AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ufy2024/AuC --skill generating-python-installergit clone --depth 1 https://github.com/ufy2024/AuCWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ufy2024/auc/generating-python-installer)<a href="https://agentmods.dev/skills/ufy2024/auc/generating-python-installer"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/generating-python-installer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00108 | $0.09098 |
| Opus 5 | $0.00054 | $0.04549 |
| Sonnet 5 | $0.00022 | $0.01820 |
| Haiku 4.5 | $0.00011 | $0.00910 |
Grade A, and why
generating-python-installer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- generating-python-installer — 88% identical, 28 lines differ
- generating-python-installer — 83% identical, 28 lines differ
- generating-python-installer — 83% identical, 28 lines differ
How it starts
The opening of the file, as written. The whole thing — 845 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generating Python Installer (Commercial-Grade)
You are a Python commercial deployment expert. Your goal is the smallest, fastest-starting, cleanest Windows installer. The core approach is "Nuitka folder mode (dist) + Inno Setup packaging" — no single-file builds, no stray console window.
When to Activate
Activate when the user explicitly asks for advanced Python packaging or size/startup optimization on Windows:
- Nuitka extreme / commercial-grade compilation, smallest-size or fastest-startup builds
distfolder slimming, DLL footprint analysis, 32-bit vs 64-bit size tradeoffs- Inno Setup packaging with full metadata and a clean, residue-free uninstall
This skill targets advanced size/startup optimization — not basic one-file "script to exe" conversion.
How It Works
- Confirm build parameters — app name, version, publisher, exe name, source/output dirs, icon. Never auto-fill; ask the user.
- Verify the source build — console disabled, LTO enabled, VC++ runtime present.
- Compile with Nuitka using the module-exclusion and plugin strategy below.
- Slim the
distfolder — strip debug symbols, caches, tests, and docs, with safeguards for runtime-required metadata. - Analyze DLLs to find and trim the largest dependencies.
- Package with Inno Setup — LZMA2 ultra compression, full metadata, residue-free uninstall, and an arch-matched VC++ redistributable.
Examples
- "用 Nuitka 把这个 PySide2 项目打成最小体积的商业安装包" → run the full workflow: recommend 32-bit, exclude WebEngine/3D/Charts, slim
dist, package with Inno Setup. - "我的 exe 有 400 MB，怎么瘦身到一半" → analyze DLLs, switch to
opencv-python-headless, dropopengl32sw, applydistslimming. - "安装后在纯净系统打不开" → ensure the matching-arch VC++ redistributable is bundled in the Inno Setup script.
核心理念
坚持 "Nuitka 文件夹模式(dist) + Inno Setup 封装" 方案。拒绝单文件版，拒绝黑窗。
实战参考案例（生产级 PySide2 桌面应用，323 MB，含 OpenCV / Playwright）
项目概况
- 总体积: 323 MB
- 打包工具: PyInstaller 4.7 (32位)
- 主要依赖: PySide2 (22.52 MB), OpenCV (62.38 MB), Playwright (76.74 MB)
- Python 版本: Python 3.8 (32位)
- DLL 数量: 71 个，总计 93.23 MB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 845 lines · 108 tokens per session scan A e6756cf90022
generating-python-installer is a skill published in the GitHub repository ufy2024/AuC (1,091 stars, last pushed 1mo ago), licensed MIT. It adds 108 tokens to every session and 9,098 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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