2026-08-28T04:03:43.789035+00:00, sha 85e45b1a0ab8)
- Data as of 2026-08-30T08:39:29.467469+00:00.