官方说明开源项目3 天前
Aureliolo/steamgauge
Reads every Steam review of a game on your own computer: what players praise and criticise, how often, and…
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官方说明Reads every Steam review of a game on your own computer: what players praise and criticise, how often, and the reviews behind every figure. A desktop app, with a command line and an MCP server for AI assistants.
- ★ 数取值
- 1 ★ · Aureliolo/steamgauge · 取自 GitHub 搜索 API · （2026-10-16 20:00:20 核对）
- 来源明细
- 从该仓库的 GitHub 页面直接读取 · 条目类型：开源项目
- 收录与活跃
- 收录于 2026-10-14 · 最近见到 2026-10-16 · 共见到 3 次 · 被 1 个来源列出
- 评分构成
- 综合分 103 · 1 ★ · 1 个来源 · repo
常见问题（答案都来自我们的记录）
怎么安装？
我们【没有】从该仓库取到安装命令（我们的 install 覆盖率只有约 4%）。请看它的 README 或 releases 页。
它适合谁 / 用来做什么？
我们把它归在这些标签下: 开源项目。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
仓库
Aureliolo/steamgauge；我们收录于 2026-10-14；最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。第三方质量标记
SteamGauge
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
A tool for finding out what players of a game actually think, rather than what the loudest
reviews say.
Install
The installer and the portable archive for each system are in the
latest release. A package manager
installs it too:
Install has the rest: the installer and archive for each system, what each
system needs, the first launch, verifying a download and upgrading.
Documentation
The documentation is published at and lives in
docs/.
- Install: the files for each system, the first launch, checking a download and
updating.
- How it works: what it downloads, how it reads a review, and what each
figure counts.
- Accuracy: how often the reader is right, against what, in which languages, and
what the figures cannot tell you.
- AI assistants: the app's MCP server and the assistants it adds itself to.
- MCP tools: every prompt and tool the MCP server offers and what each is given.
- Command line: every command of the steamgauge program.
- Architecture: how the parts fit and the rules they keep, for anyone
changing SteamGauge.
- Privacy: what stays on your computer and the four places the app connects to.
Models and dataset
The readers, the search models and the labelled claims are on Hugging Face, each release
tagged v1, v2 and so on. The app fetches every file from one pinned commit and checks its
length and SHA-256 before using it.
| | Repository | DOI |
|---|---|---|
| Reader | Aureliolo/game-review-reader | 10.57967/hf/10732 |
| Reader, small | Aureliolo/game-review-reader-small | 10.57967/hf/10734 |
| Labelled claims | Aureliolo/game-review-claims | 10.57967/hf/10735 |
| Search encoder | Aureliolo/steamgauge-search-encoder | |
| Search reranker | Aureliolo/steamgauge-search-reranker | |
The search models are fp16 ONNX exports of Qwen3-Embedding-0.6B and Qwen3-Reranker-0.6B with
their weights unchanged. The dataset holds no review text and nothing about the authors: each
labelled review carries a SHA-256 fingerprint of its text, and training/fetch_text.py
reviews say.
Install
The installer and the portable archive for each system are in the
latest release. A package manager
installs it too:
Install has the rest: the installer and archive for each system, what each
system needs, the first launch, verifying a download and upgrading.
Documentation
The documentation is published at and lives in
docs/.
- Install: the files for each system, the first launch, checking a download and
updating.
- How it works: what it downloads, how it reads a review, and what each
figure counts.
- Accuracy: how often the reader is right, against what, in which languages, and
what the figures cannot tell you.
- AI assistants: the app's MCP server and the assistants it adds itself to.
- MCP tools: every prompt and tool the MCP server offers and what each is given.
- Command line: every command of the steamgauge program.
- Architecture: how the parts fit and the rules they keep, for anyone
changing SteamGauge.
- Privacy: what stays on your computer and the four places the app connects to.
Models and dataset
The readers, the search models and the labelled claims are on Hugging Face, each release
tagged v1, v2 and so on. The app fetches every file from one pinned commit and checks its
length and SHA-256 before using it.
| | Repository | DOI |
|---|---|---|
| Reader | Aureliolo/game-review-reader | 10.57967/hf/10732 |
| Reader, small | Aureliolo/game-review-reader-small | 10.57967/hf/10734 |
| Labelled claims | Aureliolo/game-review-claims | 10.57967/hf/10735 |
| Search encoder | Aureliolo/steamgauge-search-encoder | |
| Search reranker | Aureliolo/steamgauge-search-reranker | |
The search models are fp16 ONNX exports of Qwen3-Embedding-0.6B and Qwen3-Reranker-0.6B with
their weights unchanged. The dataset holds no review text and nothing about the authors: each
labelled review carries a SHA-256 fingerprint of its text, and training/fetch_text.py
- 来源
- github · https://github.com/Aureliolo/steamgauge
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