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 nklofy/code-agent-skills --skill generating-python-installergit clone --depth 1 https://github.com/nklofy/code-agent-skillsWrote 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/nklofy/code-agent-skills/generating-python-installer)<a href="https://agentmods.dev/skills/nklofy/code-agent-skills/generating-python-installer"><img src="https://agentmods.dev/badge/skills/nklofy/code-agent-skills/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 | $0.08988 |
| Opus 5 | $0.00054 | $0.04494 |
| Sonnet 5 | $0.00022 | $0.01798 |
| Haiku 4.5 | $0.00011 | $0.00899 |
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.
This is a copy
88% identical to generating-python-installer — 28 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 821 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 · 821 lines · 108 tokens per session scan A b214c00051ee
generating-python-installer is a skill published in the GitHub repository nklofy/code-agent-skills (18 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 108 tokens to every session and 8,988 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to generating-python-installer, differing in 28 lines, and is treated as a copy.
Other skills, from other repositories
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with…
dd-code-generation
Use pup CLI for immediate Datadog operations or generate code for integration into applications.
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…
holoscan-install-wheel
Install Holoscan SDK Python wheel via pip into a venv. Use for Python installs; not for native C++/apt or Conda installs.
typing-exclusion-worker
Python typing exclusion worker: remove assigned mypy exclusion modules in small scoped batches, fix typing issues, run validation, and produce a structured completion summary. Use when running parallel typing-debt workers or when asked to remove modules from pyproject mypy exclusion overrides.